Gene LPL, STAT5 and AGPAT6 Polymorphisms Relationship with Goat Milk Traits

Goat breeding has become an important branch of livestock production spreading all over the world (Darcan et al., 2018; Gautam et al., 2019). Selection of the most productive individuals allows achieving the best performance from dairy goats. Goat breeds commonly selected for milk production receive more research considerations regarding milk yield and quality. Genetic differences are seen as breed differences (Goetsch et al., 2011). Some studies have found that the level of milk production depends on the breed (Zazharska et al., 2018) and there is variation in milk yield among different breeds and within the goat breed (Kendalla et al., 2006; Sandrucci et al., 2018).

Molecular genetics leads to the revealing candidate genes with substantial effects on the traits of economic importance. Several single nucleotide polymorphisms (SNPs) had been detected in goats (Maitra et al., 2016) and some of them were associated with milk production (Badaoui et al., 2007; Merkel et al., 2002).

AGPAT6 plays a fundamental role in the milk fat synthesis. Triglyceride synthesis occurs through the stepwise addition of fatty acyl groups to glycerol-3-phosphate, with DGAT catalysing the last step in this chain and 1-acylglycerol-3- phosphate acyltransferases (AGPAT) an intermediary step (Coleman 2004). Sequence variation nearby AGPAT6 primarily controls fat content in early lactation which is also consistent with the expression of AGPAT6 at different stages of lactation (Bionaz et al., 2008).

STAT5A is one of the members of STAT (signal transducer and activator of transcription) family (Zhang et al., 2012), which is very important in cell activity, immune system, cancer regulation mechanism (Vafaizadeh et al., 2010; Peck et al., 2011) and is discovered as a MGF (mammary gland factor) which regulates the milk protein synthesis (Xie et al., 2015).

Lipoprotein lipase (LPL) is a glycoprotein that plays a central role in plasma triglyceride metabolism by hydrolysing triglyceride-rich chylomicrons (Badaoui et al., 2007) and regulating energy balance, fat deposition and growth traits (Ling et al., 2015). Milk LPL activity in goats is influenced by the stage of lactation, milking frequency, lipid supplementation (Chilliard et al., 2003) and has been shown to differ among several goat breeds (Badaoui et al., 2007).

Understanding of genetic information, particularly for those loci which affect the performance traits, is an important tool in the breeding programmes (Selvaggi et al., 2009). Therefore, the aim of this study was to investigate the association between LPL, STAT5 and AGPAT6 gene polymorphisms and milk traits of goats.

Animals and milk sample collection
 
The research (period during 2018-2019 year) was carried out in the herd of goats (n=204) and in Lithuanian University of Health Sciences, Department of Animal Breeding. All investigated goats were raised at the same feeding and housing conditions. The goats were milked twice a day and milk yields were recorded using monthly control milking data during 2018-2019 year. The milk samples were analysed for milk fat, milk protein, lactose and urea concentrations. The analysis was carried out together with Pieno Tyrimai SE (Lithuania), using spectrophotometers LactoScope 550 and LactoScope FTIR (Delta Instruments, the Netherlands). SCC in milk was determined by flow cytometry method using the Somascope CA-3A4 (Delta Instruments, the Netherlands).
 
DNA Extraction and SNP Genotyping
 
Analyses were done in Lithuanian University of Health Sciences, Institute of Biology Systems and Genetic Research, Dr. K. Janušauskas Laboratory of Genetic.

Genomic DNA was extracted from the hair roots using DTT (1M), Chelex 100, Proteinase K (20mg/ml) chemicals (Thermo Fisher Scientific, Waltham, MA, USA). The samples with lysis mixture were incubated for 45 min at 56°C. After incubation, the samples were heated at 94°C for 10 min.

The method of polymerase chain reaction and restriction length polymorphism were used to genotype Lipoprotein Lipase (LPL) AGPAT 6(1-acylglycerol-3- phosphate acyltransferase) and STAT5 (signal transducer and activator of transcription) gene polymorphisms., Using Primer3 and CLC Sequence Viewer 7 programs, the oligonucleotide primers and restriction enzyme were designed according to the goat LPL, STAT5 and AGPAT6 gene sequences. The PCR-RFLP reactions were carried out using LPL F: 5‘-AGACCGCTGCTCCAGCCT-3’, LPL R: 5‘-CAGCCCTCCGT GGGAGAC-3‘ oligonucleotide primers (10pmol) and SchI restriction enzyme, STAT5 F: 5’- CTGCAGGGCTGTTCTGA GA-3‘, STAT5 R: 5’- TGGTACCAGGACTGTAGCACAT-3‘ oligonucleotide primers (10pmol) and AvaI restriction enzyme and AGPAT6 F: 5’-ATCTGGCATTTTCACACATT-3‘, AGPAT6 R: 5‘- CTGACTCCATCTAAGAGCCT-3‘ oligo-nucleotide primers (10pmol) and NcoI restriction enzyme. The reaction conditions were as follows: an initial denaturation step at 95°C for 2 min; 35 cycles of denaturation at 94°C for 30 s, annealing for 45 s at temperature presented in Table 1 and elongation at 72°C for 45 s; and a final elongation step at 72°C for 7 min. After amplification, 10 μl of PCR product were digested with selected restriction enzyme (Table 1) according to producer recommendations (Thermo Fisher Scientific, Waltham, MA, USA).



