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• W1934733935 abstract "Optimization of artificial insemination (AI) for pig production and evaluation of the fertilizing capacity of boar semen are highly related. Field studies have demonstrated significant variation in semen quality and fertility. The semen quality of boars is primarily affected by breed and season. AI centres routinely examine boar semen to predict male fertility. Overall, the evaluation of classical parameters, such as sperm morphology, sperm motility, sperm concentration and ejaculate volume, allows the identification of ejaculates corresponding to poor fertility but not high-efficiency prediction of field fertility. The development of new sperm tests for measuring certain sperm functions has attempted to solve this problem. Fluorescence staining can categorize live and dead spermatozoa in the ejaculate and identify spermatozoa with active mitochondria. Computer-assisted semen analysis (CASA) provides an objective assessment of multiple kinetic sperm parameters. However, sperm tests usually assess only single factors involved in the fertilization process. Thus, basing prediction of fertilizing capacity on a selective collection of sperm tests leads to greater accuracy than using single tests. In the present brief review, recent diagnostic laboratory methods that directly relate to AI performance as well as the development of a new boar fertility in vitro index are discussed. Assessing the fertilization capacity of boar ejaculate is important for optimizing artificial insemination (AI) in pig production (Foote 2003). High and stable semen performance by genetically high-quality paternal animals determines the efficacy of sperm production and critically influences the position of AI organizations on the market (Robinson and Buhr 2005). Classical methods of evaluating sperm quality, such as testing morphology, motility, and ejaculate concentration or volume, allow the examiner to make a statement (in principle) regarding boar qualification for AI (Table 1). However, few of these standard spermatology parameters are directly related to boar fertility (Berger et al. 1996). Fertilization is a complex process involving a large number of events. Interpretation of in vitro test results must be performed in the context of the relationship of these results to in vivo AI performance (Amann 1989). Several new technologies have been developed to assess sperm functionality and in vitro fertility in recent years. Fluorescence staining is currently used to make assertions regarding the proportion of live, mitochondrially active and/or membrane-intact sperm cells in ejaculates (Schulze et al. 2013b). Computer-assisted semen analysis (CASA) enables objective measurement of several kinetic parameters (Broekhuijse et al. 2012c). Until very recently, however, with their relationship to boar fertility performance established, a reliable array of in vitro spermatology parameters and their validation were not available. There are numerous reasons for subfertility or sterility in boars that are ultimately attributable to the quantity and quality of the semen (Bonet 1990). In this regard, semen quality deserves special attention in fertility performance due to the required sperm concentration standards for AI. The ejaculate of healthy boars always harbours a certain proportion of morphologically abnormal spermatozoa. However, disturbances in fertility capacity are to be expected only from a certain proportion of boars starting at the point of excess beyond the normative range. Accordingly, in 2005, the German Umbrella Association for pig production (ZDS – Zentralverband der Deutschen Schweineproduktion) appropriated regulations for including young boars with a maximum proportion of up to 25% of morphologically deviant spermatozoa in the ejaculate (Schulze et al. 2014). An investigation by Waberski et al. (1994) revealed significant negative correlations between the proportion of distal cytoplasmic droplets (DCD) in the extended semen and pregnancy rate (PR) and litter size (LS) when the sperm were stored for two days and PR when the sperm were stored for four days. Sperm with proximal cytoplasmic droplets (PCD) were negatively correlated with PR and LS only when the sperm were stored for four days. These authors concluded that the attached droplets (CD) negatively affected fertility. However, assertions regarding CD fractions vary considerably (Zeuner 1992). Thundathil et al. (2001), in their study on bulls, found evidence that sperm with PCD are unable to bind to the zona pellucida. Bull ejaculate with elevated PCD demonstrated a significantly lower fertility rate. The authors concluded that the zona pellucida functions as an important barrier against morphologically deviant sperm. Frequently, the incidence of