Do CASA Systems Satisfy Consumers Demands? A Critical Analysis

Discriminant Analysis and Data Mining CHAID Decision Tree as Tools to Evaluate the Buffering Effect of Hydroxytyrosol on Reactive Oxygen Species in Rooster Sperm Cryopreservation

Sperm cryopreservation is effective in safeguarding genetic biodiversity in avian species. However, during this process, spermatozoa are very susceptible to plasma membrane peroxidation in the presence of high concentrations of reactive oxygen species (ROS). To mitigate this effect, the addition of exogenous antioxidants, such as hydroxytyrosol (3,4-dihydroxyphenylethanol; HT), an antioxidant derived from olive oil, to the cryopreservation sperm diluent, could be useful. To verify this, a cryopreservation diluent was supplemented with different concentrations (0 μg/mL, 50 μg/mL, 100 μg/mL, and 150 μg/mL) of HT. For this, semen was collected in 10 replicates from 16 roosters of the Utrerana avian breed, and a pool was prepared with the optimum quality ejaculates in each replicate. After cryopreservation, spermatozoa were thawed and different in vitro semen quality parameters were evaluated. A discriminant canonical analysis (DCA) was carried out and revealed that total motility (TM; Lambda = 0.301, F = 26,173), hypo-osmotic swelling test (HOST; Lambda = 0.338, F = 22,065), and amplitude of lateral head displacement (ALH, Lambda = 0.442; F = 14,180) were the variables with the highest discriminant power. Finally, a chi-squared automatic interaction detection (CHAID) decision tree (DT) was performed excluding fresh semen samples and ROS was found to be the most valuable variable to discriminate between the different established freezing groups. Samples in the absence of HT or with low concentrations of this antioxidant showed less desirable ROS values in cryopreserved rooster semen. The present study could lead to the improvement of cryopreservation techniques for the genetic material of local poultry breeds and optimize the conservation programs of endangered native avian breeds.

Proteomic Analysis of Fresh and Liquid-Stored Boar Spermatozoa

In this study comparative proteomics was used to define changes in the expression of the spermatozoa proteins during liquid storage. Semen from eight boars was analyzed on the day of collection and after liquid preservation at 15-17 °C for three days. Sperm parameters (concentration, motility, morphology, vitality) and percentage of non-capacitated and acrosomal-reacted spermatozoa were determined. Sperm proteins were extracted and separated by two-dimensional sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) and proteomic profiles were computationally compared to highlight differentially expressed protein spots that were, in turn, identified by mass spectrometry. The intensities of four spots were significantly different between fresh and liquid stored sperm. Namely: ATP citrate lyase, chaperonin containing T-complex polypeptide 1 (TCP1) subunit ε and probable phospholipid-transporting ATP-ase were over-expressed in liquid stored sperm, whereas cytosolic non-specific dipeptidase was over-expressed in fresh sperm. These differentially expressed proteins could be used as plausible biomarkers for the evaluation of boar semen quality and spermatozoa survival after liquid storage and could help to address problems associated with sperm preservation.

Implications of boar sperm kinematics and rheotaxis for fertility after preservation

Artificial insemination (AI) is the single most important assisted reproductive technique devised to facilitate the genetic improvement of livestock. In the swine industry, it has broadly replaced natural service over the last number of decades which has been made possible by the high pregnancy rates and litter sizes obtainable with semen extended, up to, and sometimes beyond 5 d. Central to achieving good reproductive performance is the ability of boar studs to monitor semen quality, the basis of which has long been the assessment of sperm motility by subjective and, more recently, by more objective computerised systems. In this review, the literature on the relationship between sperm motility and kinematic parameters and field fertility is summarised. We discuss how this relationship is dependent on factors such as the viscosity of the media and the use of standard operating procedures. Emerging evidence is discussed regarding the importance of sperm rheotaxis and thigmotaxis as long-distance sperm guidance mechanisms, which enable motile functional spermatozoa to avoid the backflow of fluid, mucus and semen from the sow's uterus in the hours post AI, facilitating the establishment of sperm reservoirs in the oviducts. The literature on the use of microfluidics in studying sperm rheotaxis in vitro is also summarised, and we discuss how these systems, when combined with techniques such as lensless microscopy, have the potential to offer more physiological assessments of the swimming patterns of boar spermatozoa. Finally, possible future avenues of further investigation are proposed.

Automatic Tracking and Motility Analysis of Human Sperm in Time-Lapse Images

We present a fully automated multi-sperm tracking algorithm. It has the demonstrated capability to detect and track simultaneously hundreds of sperm cells in recorded videos while accurately measuring motility parameters over time and with minimal operator intervention. Algorithms of this kind may help in associating dynamic swimming parameters of human sperm cells with fertility and fertilization rates. Specifically, we offer an image processing method, based on radar tracking algorithms, that detects and tracks automatically the swimming paths of human sperm cells in timelapse microscopy image sequences of the kind that is analyzed by fertility clinics. Adapting the well-known joint probabilistic data association filter (JPDAF), we automatically tracked hundreds of human sperm simultaneously and measured their dynamic swimming parameters over time. Unlike existing CASA instruments, our algorithm has the capability to track sperm swimming in close proximity to each other and during apparent cell-to-cell collisions. Collecting continuously parameters for each sperm tracked without sample dilution (currently impossible using standard CASA systems) provides an opportunity to compare such data with standard fertility rates. The use of our algorithm thus has the potential to free the clinician from having to rely on elaborate motility measurements obtained manually by technicians, speed up semen processing, and provide medical practitioners and researchers with more useful data than are currently available.

