Virus removal from semen with a pinched flow fractionation microfluidic chip.

Nowadays pigs are bred with artificial insemination to reduce costs and transportation. To prevent the spread of diseases, it is important to test semen samples for viruses. Screening techniques applied are enzyme-linked immunosorbent assays and/or polymerase chain reaction, which are labor-intensive and expensive methods. In contrast to the current used screening techniques, it is possible to remove viruses physically from semen. However, existing methods for virus removal techniques have a low yield of spermatozoa. Therefore, we have developed a microfluidic chip that performs size-based separation of viruses and spermatozoa in boar semen samples, thereby having the potential to reduce the risk of disease spreading in the context of artificial insemination in the veterinary industry. As the head of a spermatozoon is at least twenty times larger than a virus particle, the particle size can be used to achieve separation, resulting in a semen sample with lower viral load and of higher quality. To achieve the size separation, our microfluidic device is based on pinched-flow fractionation. A model virus, cowpea chlorotic mottle virus, was used and spiked to porcine semen samples. With the proposed microfluidic chip and the optimized flow parameters, at least 84 ± 4% of the model viruses were removed from the semen. The remaining virus contamination is caused by the model virus adhering to spermatozoa instead of the separation technique. The spermatozoa recovery was 86 ± 6%, which is an enormous improvement in yield compared to existing virus removal techniques.

Genetic parameters for semen quality and quantity traits in five pig lines.

We aimed to estimate genetic parameters for semen quality and quantity traits as well as for within-boar variation of these traits to evaluate their inclusion in breeding goals. Genetic parameters were estimated within line using a multiple-trait (4 × 4) repeatability animal model fitted for 5 pig lines, considering 4 semen traits: sperm motility (MOT), sperm progressive motility (PROMOT), log-transformed number of sperm cells per ejaculate (lnN), and total morphological abnormalities (ABN). The within-boar variation of these traits was analyzed based on a multiple-trait (2 × 2) approach for SD and average (AVG) and a single-trait analysis for CV. The average heritabilities across the 5 lines estimated by multiple-trait analysis were 0.18 ± 0.07 (MOT), 0.22 ± 0.08 (PROMOT), 0.16 ± 0.04 (lnN), and 0.20 ± 0.04 (ABN). The average genetic correlations were favorable between MOT and PROMOT (0.86 ± 0.10), between MOT and ABN (-0.66 ± 0.25), and between PROMOT and ABN (-0.65 ± 0.25). As determined by within-boar variation analysis, AVG exhibited the greatest heritabilities followed by SD and CV, respectively, for the traits MOT and ABN. For PROMOT, average SD heritability was lower than CV heritability, whereas for lnN, they were the same. The average genetic correlations between AVG and SD were favorable for MOT (-0.60 ± 0.13), PROMOT (-0.79 ± 0.14), and ABN (0.78 ± 0.17). The moderate heritabilities indicate the possibility of effective selection of boars based on semen traits. Average and SD are proposed as appropriate traits for selection regarding uniformity.

Relationship between ovulation rate and embryonic characteristics in gilts at 35 d of pregnancy.