PCD is related to immaturity, and the effect on LS is increased when the number of inseminated porcine sperm cells decreases (Table 1). In contrast, Quintero-Moreno et al. (2004) found that in boars with significant PCD proportion differences, there were no significant differences in PR or LS. Similarly, Galli and Bosisio (1988) found no correlations between sperm with PCD and LS or farrowing rate (FR). They found that the proportion of abnormal spermatozoa and acrosomal defects was positively correlated with FR. Fazeli et al. (1997) demonstrated that only sperm with intact acrosomes can initially bind to the zona pellucida of the oocyte in the pig. Within the framework of the oviduct plant assay, Petrunkina et al. (2001) studied sperm–oviduct binding as a prerequisite for the formation of the functional sperm reservoir in the female genitalia. The binding index reflected a significant negative correlation with both the proportion of abnormal spermatozoa and the proportion of spermatozoa with CD. A study by Lovercamp et al. (2007) regarding FR revealed that boars with a low fertilization performance had a significantly lower proportion of normal spermatozoa than boars with a high performance. The total proportion of spermatozoa with DCD and CD and the proportion of morphologically deviant spermatozoa were significantly higher in boars with a low FR. A conceivable rationale for the negative effects of CD on fertility may stem from their relationship to the DNA integrity of spermatozoa. Fischer et al. (2003) demonstrated a positive correlation between DNA damage and CD proportion in human spermatozoa with negative influences on fertility. A subsequent study by Lopez-Fernandez et al. (2008) on boar sperm confirmed this relationship to CD. Gil et al. (2009) concluded that the morphology of the sperm head and tail significantly influences sperm motility. Ideally, if there is a correlation between morphological abnormalities and assessed CASA parameters, the latter can potentially suffice for determining the relationship between morphology and fertility. In a recent study, McPherson et al. (2014) determined that LS was influenced by abnormal sperm head morphology and retained DCD. Regarding the relationship between sperm motility and fertility, studies to date have yielded different results. Flowers (1997), in a subjective estimate of motility, demonstrated no relationship between the proportion of motile sperm in ejaculates and in vitro as well as in vivo fertility parameters in the presence of motility >60%. When sperm motility was <60%, fertilization reductions were observed (Flowers 1997). Motility of <60% negatively affected all fertility parameters (sperm penetration rate in vitro, FR and LS). Estimates on sperm motility and FR by Tardif et al. (1999) demonstrated a significant positive correlation between AI and considerably low sperm cell numbers (0.3 × 109 motile cells in 70 ml of total volume). These authors concluded that sperm motility is one of the most important indicators of fertility capacity in vivo. When comparing three boars with known differences in FR, LS and monospermic oocyte penetration in vitro, Popwell and Flowers (2004) found no significant differences in total sperm motility. They concluded that fresh sperm motility can be a good predictor for one boar but is not necessarily a good predictor for another boar even though the parameter (sperm motility) may suggest otherwise. Xu et al. (1998) found no correlations between sperm motility and LS with 3 or 2 × 109 sperm cells per AI dose. Furthermore, a lower percentage of motile spermatozoa could not be compensated for simply by adjusting the number of sperm cells in an AI dose. In contrast to these findings, research by Ruiz-Sanchez et al. (2006) demonstrated significant correlations between the motility of extended boar semen after 7 and 10 days of storage and FR and LS in gilts. Their study supported a variance of 12–27% in PR, FR and LS. The proportion of spermatozoa with CD in the ejaculate and subjectively estimated motility after 7 and 10 days of storage were significant predictors of fertilization performance. Foxcroft et al. (2008) suggested that identification of sperm motility in extended boar semen after different storage durations represents a suitable approach for ascertaining boar fertility. Broekhuijse et al. (2012b) concluded that semen motility is considered to be an important parameter with which to validate the quality of an ejaculate. However, other factors (e.g. the genetic line of the boar) are more important. They demonstrated that boar- and semen-related parameters explained only 6% of the variation in fertility. Of this variation, only 4% was explained by semen motility. In the last decade, CASA systems have become commercially available for the objective and accurate