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Seminal plasma samples from 13 bulls with various freezability and semen freezability data were provided by a commercial breeding company (Alta Genetics Inc., Watertown, WI, United States). The bulls were housed in the same nutrition and management environment to prevent sample variation. Semen was collected using artificial vagina and protease inhibitor was added immediately. Semen was then centrifuged at 800 × g for 15 min to separate the seminal plasma and sperm. This seminal plasma was transferred into sterile microcentrifuge tubes and centrifuged again at 800 × g for 15 min to completely eliminate sperm in the sample. Following this second centrifugation, seminal plasma was transferred into new tubes and shipped to Mississippi State University (MSU) in a liquid nitrogen tank.

Bull semen was extended with commercial egg-yolk–tris-based extender, and then frozen at Alta Genetics using standard protocols (Pace et al., 1981). Briefly, fresh semen was collected from bulls via artificial vagina, and semen was evaluated for concentration, volume, color, and motility. Then, semen was extended with one-step egg-yolk–tris–glycerol extender. The extender included 20% egg-yolk and 6% glycerol. This is called initial extension which includes fourfold dilution with extender at 32°C. The extended semen was cooled down to 5°C within 90 min. Then, the remaining extender was added at 5°C to complete extension, and packaged into quarter cc straws (250 μl) and let semen equilibrate for 3–4 h. Following the equilibration process, straws were frozen using automated freezer machine. The freezing was completed within 14 min (temperature from 5°C to −196°C), and stored in a liquid nitrogen tank.

Post-thaw sperm viability was assessed using flow cytometry (CyFlow SL, Partec, Germany). Fluorescent stain combinations of SYBR-14 with propidium iodide (SYBR-14/PI, Live/Dead Sperm Viability Kit L-7011, Thermo Fisher Scientific) were used as described previously (Garner et al., 1994; Nagy et al., 2004). Membrane integrity of 10,000 sperm cells from each collection was measured with the highest accuracy and objectivity. We verified that biological sample preparations, instrument configurations, and data analysis were compliant with the recommendations set by the International Society for Advancement of Cytometry on the minimum information necessary. The CyFlow SL (Partec, Münster, Germany) instrument equipped with 488 nm blue state laser allowed excitation of SYBR14 and PI to measure sperm viability. It was also fully equipped with five parameters: FSC, SSC, red, green, and orange/yellow colors. With the Partec FloMax software, the instrument allowed a real-time data acquisition, data display, and data evaluation.

The quality control measures and repeatability of flow cytometric sperm viability analysis were routinely verified by control samples which consisted of positive (100% dead sperm) and negative control (100% live sperm) of standard biological samples and their mixture of different ratios (100/100, 75/25/50/50, 25/75, and 100/100% dead and live sperm combinations). Another quality measure we used was the control of reagents (SYBR-14 and PI). The reagents and biological standards were used to calibrate the instrument settings and data processing. In the calibration, non-sperm particles were gated out and not included in the calculations. Partially stained (green and red) moribund sperm were considered as dead in the analysis. The percentage of live (green) sperm is used as a measure of sperm freezability parameter, the percentage of sperm that maintained membrane integrity during freeze–thawing process. The following formula was used to count the percentage of viable sperm: The% Viable sperm = The number of viable sperm/Total sperm (viable + dead + moribund) × 100.

Collectively, a unique freezability phenotype was generated to characterize variation among bulls for their lifetime post-thaw viability of sperm. For this particular research, we used post-thaw viability data generated over 8 years period (between 2008 and 2016). The database included 100,448 ejaculates from 860 Holstein bulls that were collected at least 20 different times in approximately 3 months period. The average and standard deviation of post-thaw viability for individual bulls were calculated, and then bulls were ranked based on average post-thaw semen viability. The threshold was the population average which consisted of the 100,448 ejaculates from 860 Holstein bulls. The average post-thaw viabilities of all bulls ranged from 33.03 to 67.3% (population average 54.7 ± 5.4%). The bulls were then classified as GF and PF based on average post-thaw viability score and the differences from the population average. The population average was our threshold to classify GF and PF groups. Bulls with greater sperm post-thaw viability than population average grouped as GF while those lower than average were considered as PF. Total of 13 bulls were selected with high confidence among 860 bulls for the presented study (Table 1).