Artificial insemination in pigs today

Use of artificial insemination (AI) for breeding pigs has been instrumental for facilitating global improvements in fertility, genetics, labor, and herd health. The establishment of AI centers for management of boars and production of semen has allowed for selection of boars for fertility and sperm production using in vitro and in vivo measures. Today, boars can be managed for production of 20 to 40 traditional AI doses containing 2.5 to 3.0 billion motile sperm in 75 to 100 mL of extender or 40 to 60 doses with 1.5 to 2.0 billion sperm in similar or reduced volumes for use in cervical or intrauterine AI. Regardless of the sperm dose, in liquid form, extenders are designed to sustain sperm fertility for 3 to 7 days. On farm, AI is the predominant form for commercial sow breeding and relies on manual detection of estrus with sows receiving two cervical or two intrauterine inseminations of the traditional or low sperm doses on each day detected in standing estrus. New approaches for increasing rates of genetic improvement through use of AI are aimed at methods to continue to lower the number of sperm in an AI dose and reducing the number of inseminations through use of a single, fixed-time AI after ovulation induction. Both approaches allow greater selection pressure for economically important swine traits in the sires and help extend the genetic advantages through AI on to more production farms.

Impact of swine reproductive technologies on pig and global food production

Reproductive technologies have dramatically changed the way pigs are raised for pork production in developed and developing countries. This has involved such areas as pigs produced/sow, more consistent pig flow to market, pig growth rate and feed efficiency, carcass yield and quality, labor efficiency, and pig health. Some reproductive technologies are in widespread use for commercial pork operations [Riesenbeck, Reprod Domest Anim 46:1-3, 2011] while others are in limited use in specific segments of the industry [Knox, Reprod Domest Anim 46:4-6, 2011]. Significant changes in the efficiency of pork production have occurred as a direct result of the use of reproductive technologies that were intended to improve the transfer of genes important for food production [Gerrits et al., Theriogenology 63:283-299, 2005]. While some technologies focused on the efficiency of gene transfer, others addressed fertility and labor issues. Among livestock species, pig reproductive efficiency appears to have achieved exceptionally high rates of performance (PigCHAMP 2011) [Benchmark 2011, Ames, IA, 12-16]. From the maternal side, this includes pigs born per litter, farrowing rate, as well as litters per sow per year. On the male side, boar fertility, sperm production, and sows served per sire have improved as well [Knox et al., Theriogenology, 70:1202-1208, 2008]. These shifts in the efficiency of swine fertility have resulted in the modern pig as one of the most efficient livestock species for global food production. These reproductive changes have predominantly occurred in developed countries, but data suggests transfer and adoption of these in developing countries as well (FAO STAT 2009; FAS 2006) [World pig meat production: food and agriculture organization of the United Nations, 2009; FAS, 2006) Worldwide Pork Production, 2006]. Technological advancements in swine reproduction have had profound effects on industry structure, production, efficiency, quality, and profitability. In all cases, the adoption of these technologies has aided in the creation of a sustainable supply of safe and affordable pork for consumers around the world [den Hartog, Adv Pork Prod 15:17-24, 2004].

Multivariate analyses for determining the association of field porcine fertility with sperm motion traits analysed by computer-assisted semen analysis and with sperm morphology

This study investigates the association of semen traits with boar fertility. The fertility outcome (farrowing rate - FR and total piglets born - TB) of 14 boars was obtained from a field trial conducted during 10 week of breeding period on a commercial farm using multiparous sows (n = 948) through single-sire mating with 2 × 10(9) motile sperm cells per artificial insemination (AI) dose. Sperm motion parameters, evaluated with computer-assisted semen analysis system in raw and stored semen at 17°C for 240 h, in addition to morphological sperm defects, measured on the collection day, were included in the analysis to determine which semen traits were important to discriminate the fertility potential of ejaculates from these boars. The data underwent multivariate cluster, canonical and discriminant analyses. Four clusters of boars were formed based on fertility outcome. One boar, with the lowest FR and TB values (89.7% and 11.98), and two boars, with the highest FR and TB values (97.8% and 14.16), were placed in different clusters. The other boars were separated in two distinct clusters (four and seven boars), including boars with intermediate TB (12.64 and 13.22) but divergent values for FR (95.9% vs 91.8%). Semen traits with higher discriminatory power included total motility, progressive motility, amplitude of lateral head displacement and cytoplasmatic droplets. Through multivariate discriminant analysis, more than 80% of the 140 ejaculates were correctly classified into their own group, showing that this analysis may be an efficient statistical tool to improve the discrimination of potential fertility of boars. Nevertheless, the validation of the relationship between fertility and semen traits using this statistical approach needs to be performed on a larger number of farms and with a greater number of boars.