The digested products were detected by electrophoresis in 2% and 3% (LPL) agarose gel stained with ethidium bromide. The ethidium bromide was added to agarose to a final concentration of 0.5 µg/ml (Thermo Fisher Scientific, Waltham, MA, USA). Fragment identification was performed in ultraviolet light, using MiniBIS Pro Video Documentation System (DNR Bio Imaging System, Neve Yamin, Israel).

The observed heterozygosity (HO) per animal, within breed, was calculated, based on the markers which passed the quality control and compared to the expected heterozygosity under Hardy Weinberg Equilibrium (HE). HO was calculated as the number of heterozygotes divided by the total number of genotypes.
 
Statistical analysis
 
Genotypic and allelic frequencies of polymorphism sites on the AGPAT6, STAT5, LPL genes were analysed by the Chi-square test; statistical characteristics in the sample (n) – arithmetic mean (M) and standard error of the mean (SE) – were calculated using the SPSS software (version 20.0). Values of SCC (×1000 cells/mL) were transformed into base 10 logarithm to normalize their distributions. Student T-test was calculated for comparison between quantitative variables. The results were considered statistically significant as P≤0.05.

Variations of the goat AGPAT6, STAT5, LPL genes were detected by PCR-RFLP method (Table 2). In the investigated group, the genotype CG of AGPAT6 gene was present in 44.0 % of goats, the genotype CC of STAT5 gene was found in 69.0 % of goats and, with the highest frequency, GG genotype (51.0 %) was found in the LPL gene. Allele C of the gene AGPAT6 had higher frequency than G allele. Allele C of the gene STAT5 had higher frequency than T allele, while allele G of LPL gene had higher frequency than G allele.



The results of our study on the AGPAT6 gene were different from those obtained by other researchers. For instance, He with other researchers (2011) found the following genotype frequencies: GG (0.922 in Xinong Sannen, 0.890 in Guanzhong), GC (0.067 in Xinong Sannen, 0.093 in Guanzhong), CC (0.011 in Xinong Sannen, 0.018 in Guanzhong goats).

The frequencies of STAT5 alleles C and T of 0.863 and 0.137 respectively reported by Selvaggi et al. (2015) were close to our results. The results of our study on the STAT5 allele frequencies differed from those obtained by Wu et al. (2014), they found the frequencies of STAT5 alleles C – 0.638 and T – 0.362 in Xinong Saanen goats.

Crepaldi et al. (2013) estimated lower genotype frequencies of LPL gene in Alpine goats: (CC – 0.02, CG – 0.26) compared to our results, while frequency of GG genotype was similar (0.764).

Comparison of CC, CG, GG genotypes of goat AGPAT6 gene shows (Table 3) that goats with CG genotype have higher milk yield (27.31 % higher compared to CC, P<0.05; 21.01% – to GG genotype, P<0.05). The comparison of CC, CG, GG genotypes of goat AGPAT6 gene shows that the average of fat and lactose did not differ significantly. Meanwhile, goats with CG genotype have higher protein content (0.4% higher compared to CC and 0.36% - to GG genotype, P<0.05). In the examined group, the goats with the genotype GG have higher content of urea and SCC (P<0.01). In comparison with our results, He with other researchers (2011) found that AGPAT6 genotype GG and CG goats showed significantly higher milk fat, protein content and milk yield than those with the CC genotype (P<0.05).



Study in goat STAT5 gene shows that goats with CT genotype had higher, but non-significant, milk yield compared to CC and TT genotype. The STAT5 CC genotype had higher fat and lactose content and the lowest urea and SCC compared to genotypes CT and TT.  An et al. (2013) studied the association among genotypes at STAT5 locus and milk performance traits of animals: the goats with CT genotype had greater milk yield than those with CC genotype (P<0.05). Researcher Bao et al. (2010) found that cows of genotype CT showed higher protein content than cows of the CC genotype (P<0.05).