The amino acid analyses of seminal plasma from 13 bulls with various freezability were performed using EZfaast Amino Acid kit (©Phenomenex Inc., Torrance, CA, United States) as previously described by Kaspar et al. (2008). All samples were prepared and analyzed according to protocol that provided by Phenomenex Inc., Torrance, CA, United States. Briefly, 10 μl of seminal plasma and 25 μl of internal standard solution (norvaline 0.02 nM and N-propanol 10%) were pipetted into glass sample preparation vials. Solution in the sample preparation vial was passed through the sorbent tip using a syringe. Then, 200 μl of N-propanol was pipetted into the same vial and passed through the sorbent tip and into syringe barrel. Drained liquid from sorbent tip was discarded. One hundred and twenty microliters of sodium hydroxide and 80 μl of N-propanol were pipetted into same glass vial, and the particles inside the sorbent tip were ejected into solution. A volume of 50 μl of chloroform–propyl chloroformate, and 100 μl of iso-octane were transferred to the tube and the resulted mixture was vortexed for 1 min after each adding. Transparent part of the (upper) organic layer transferred into autosampler vial, and evaporation of the solvent was achieved using a TurboVap^® LV evaporator (Biotage, Charlotte, NC, United States) with a gentle stream of nitrogen at 30°C. The extract was then suspended in 50 μl of solution containing iso-octane (80%) and chloroform (20%) and transferred to an amber glass vial having a fixed insert (Agilent Technologies, Santa Clara, CA, United States) for the analysis using GC–MS.

We used recommended GC-MS parameters to analyze the seminal plasma amino acids and the reference standards in an Agilent 7890A GC System that was coupled to an Agilent 5975C inert XL MSD with triple-axis mass detector, an Agilent 7693 Series Autosampler, and a capillary column (Zebron^TM EZ-AAA 10 m × 0.25 mm; ©Phenomenex, Santa Clara, CA, United States). The derivatized mixture (1.5 μl) was injected into the inlet that was heated at 250°C with 1:15 split ratio. Following the injection of the sample at 3 ml/min, standard septum purge was performed using helium carrier gas at 1 ml/min constant flow rate. Auxiliary, ion source, and quadrupole were heated at 310, 240, and 180°C, respectively. The oven was programmed initially at 110°C, and ramped up to 320°C within 11 min. The solvent delay time was at 1.30 min. The MS was operated in selected ion monitoring (SIM) mode and appropriate ion sets were selected. All amino acids were identified based on retention times, target and qualifier ions in comparison with authentic standards supplied by ©Phenomenex Inc., Torrance, CA, United States (Table 2). For calibration, increasing volumes of the diluted standards (0, 5, 10, 40, 80, 160, and 200 nmol/ml) were as described above. Abundances of the target ions of amino acids were divided by abundance of target ion of the internal standard (norvaline) and the unitless ratios were used to calculate amino acid concentrations using internal standard calibration.

The associations between freezability of sperm and concentration of seminal plasma amino acids were assessed using both univariate and multivariate approaches. For univariate approach, a generalized linear mixed model was used to determine the statistical significance between GF and PF groups. The variance was estimated by the GLIMMIX procedure of SAS 9.4 (SAS Institute Inc., Cary, NC, United States). The Kenward–Roger approximation was used to calculate the degree of freedom in case of heterogeneous variances. In addition, correlations among seminal plasma amino acids and correlations between seminal plasma amino acid concentrations and freezability scores were determined using Pearson’s correlation (Xia and Wishart, 2011). For multivariate analyses, MetaboAnalyst 3.0¹ (Xia et al., 2015) was used. For each variable, an observation was subtracted from the overall mean and the difference was divided by the standard deviation. This scaling or normalization of the data allowed us to bring the variances of all variables to the value of 1 while preserving the relative variability among observations within a variable. Following the normalization of data, partial least squares regression-discriminant analysis (PLS-DA) was performed and the bi-plot was constructed. The VIP scores in PLS-DA were calculated to identify significance of variables on phenotype. A VIP score >1.5 was considered as significant for group separation, and the significance level of 0.05 was used to determine statistical significance for other analyses. GraphPad Prism 8 was used (GraphPad Software, Inc., La Jolla, CA, United States) to generate some of the figures.