This study investigated the relationship between ovulation rate (OR) and embryonic characteristics in gilts. Landrace ( = 86) and Yorkshire x Landrace ( = 212) gilts were inseminated with semen stored for 3 to 5 d (SS1, = 59), 6 to7 d (SS2, = 133), or 8 to 10 d (SS3, = 106), and slaughtered at 35 d of pregnancy. Ovulation rate was assessed by dissection of the corpora lutea on both ovaries. Embryos were classified as vital (VE) by visual appearance and individually weighed (VEg) and the SD of the weight calculated (SDVEg). Early embryonic mortality (EM) was estimated as the difference between OR and the number of vital plus nonvital embryos. Embryonic characteristics were analyzed with a model that included linear and quadratic terms of OR and fixed class effects of semen storage duration (SS) and genetic line (GL). Landrace gilts had a higher OR than Yorkshire x Landrace gilts (22.1 ± 0.4 vs. 20.3 ± 0.2, ≤ 0.05) and also a higher EM (6.1 ± 0.4 vs. 3.5 ± 0.3, ≤ 0.05). EM was also higher in gilts inseminated with semen stored for more than 8 d. Also, Yorkshire x Landrace gilts had a higher number of VE (16.9 ± 0.7) than the Landrace gilts (13.3 ± 0.8) when inseminations were done with semen stored for up to 5 d. Yorkshire x Landrace gilts had the highest VEg when inseminated with semen stored for 3 to 5 d (SS1: 4.9 ± 0.2 g, SS2: 4.1 ± 0.1 g, and SS3: 4.0 ± 0.2 g; ≤ 0.05). VE and VEg did not differ within Landrace gilts between different SS classes. A quadratic relationship of OR ( ≤ 0.05) was found with VE: a maximum of 16.8 VE was observed at 26 ovulations [(2.5 (± 0.6)*OR- 0.05 (± 0.01)*OR]. A quadratic relationship of OR ( ≤ 0.05) was also found for EM: a minimum of 3.33 EM was observed at 15 ovulations [(-1.1 (± 0.6)*OR -0.03 (± 0.01)*OR]. VEg was not related with OR, but SDVEg had a positive linear relationship with OR [0.01 (± 0.003)*OR, ≤ 0.05]. Results show that Yorkshire x Landrace gilts perform better than Landrace when inseminated with fresh semen, but not with semen stored for longer time. Also, the VE increases with an increase in OR up to 26, but at a lower level at higher OR, which is likely related with the increase in EM. The higher EM at higher OR might arise from a higher variation in follicular/oocyte quality leading to a higher variation in embryonic quality and development, increasing mortality before uterine implantation and the variation in embryonic weight already at 35 d of pregnancy.

Relationships between ovulation rate and embryonic and placental characteristics in multiparous sows at 35 days of pregnancy.

The objective of this study was to investigate relationships between ovulation rate (OR) and embryonic and placental development in sows. Topigs Norsvin® sows (n=91, parity 2 to 17) from three different genetic backgrounds were slaughtered at 35 days of pregnancy and the reproductive tract was collected. The corpora lutea (CL) were counted and the number of vital and non-vital embryos, embryonic spacing (distance between two embryos), implantation length, placental length, placental weight and embryonic weight were assessed. The difference between number of CL and total number of embryos was considered as early embryonic mortality. The number of non-vital embryos was considered as late mortality. Relationships between OR and all other variables were investigated using two models: the first considered parity as class effect (n=91) and the second used a subset of sows with parities 4 to 10 (n=47) to analyse the genetic background as class effect. OR was significantly affected by parity (P<0.0001), but was not affected by the genetic background of the sows. Parity and genetic background did not affect embryonic and placental characteristics at 35 days of pregnancy. OR (varying from 17 to 38 CL) was positively related with early embryonic mortality (ß=0.49±0.1 n/ovulations, P<0.0001), with late embryonic mortality or number of non-vital embryos (ß=0.24±0.1 n/ovulations, P=0.001) and with the number of vital embryos (ß=0.26±0.1 n/ovulations, P=0.01). However, dividing OR in four classes, showed that the number of vital embryos was lowest in OR class 1 (17 to 21 CL), but not different for the other OR classes, suggesting a plateau for number of vital embryos for OR above 22. There was a negative linear relationship between OR and vital embryonic spacing (ß=-0.45±0.1 cm/ovulation, P=0.001), implantation length (ß=-0.35±0.1 cm/ovulation, P=0.003), placental length (ß=-0.38±0.2 cm/ovulation, P=0.05) and empty space around embryonic-placental unit (ß=-0.4±0.2 cm/ovulation, P=0.02), indicating uterine crowding. Further analyses showed that effects of OR on embryonic and uterine parameters were related with the increase in late mortality and not early embryonic mortality. Therefore, we conclude that a high OR results in an moderate increase in the number of vital embryos at day 35 of pregnancy, but compromises development in the surviving embryonic/placental units, suggesting that the future growth and survival of the embryos might be further compromised.