The results showed that the goat LPL gene with CC genotype has higher milk yield (37.39% higher compared to CG, P<0.001 and 8.05% higher compared to GG genotype, P<0.05). The goats with GG genotype produce milk with higher fat content (0.12% higher compared to CC, 0.22% higher compared to GC genotype, P<0.05) and protein content (0.08% higher compared to CC; 0.01% higher compared to CG genotype, P<0.05). In comparison with our results, Svitáková et al. (2014), in the research with Czech dairy goats, found a significant effect of LPL gene on fat percentage and protein percentage. Crepaldi et al., (2013) in the research with Alpine goats reported that the LPL was highly associated with goat’s milk yield of CC genotype compared to GG genotype goats and fat percentage showed a rather consistent difference among genotypes during lactation, whereas differences for protein content were relevant mainly until about 120 days in milk.

The mean values of milk yield and content of different goat breeds are demonstrated in Table 4, where Saanen and Alpine crossbreed goats showed significant highest milk yield compared to Czech White, Shorthaired, Saanen, Anglo Nubian, Alpine, Saanen and Anglo Nubian crossbreeds (25.35%, 25.07%, 0.14 %, 3.82% and 2.24%, respectively). Mioč et al. (2008) reported similar results with Saanen goat breed (2.63 kg/day), while milk yield of Alpine breed was lower (2.08 kg/day). In the present study, the daily milk yield ranged from 1.148 in Anglo Nubian goats to 1.686 in Saanen and Alpine crossbreds. The milk yield of Czech White, Saanen and Anglo Nubian was lower than that reported by Bolacali and Kucuk (2012) but higher than estimated by Rojo-Rubio et al. (2015), where milk yield of Alpine goats was – 1.41/day kg, Saanen – 1.28 kg/day and Anglo Nubian – 1.18 kg/day.



Anglo Nubian goats showed significantly higher milk fat (0.50 %-0.74 % higher in Saanen and Anglo Nubian and Saanen and Alpine crossbreed) and protein (0.14 %-0.35 % higher in Saanen and Anglo Nubian and Saanen and Alpine crossbreeds) content compared to other breeds. The breed influence on milk lactose was significant and ranged from 3.81 % (Anglo Nubian breed) to 4.30 % (Saanen breed).

The mean values of fat, protein and lactose contents in the present study in a milk of investigated breeds were within the range of the estimates recorded for dairy goats by Mioč​ et al. (2008), where Alpine goats’ fat content was 3.47%, Saanen – 3.25%; protein content of Alpine goats was 3.08%, Saanen – 3.25%; lactose content of Alpine goats was 4.54 %, Saanen – 4.46%. In the research conducted by Ferro et al. (2017), the milk fat, protein and lactose content was lower compared to the present study results.

The mean values of milk urea of different breeds of goats ranged from 35.07 mg/dl in Saanen and Alpine crossbreeds to 46.30 mg/dl Saanen and Anglo Nubian crossbreeds and were consistent with the results reported by Giaccone et al. (2007).

In the present research, SCC of Saanen goats was the highest and significant differences of SCC variations between breeds were estimated between Saanen and Anglo Nubian 6.97 %, P<0.01 and between Saanen and Alpine crossbreeds 5.85 %, P<0.05. Significant variation in SCC among breeds was noted by Csanadi et al. (2015) in the research with Saanen and Alpine x Saanen crossbred goats and in the research announced by Pleguezuelos et al. (2015) SCC was equal to 5.780.

Evaluating the Hardy-Weinberg equilibrium principle, the calculation of the observed and expected heterozygosity in the studied group of goat genes AGPAT6 (P<0.05) and LPL (P<0.05) was found to be lower than expected, indicating a lack of heterozygosity; STAT5 was near expected heterozygosity, but still lower than P<0.05 (Table 5).



He et al. (2011) reported AGPAT6 significant differences with an observed heterozygosity higher than expected and it was in disagreement with the results of our research. Coizet et al. (2017) in the research with Bubalus Bubalis estimated that STAT5 was not in HW equilibrium, with significantly different values for the observed and expected heterozygosity (P<0.05), where the observed heterozygosity was higher than expected, but no bibliographic references were found about goats. Crepaldi et al. (2013) reported LPL deviated significantly from the Hardy-Weinberg equilibrium, with an observed heterozygosity lower than expected and it was in agreement with the present study.

Examined AGPAT6, STAT5 and LPL genes polymorphisms appear to be the valuable biomarkers of the goat selection process. Also, further research on larger samples of goats is needed to properly assess the effect of these genes, especially for those markers that show rare genotypes, which may offer useful indications for the development of gene assisted programs.