assessment of sperm motility and sperm kinetics in various species (Amann and Waberski 2014). Some characteristics of sperm movement are important for fertilization, while those of value for predicting fertility remain unclear. Different subpopulations of spermatozoa coexist within any ejaculate with differential maturational statuses and ages. CASA permits the study of motion characteristics in these sperm subpopulations (Abaigar et al. 1999). Using multiple regression analyses, Holt et al. (1997) explained for the first time (and with different models) a variance of 12–14% in LS. In addition to the kinetic parameters, velocity average path (VAP), velocity straight line (VSL), velocity curved line (VCL), velocity quotient straightness (VSL/VAP) and beat cross-frequency (BCF) of spermatozoa after a 2-h incubation at 39°C were significant predictors of LS. These authors demonstrated that increasing the probe incubation duration increased the correlated motility parameters of spermatozoa with average LS. It is noteworthy that good sperm kinetics positively affects fertility. The previous finding was also observed by Juonala et al. (1998). For AI with liquid-preserved semen stored for 7 days at 20°C, they found that total motility significantly correlated with LS. There was no correlation with the non-return rate (NRR). Sutkeviciene et al. (2005) demonstrated that the NRR and CASA-based total sperm motility after 7 days of storage were significantly correlated. Therefore, motility assessments after varying storage durations may offer a practical approach for identifying boar fertility. A variance of 29.9% in LS was found by Gadea et al. (1998), and FR variances of 27.1% and 30.8% were found by their two further models. In this study, the significant in vitro parameters for predicting boar fertility were progressive motility, oocyte penetration rate, ATP content in native ejaculate, proportion of spermatozoa with a normal apical ridge and proportion of live spermatozoa, which was revealed by eosin–nigrosin staining. Hirai et al. (2001) found no difference in the proportion of motile sperm in the ejaculate from boars with an above-average (>86%) or a below-average (<86%) NRR. Boars with a high NRR showed a significantly higher proportion of spermatozoa with linear motion in their ejaculate. The average velocity (μm/s) of motile sperm was significantly lower for boars with good fertilization performance. Boars with a LS of more than ten piglets had a significantly higher proportion of motile spermatozoa than boars with a LS of less than ten piglets (Hirai et al. 2001). In contrast, Didion (2008) found no evidence of a relationship between CASA-assessed sperm motility and fertility outcomes. They attributed their results to a very high sperm concentration of 3–5 × 109 sperm cells per AI dose and to the number of inseminated sows for each boar (averaging twenty animals), which was too low. The use of many sperm cells compensated for semen quality characteristics, which indicates that the intrinsic differences in semen quality between individual ejaculates and/or individual boars were masked. Broekhuijse et al. (2012c) reported significant effects of progressive motility, VCL and BCF on FR. Furthermore, they showed that total motility, VAP, VSL and the amplitude of lateral head displacement (ALH) significantly affected LS. The most significant CASA parameter affecting LS did not affect FR. ALH and VSL were negatively correlated with LS. ALH was an important motility parameter, acquired during sperm capacitation, and was required to accomplish penetration of fertilization barriers, such as the zona pellucida. Of the total variation in fertility, only 5–6% could be explained by boar- and sperm-related parameters. Of this variation, 9–10% was explained by detailed sperm motility parameters. The study results of Schulze et al. (2013b) provided explanatory power for 11–28% of the variance by expanding the methodology used for measuring fertilization capacity. These investigators extended the duration of sperm storage and implemented a stress resistance test on Day 7. Biostatistical analyses resulted in four independent spermatozoa characteristics qualifying as relevant prognostic variables of AI success: percentage of spermatozoa with PCD, percentage of spermatozoa with active mitochondria (RHO), BCF of progressively motile spermatozoa in a thermoresistance test (TRT) and oscillation measure of the actual path (WOB) of progressively motile spermatozoa in a TRT. By examining the different factors that may influence fertility, this study revealed no significant effects of sperm concentration, ejaculate volume, sperm output or collection interval on LS and PR. General laboratory practice involves measuring sperm motility and morphology, although flow cytometry is being