Use of combinations of in vitro quality assessments to predict fertility of bovine semen.

Predicting in vivo fertility of bull ejaculates using in vitro-assessed semen quality criteria remains challenging for the breeding industry. New technologies such as computer-assisted semen analysis (CASA) and flow cytometry may provide accurate and objective methods to improve semen quality control. The aim of this study was to evaluate the relationship between semen quality parameters and field fertility of bull ejaculates. A total of 153 ejaculates from 19 Holstein bulls have been analyzed using CASA (postthawing semen motility and morphology) and several flow cytometric tests, including sperm DNA integrity, viability (estimated by membrane integrity), acrosomal integrity, mitochondria aerobic functionality and oxidation. Samples were analyzed both immediately after thawing and after 4 hours at 37 °C. A fertility value (FV), based on nonreturn rate at 56 days after insemination and adjusted for environment factors, was calculated for each ejaculate. Simple and multiple regressions have been used to correlate FV with CASA and flow cytometric parameters. Significant simple correlations have been observed between some parameters and FV (e.g., straight line velocity [µm/s], r(2) = -0.12; polarized mitochondria sperm (%), r(2) = 0.07), but the relation between simple parameter and FV was too week to predict the fertility. Partial least square procedure identified several mathematical models combining flow cytometer and CASA variables and had better correlations with FV (adjusted r(2) ranging between 0.24 and 0.40 [P < 0.0001], depending on the number of included variables). In conclusion, this study suggests that quality assessment of thawed bull sperm using CASA and flow cytometry may provide a reasonable prediction of bovine semen fertility. Additional work will be required to increase the prediction reliability and promote this technology in routine artificial insemination laboratory practice.

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 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.

Efficient Boar Semen Production and Genetic Contribution: The Impact of Low-Dose Artificial Insemination on Fertility.

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.

A genome-wide association study reveals a novel candidate gene for sperm motility in pigs.

Sperm motility is one of the most widely used parameters in order to evaluate boar semen quality. However, this trait can only be measured after puberty. Thus, the use of genomic information appears as an appealing alternative to evaluate and improve selection for boar fertility traits earlier in life. With this study we aimed to identify SNPs with significant association with sperm motility in two different commercial pig populations and to identify possible candidate genes within the identified QTL regions. We performed a single-SNP genome-wide association study using genotyped animals from a Landrace-based (L1) and a Large White-based (L2) pig populations. For L1, a total of 602 animals genotyped for 42,551 SNPs were used in the association analysis. For L2, a total of 525 animals genotyped for 40,890 SNPs were available. After the association analysis, a false discovery rate q-value ≤0.05 was used as the threshold for significant association. No SNPs were significantly associated with sperm motility in L1, while six SNPs on Sus scrofa chromosome 1 (position 117.26-119.56Mb) were significant in L2. The mitochondrial methionyl-tRNA formyltransferase (MTFMT) gene, which affects translation efficiency of proteins in sperm cells, was identified as a putative candidate gene. The significant markers identified in this study may be useful to enhance the genetic improvement of sperm motility by selection of boars at an earlier age under a marker assisted selection strategy.

The practical use of density of whole bull semen to determine the volume of an ejaculate at AI laboratories.

Determining the volume of an ejaculate is an important part of processing semen in bovine AI laboratory practice. A multi-AI laboratory study was performed to estimate the density of whole bull semen, and whether the use of this value as a standard is suitable for practical use when semen of different breeds is processed at different AI laboratories. The density of whole bull semen had been determined for 90 ejaculates at five AI laboratories (five breeds). The results showed no effect of bull (p=0.766), breed (p=0.279) and laboratory (p=0.183). All duplicate measurements within the same sample were within the level of agreement (5%). Using the mean value of 1.053g/ml as a golden standard for the density of whole bull semen is therefore suitable for use in routine bovine AI laboratory practice.

Relationship of flow cytometric sperm integrity assessments with boar fertility performance under optimized field conditions.

