Prediction of the fertility of stallion frozen-thawed semen using a combination of computer-assisted motility analysis, microscopical observation and flow cytometry.

Spermatozoa from some stallions do not maintain an acceptable fertility after freezing and thawing. The selection of frozen ejaculates that would be suitable for insemination is mainly based on post-thaw motility, but the prediction of fertility remains limited. A recent study in our laboratory has enabled the determination of a new protocol for the evaluation of fresh stallion semen, combining microscopical observation, computer-assisted motility analysis and flow cytometry, and providing a high level of fertility prediction. The purpose of the present experiment was to perform similar investigations on frozen semen. A panel of tests evaluating a large number of compartments or functions of the spermatozoa was applied to a population of 42 stallions, 33 of which showing widely differing fertilities (17-67% pregnancy rate per cycle [PRC]). Variability was evaluated by calculating the coefficient of variation (CV=SD/mean) and the intra-class correlation or "repeatability" for each variable. For paired variables, mean within-stallion CV% was significantly lower than between-stallion CV%, which was significantly lower than total CV%. Within-ejaculate repeatability, determined by analysing 6 straws for each of 10 ejaculates, ranged from 0.60 to 0.97. Within-stallion repeatability, determined by analysing at least 5 ejaculates for each of 38 stallions, ranged from 0.12 to 0.95. Principal component regression using a combination of 25 variables, including motility, morphology, viability, oxidation level, acrosome integrity, DNA integrity and hypoosmotic resistance, accounted for 94.5% of the variability regarding fertility, and was used to calculate a prediction of the PRC with a mean standard deviation of 2.2. The difference between the observed PRC and the calculated value ranged from -3.4 to 4.2. The 90% confidence interval (90CI) for the prediction of the PRC for the stallions of unknown fertility ranged from 8 to 30 (mean = 17). The best-fit model using only motility variables, evaluated after 10 min at 36 °C and 2 h at 36 °C or room temperature, accounted for only 74.2% of the variability. The difference between the observed PRC and the calculated value ranged from -7.2 to 14. The 90CI for the prediction of the PRC for the stallions of unknown fertility ranged from 23 to 48 (mean = 33). In conclusion, this study demonstrated that an appropriate combination of computer-assisted motility analysis, microscopical observation and flow cytometry can provide a higher prediction of fertility than motility analysis alone.

Development of a new fertility prediction model for stallion semen, including flow cytometry.

Several laboratories routinely use flow cytometry to evaluate stallion semen quality. However, objective and practical tools for the on-field interpretation of data concerning fertilizing potential are scarce. A panel of nine tests, evaluating a large number of compartments or functions of the spermatozoa: motility, morphology, viability, mitochondrial activity, oxidation level, acrosome integrity, DNA integrity, "organization" of the plasma membrane, and hypoosmotic resistance, was applied to a population of 43 stallions, 33 of which showing widely differing fertilities (19%-84% pregnancy rate per cycle [PRC]). Analyses were performed either within 2 hours after semen collection or after 24-hour storage at 4 °C in INRA96 extender, on three to six ejaculates for each stallion. The aim was to provide data on the distribution of values among said population, showing within-stallion and between-stallion variability, and to determine whether appropriate combinations of tests could evaluate the fertilizing potential of each stallion. Within-stallion repeatability, defined as intrastallion correlation (r = between-stallion variance/total variance) ranged between 0.29 and 0.84 for "conventional" variables (viability, morphology, and motility), and between 0.15 and 0.81 for "cytometric" variables. Those data suggested that analyzing six ejaculates would be adequate to characterize a stallion. For most variables, except those related to DNA integrity and some motility variables, results differed significantly between immediately performed analyses and analyses performed after 24 hours at 4 °C. Two "best-fit" combinations of variables were determined. Factorial discriminant analysis using a first combination of seven variables, including the polarization of mitochondria, acrosome integrity, DNA integrity, and hypoosmotic resistance, permitted exact determination of the fertility group for each stallion: fertile, that is, PRC higher than 55%; intermediate, that is, 45% < PRC less than 55%; or subfertile, that is, PRC less than 45%. Linear regression using another combination of 20 variables, including motility, viability, oxidation level, acrosome integrity, DNA integrity, and hypoosmotic resistance, accounted for 94.2% of the variability regarding fertility and was used to calculate a prediction of the PRC with a mean standard deviation of 3.1. The difference between the observed fertility and the calculated value ranged from -4.2 to 5.0. In conclusion, this study enabled to determine a new protocol for the evaluation of stallion semen, combining microscopical observation, computer-assisted motility analysis and flow cytometry, and providing a high level of fertility prediction.