increasingly used for semen assessment. Analysis of sperm viability is one of the most important parameters in evaluating male fertility according to Pintado et al. (2000). The function and metabolism of sperm depend primarily on plasma membrane integrity (Harrison 1997). There are a variety of methods for testing this integrity, such as the eosin–nigrosin staining technique and the use of different fluorescence staining substances, such as propidium iodide (PI), carboxyfluorescein diacetate, SYBR-14 (Garner and Johnson 1995), Hoechst 3358 (Gadea 2005), and ethidium homodimer-1 or Yo-Pro-1 (Gillan et al. 2005). Sperm membrane integrity in boars was demonstrated by Christensen et al. (2004) to have a closer relationship to LS than to traditionally estimated sperm motility. Juonala et al. (1999) found that sperm viability in liquid-preserved boar semen stored for 7 days was significantly correlated with the NRR and LS. Gadea et al. (2004) asserted that information obtained from different vital staining techniques has no relationship to fertility, although they advocated for the establishment of a rationale for missing information on the functionality of the spermatozoa under investigation. According to Graham et al. (1990), cell competency not only depends on the integrity of plasma membranes but also on the integrity of acrosomal and mitochondrial membranes. The authors comment in a later study that measuring just one parameter does not sufficiently contribute to an efficient prediction of fertility and that only a combination of the various parameter analyses will facilitate the reliable prediction of fertility potential (Graham 2001). One of the first attempts at a combined ascertainment of viability and mitochondrial activity with flow cytometry analysis came from Evenson et al. (1982), which was performed on human spermatozoa. After combining the fluorescent dyes ethidium bromide and rhodamine 123 (R123), a relationship between sperm motility and colour intensity was visually reliably established. Schulze et al. (2013b) illustrated the relationship between intact energy metabolism of sperm and boar fertility. For the first time, double staining with R123/PI (RHO) in extended ejaculate was able to predict the fertilizing capacity of boar ejaculate (contrasting the findings by Broekhuijse et al. (2012a)). Schulze and colleagues considered selected spermatology parameters regarding season and age and combined these into the in vitro boar fertility index. They established a seasonal profile for the PCD, RHO, BCF and WOB parameters for young Pietrain boars. All four prediction parameters of boar fertility were influenced by the month of the ejaculate collection. A statistical model corrected for seasonal effects was used for the calculation of the boar fertility index. In addition to the dependence of sperm parameters on season, age and breed of the boar, numerous other factors affect sperm quality (Schulze et al. 2014). Conditions under which AI is conducted exert a decisive influence on ejaculate quality and quantity (Schulze et al. 2013a, 2015). Housing conditions and feeding of the AI boars, frequency and method of semen collection, and handling of the ejaculate after collection influence the quality and quantity of the ejaculate in the ‘AI centre’ (umbrella term). Broekhuijse et al. (2011) also raised the concern that non-negligible parameters of AI performance in the sows should be investigated. These authors cited factors to be considered in evaluating fertility data, such as breed, age, parity, health status, general housing conditions and overall AI management (including weaning-to-oestrous interval and the number of inseminations per oestrus). The framework of spermatology for AI in boars currently offers possibilities for expanding the methodology spectrum and, consequently, the attainment of a multitude of additional information on sperm quality. Until recently, a comprehensive evaluation of the many potential parameters related to sperm quality and fertility in boars was not possible. The introduction of a new fertility in vitro index now increases the options for selecting and ranking boars within AI centres and provides a new instrument for AI management. The authors' studies included in this review were supported by IFN Schönow e. V. (Germany) and the Association for Biotechnology Research (FBF, Germany). The authors declare no conflict of interests. All authors contributed to the planning, writing and revision of the manuscript." @default.
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• W1934733935 title "<i>In Vitro</i>Measures for Assessing Boar Semen Fertility" @default.
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• W1934733935 doi "https://doi.org/10.1111/rda.12533" @default.
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