The number of intact and functional spermatozoa in semen can be assessed with flow cytometry and is believed to relate to male fertility. The aim of this study was to examine whether currently used sperm integrity assessments with flow cytometry correlate with field fertility data obtained for boar semen. For this purpose, 20 boars were followed for a 20-wk period (with a total average production of 33 ejaculates per boar) and the obtained fertility results (farrowing rate and number of piglets born) of commercial artificial insemination doses made from these ejaculates were recorded. Fertility results were corrected for farm, sow, boar, and semen-related parameters. From the same semen samples, sperm cell integrity was assessed with respect to DNA and to membrane integrity, acrosome intactness and responsiveness, and mitochondrial potential using established flow cytometric assays. This was done on freshly produced semen and on semen stored for up to 15 d. Remarkably, none of the individual membrane integrity variables was significantly related to fertility results. In contrast, the amount of DNA damage as assessed at 7 to 10 d and at 14 to 15 d of semen storage related to farrowing rate (P = 0.0400) and total number of piglets born (P = 0.0310), respectively. Therefore, the degree of DNA damage in stored boar semen samples may be a useful factor to evaluate semen as an indicator for litter size and farrowing rate.

Artificial insemination in pigs: predicting male fertility.

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. Variation in fertility is caused not only by farm- or sow-related parameters but also by boar- and semen-related parameters. In pig AI there is no gold standard concerning semen quality assessment. Assessing semen quality characteristics objectively and relating them to large field fertility datasets leads to an efficient production of insemination doses, which results in an efficient dissemination/descent of the breeding program required genes. Overall, this contributes to the development of semen quality assessments, which improves the prediction of porcine male fertility. Knowing which semen characteristics, and to what extent, contribute to male fertility and makes the field fertility more predictable.

The value of microscopic semen motility assessment at collection for a commercial artificial insemination center, a retrospective study on factors explaining variation in pig fertility.

This study was conducted to evaluate the relationship between boar and semen related parameters and the variation in field fertility results. In 8 years time semen insemination doses from 110 186 ejaculates of 7429 boars were merged to fertility parameters of inseminations of 165 000 sows and these records were used for analysis. From all ejaculates boar and semen related data were recorded at the artificial insemination (AI) centers. Fertility parameters, such as farrowing rate (FR), ranging between 80.0% and 84.0%, and the total number of piglets born (TNB), ranging between 12.7 and 13.1, were recorded and from these the least square means per ejaculate were calculated. Only 5.9% of the total variation in FR was due to boar and semen variability of which 21% (P = 0.0001) was explained by genetic line of the boar, 11% (P = 0.047) was explained by laboratory technician, and 7% (P = 0.037) was explained by the AI center. For TNB the total variation was 6.6% boar and semen related of which 28% (P < 0.0001) was explained by genetic line of the boar and 7% (P = 0.011) was explained by the AI center. Only 4% of the boar and semen related variation was caused by sperm motility (microscopically assessed at collection, ranging from 60% to 90%). Other variation in FR and TNB was explained by management and semen related parameters (age of boar, 3%; P = 0.009; and 8%; P = 0.031, respectively), days between ejaculations (1%; P < 0.0001 of FR), number of cells in ejaculate (1%; P = 0.042 of TNB), year (9%; P = 0.032), and 13%; P = 0.0001, respectively), and month (11%; P = 0.0001; and 5%; P = 0.0001, respectively). Although semen motility is considered an important parameter to validate the quality of the ejaculate processed, it only minimally relates to fertility results under the current Dutch AI practice. Other boar and semen related parameters, like genetic line of the boar, are more relevant factors to select boars for AI purposes.

Application of computer-assisted semen analysis to explain variations in pig fertility.