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.

Is photometry an accurate and reliable method to assess boar semen concentration?

Sperm concentration assessment is a key point to insure appropriate sperm number per dose in species subjected to artificial insemination (AI). The aim of the present study was to evaluate the accuracy and reliability of two commercially available photometers, AccuCell™ and AccuRead™ pre-calibrated for boar semen in comparison to UltiMate™ boar version 12.3D, NucleoCounter SP100 and Thoma hemacytometer. For each type of instrument, concentration was measured on 34 boar semen samples in quadruplicate and agreement between measurements and instruments were evaluated. Accuracy for both photometers was illustrated by mean of percentage differences to the general mean. It was -0.6% and 0.5% for Accucell™ and Accuread™ respectively, no significant differences were found between instrument and mean of measurement among all equipment. Repeatability for both photometers was 1.8% and 3.2% for AccuCell™ and AccuRead™ respectively. Low differences were observed between instruments (confidence interval 3%) except when hemacytometer was used as a reference. Even though hemacytometer is considered worldwide as the gold standard, it is the more variable instrument (confidence interval 7.1%). The conclusion is that routine photometry measures of raw semen concentration are reliable, accurate and precise using AccuRead™ or AccuCell™. There are multiple steps in semen processing that can induce sperm loss and therefore increase differences between theoretical and real sperm numbers in doses. Potential biases that depend on the workflow but not on the initial photometric measure of semen concentration are discussed.

Expansion of HBV-specific memory CTL primed by dual HIV/HBV genetic immunization during SHIV primary infection in rhesus macaques.

We have previously shown the induction of humoral and cytotoxic responses specific for human immunodeficiency virus (HIV) and hepatitis B virus (HBV) antigens, following genetic immunization of rhesus macaques with a plasmid encoding both the third variable domain of the HIV-1 external envelope glycoprotein and the pseudo-viral particle of hepatitis B surface antigen (HBsAg) as presenting molecules. The DNA-immunized primates and two control animals were then challenged with a chimeric simian/human immunodeficiency virus (SHIV). They were all infected. Significant frequencies of SHIV specific cytotoxic T lymphocyte precursors (CTLp) were detected early in peripheral blood. But, in all DNA-immunized macaques, HBV envelope specific CTLp were detected during the primary infection, and they were correlated with the peak of SHIV viremia. Furthermore, HBV or SHIV specific cytotoxicity corresponded in part to CD8(+) T cells presenting a memory phenotype. Several mechanisms could account for this cellular response. But our results suggest that an expansion of memory cytotoxic CD8(+) cells, not restricted to SHIV specific effectors, could occur in peripheral blood during SHIV primary infection.

Successful interference with cellular immune responses to immunogenic proteins encoded by recombinant viral vectors.

Vectors derived from the adeno-associated virus (AAV) have been successfully used for the long-term expression of therapeutic genes in animal models and patients. One of the major advantages of these vectors is the absence of deleterious immune responses following gene transfer. However, AAV vectors, when used in vaccination studies, can result in efficient humoral and cellular responses against the transgene product. It is therefore important to understand the factors which influence the establishment of these immune responses in order to design safe and efficient procedures for AAV-based gene therapies. We have compared T-cell activation against a strongly immunogenic protein, the influenza virus hemagglutinin (HA), which is synthesized in skeletal muscle following gene transfer with an adenovirus (Ad) or an AAV vector. In both cases, cellular immune responses resulted in the elimination of transduced muscle fibers within 4 weeks. However, the kinetics of CD4(+) T-cell activation were markedly delayed when AAV vectors were used. Upon recombinant Ad (rAd) gene transfer, T cells were activated both by direct transduction of dendritic cells and by cross-presentation of the transgene product, while upon rAAV gene transfer T cells were only activated by the latter mechanism. These results suggested that activation of the immune system by the transgene product following rAAV-mediated gene transfer might be easier to control than that following rAd-mediated gene transfer. Therefore, we tested protocols aimed at interfering with either antigen presentation by blocking the CD40/CD40L pathway or with the T-cell response by inducing transgene-specific tolerance. Long-term expression of the AAV-HA was achieved in both cases, whereas immune responses against Ad-HA could not be prevented. These data clearly underline the importance of understanding the mechanisms by which vector-encoded proteins are recognized by the immune system in order to specifically interfere with them and to achieve safe and stable gene transfer in clinical trials.