Pathways and network analyses of amino acids were performed using bioinformatic tools. The pathway-based compound-reaction-enzyme-gene networks were identified using MetScape 3.1 (Karnovsky et al., 2012) which was plug in Cytoscape 3.7.1². Interactomes of gene products defined by MetScape 3.1 were identified using the biological networks gene ontology tool (BiNGO) within Cystoscope 3.7.1. A merged network was created in Cystoscope by entering subjected genes to analyze the interactome of genes for Bos Taurus, and significance level was set as 0.05.

Twenty-one amino acids were detected in bull seminal plasma (Figure 1). Free amino acid concentrations of bull seminal plasma are depicted in Table 3. Among these, glutamic acid was the most abundant amino acid in seminal plasma with an average concentration of 3366 ± 547.3 (mean ± SD) nM, which accounts for approximately 53% of all the amino acids. The other most predominant amino acids were alanine, glycine, aspartic acid, and serine with mean concentrations of 1053 ± 187.9, 429.8 ± 57.94, 427 ± 101.3, and 278.2 ± 40.14 nM, respectively (Figure 2A). The least abundant were tyrosine, methionine, alpha-aminobutyric acid, allo-isoleucine, and asparagine with mean concentrations of 12.87 ± 1.91, 8.97 ± 1.76, 7.87 ± 2.53, 5.91 ± 2.13, and 2.92 ± 1.25 nM, respectively (Figure 2B).

There was no significant difference in amino acid concentrations between GF and PF groups (P > 0.05). However, phenylalanine concentration was significantly correlated with average post-thaw viability (r = 0.57, P-value = 0.044). Additionally, there were significant correlations among individual amino acids (Figure 3), such as the concentration of proline was positively correlated with leucine (r = 0.90, P-value < 0.0001), iso-leucine positively correlated with valine (r = 0.92, P-value < 0001), and the concentration of threonine was positively correlated with alanine (r = 0.95, P-value < 0001).

The multivariate analysis, PLS-DA, of seminal plasma amino acids showed a distinct separation between GF and PF bulls (Figure 4). PLS-DA was used for the classification. A variable importance in projection (VIP) score, which is widely used in PLS-DA, rank the amino acids considering their significance in discrimination between the GF and PF bulls. VIP score is referred as a weighted sum of squares of the PLS loadings. The X-axis specifies the VIP scores to each variable on the Y-axis. Therefore, amino acids with VIP score >1.5 was identified as phenylalanine, and VIP score in the corresponding heat map demonstrated that phenylalanine is more abundant in seminal plasma of the GF bulls than in that of PF bulls (Figure 5).

Pathway and network analyses of the amino acids with highest VIP scores (phenylalanine and threonine) and the most abundant amino acids (glutamic acid, alanine, and glycine) were performed using MetScape (3.1.3) (Karnovsky et al., 2012). By analyzing the results, we showed that phenylalanine was involved in tyrosine metabolism, and interacted with several compounds and genes (Figure 6A). The interactome of phenylalanine showed that this amino acid contributes to a number of cellular and biological processes, such as antioxidant detoxification, metabolic processes of reactive oxygen species, and oxidoreductase activity (Table 4). Threonine was involved in glycine, serine, alanine, and threonine metabolism and it shows significant gene ontology in terms of cellular amino acids and derivative metabolic processes (Figure 6B). Glutamic acid was correlated with many genes, enzymes, and other reactions (Figure 6C), most of which occur in mitochondria. It has a significant interactome regarding oxidoreductase activity, regulation of cell death, and the oxoacid metabolic process. It also contributes to histidine metabolism, the urea cycle, and the metabolism of arginine, proline, glutamate, aspartate, and asparagine, and Vitamin B9 (folate) metabolism. Alanine created a significant gene ontology in terms of ligase activity and forming carbon–oxygen bonds, and is also involved in pathways of glycine, serine, alanine, and threonine metabolism (Figure 6D). Finally, glycine is involved in seven different biological and cellular pathways, and has generated significant gene ontology in terms of oxidoreductase activity (acting on the CH-NH₂ group of donors), sarcosine oxidase activity, and D-amino-acid oxidase activity. All findings are summarized in Table 4.