Sperm quality is often evaluated through computer-assisted semen analysis (CASA) and is an indicator of boar fertility. The aim of this research was to study the relationship between CASA motility parameters and fertility results in pigs. Insemination records and semen parameters from a total of 45,532 ejaculates collected over a 3-yr period were used. The statistical model for analysis of fertility data from these inseminations included factors related to sow productivity. The boar- and semen-related variance (direct boar effect) were corrected for the effects of individual boar, genetic line of the boar, age of the boar, days between ejaculations, number of sperm cells in an ejaculate, number of sperm cells in an insemination dose, and AI station. The remaining variance was analyzed if semen motility parameters had a significant effect. This analysis revealed significant (P < 0.05) effects of progressive motility, velocity curvilinear, and beat cross frequency on farrowing rate (FR). Total motility, velocity average path, velocity straight line, and amplitude of lateral head displacement affected (P < 0.05) total number of piglets born (TNB). Boar- and semen-related parameters explained 5.3% of the variation in FR and 5.9% of the variation in TNB. Motility parameters, measured by CASA, explained 9% of the boar- and semen-related variation in FR and 10% of the boar- and semen-related variation in TNB. Individual boar and genetic line of the boar affected (P < 0.0001) the variation in FR and TNB. No differences (P > 0.05) were observed between effects of AI stations on fertility outcome, underscoring the objectivity of the CASA system used. Motility parameters can be measured with CASA to assess sperm motility in an objective manner. On the basis of the motility pattern, CASA enables one to discriminate between the fertilizing capacity of ejaculates, although this depends on the genetic line of the boar used in AI stations.

Do CASA systems satisfy consumers demands? A critical analysis.

Boar studs are often offered new technologies including several CASA (computer-assisted semen analysis) systems. However, independent information to assist their purchase decisions is not available. The systems accuracy and repeatability variation because of different factors can be evaluated through duplicate testing of semen samples and comparison of the results according to WHO standards for humans. This primary analysis and a thorough economic cost benefit evaluation will help to decide whether the purchase of a CASA system will be profitable for a boar stud. Our experience of implementing several CASA systems in the cooperative Dutch Artificial Insemination (AI) centres is used as a base for this discussion.

Field data analysis of boar semen quality.

This contribution provides an overview of approaches to correlate sow fertility data with boar semen quality characteristics. Large data sets of fertility data and ejaculate data are more suitable to analyse effects of semen quality characteristics on field fertility. Variation in fertility in sows is large. The effect of semen factors is relatively small and therefore impossible to find in smaller data sets. Large data sets allow for statistical corrections on both sow- and boar-related parameters. Remaining sow fertility variation can then be assigned to semen quality parameters, which is of huge interest to AI (artificial insemination) companies. Previous studies of Varkens KI Nederland to find the contribution to field fertility of (i) the number of sperm cells in an insemination dose, (ii) the sperm motility and morphological defects and (iii) the age of semen at the moment of insemination are discussed in context of the possibility to apply such knowledge to select boars on the basis of their sperm parameters for AI purposes.

Additional value of computer assisted semen analysis (CASA) compared to conventional motility assessments in pig artificial insemination.

In order to obtain a more standardised semen motility evaluation, Varkens KI Nederland has introduced a computer assisted semen analysis (CASA) system in all their pig AI laboratories. The repeatability of CASA was enhanced by standardising for: 1) an optimal sample temperature (39 °C); 2) an optimal dilution factor; 3) optimal mixing of semen and dilution buffer by using mechanical mixing; 4) the slide chamber depth, and together with the previous points; 5) the optimal training of technicians working with the CASA system; and 6) the use of a standard operating procedure (SOP). Once laboratory technicians were trained in using this SOP, they achieved a coefficient of variation of < 5% which was superior to the variation found when the SOP was not strictly used. Microscopic semen motility assessments by eye were subjective and not comparable to the data obtained by standardised CASA. CASA results are preferable as accurate continuous motility dates are generated rather than discrimination motility percentage increments of 10% motility as with motility estimation by laboratory technicians. The higher variability of sperm motility found with CASA and the continuous motility values allow better analysis of the relationship between semen motility characteristics and fertilising capacity. The benefits of standardised CASA for AI is discussed both with respect to estimate the correct dilution factor of the ejaculate for the production of artificial insemination (AI) doses (critical for reducing the number of sperm per AI doses) and thus to get more reliable fertility data from these AI doses in return.