Somatic cell score: gene polymorphisms and other effects in Holstein and Simmental cows

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Anim Biosci. 2022;35(1):13-21

Publication date (electronic) : 2021 April 23

doi : https://doi.org/10.5713/ab.20.0720

Jindřich Čítek^1,, Michaela Brzáková², Lenka Hanusová¹, Oto Hanuš³, Libor Večerek¹, Eva Samková¹, Eva Jozová¹, Irena Hoštičková¹, Jan Trávníček¹, Martin Klojda¹, Lucie Hasoňová¹

¹Faculty of Agriculture, South Bohemia University, CZ37005 Ceske Budejovice, Czech Republic

²Department Genetic and Animal Breeding, Institute of Animal Science, CZ104 00 Prague, Czech Republic

³Dairy Research Institute, CZ16000 Prague, Czech Republic

^*Corresponding Author: Jindřich Čítek, Tel: +420-38-777-2591, E-mail: citek@zf.jcu.cz

Received 2020 October 14; Revised 2021 February 1; Accepted 2021 April 2.

Abstract

INTRODUCTION

Mastitis is one of the most crucial health problems in the dairy industry. It causes immense financial damages by decreasing the milk yield, untimely culling of dairy cows, and increasing treatment costs. Clinical and subclinical mastitis reduces the milk quality, interferes with the processing of milk and has the potential to endanger human health due to antibiotic residues [1]. For instance, under the Czech Republic (CR) conditions, with every increase in the somatic cell count (SCC) by 10⁵ mL⁻¹, milk production is reduced by 51 kg per cow per lactation on average. Fat and protein contents are decreased, as is the payment price [2–4]. In the CR, the economic loss was estimated at 410 US dollars (USD) per cow with mastitis, which is equal to revenue from sales for 950 liters of milk at the milk price of 0.43 USD per liter. In the total losses inflicted by mastitis participate the lower takings from the sale of milk by 53%, higher culling of cows (herd turnover, 20%), higher costs for drugs and treatments (14%), labor for the treatment of ill cows (7%) and penalties on the farmer’s milk price (6%).

There are various causes of mastitis. From the perspective of cow breeds, the heritability of mastitis occurrence is unfortunately very low. In the Czech dairy cattle population, heritabilities of 0.10 and repeatabilities of 0.19 at the most were found [5]. The health status of the mammary gland is often assessed indirectly by SCC or somatic cell score (SCS), as the genetic correlation between SCC and clinical mastitis is often significant [6]. However, the heritabilities of these indicators are also usually very low. In the Czech dairy population, the heritabilities of SCS were 0.10 to 0.11 in Simmental and slightly higher in Holstein, at 0.10 to 0.14, depending on the lactation stage [7]. A somewhat higher heritability of 0.19 was found in Brazilian Holstein [8].

Within this context, analyses of some major genes were performed, aimed at the identifying polymorphisms associated with udder health. The analyses are often performed together with those assessing milk performance [9]. The analysis focused inter alia on the polymorphisms in the mannose-binding lectin-associated serine protease 1 gene (MASP1). The authors found an association of g.5766A>G in the gene with milk protein percentage, but not with fat percentage, milk yield and SCS. Other authors found a significant impact of the polymorphisms in the lipocalin-2 (LCN2) gene on the average SCS of milk, but not on the milk yield, protein, fat and lactose contents, or the incidence of mastitis in cows [10,11]. These findings correspond with the fact that the LCN2 protein is secreted inter-alia by neutrophils. Another source reported the results of the analysis of three polymorphisms in the fatty acid desaturase 2 (FADS2) gene [12]. The enzyme plays a pivotal role in the biosynthesis of polyunsaturated fatty acids, and previous studies provided evidence that FADS2 was one of the most downregulated genes during negative energy balance in the liver of postpartum dairy cattle. The polymorphisms in the gene were significantly associated with test-day milk yield, fat percentage and 305-day milk, fat and protein yields, protein percentage and SCS in the investigated population. Also the associations between polymorphisms in the gene and fatty acids contents were found [13,14].

Obviously, there are some major genes with the potential to change milk production and to improve udder health and resistance against mastitis. Other authors have performed whole-genome searches and have identified a few regions, SNPs and genes associated with the indicators of infectious diseases incl. mastitis [15–17]. Next-generation sequencing enabled the establishment of a candidate gene set of 48 genes associated with mastitis in Holstein cattle [18].

The aim of this study was to evaluate the impact of different factors on the SCS in milk of Holstein and Simmental cows. The polymorphisms in the casein alpha S1 (CSN1S1), beta-casein (CSN2), kappa-casein (CSN3), beta-lactoglobulin (LGB), acyl-CoA diacylglycerol transferase 1 (DGAT1), leptin (LEP), fatty acid synthase (FASN), stearoyl CoA desaturase 1 gene (SCD1) and 1-acylglycerol-3-phosphate O-acyltransferase 6 (AGPAT6) genes were genotyped, and an association analysis was performed.

MATERIALS AND METHODS

Genotyping

DNA was isolated from the milk samples using a MagCore HF16 Plus DNA/RNA extractor (RBC Bioscience, New Taipei, Taiwan). Genotyping was performed by the PCR/RFLP method. CSN1S1 gene alleles B and C were genotyped according to the methods of Ardicli et al [22] and Kučerová et al [23]; CSN2 gene alleles A and B as in Medrano and Sharrow [24]; alleles A¹ and A² according to Miluchová et al [25]; CSN3 gene alleles A, B, C and E according to the methodology of Barroso et al [26]; LGB gene alleles A and B according to the methods of Strzalkowska et al [27]; DGAT1 gene alleles A (alanine) and K (lysine) as in Kuhn et al [28]; LEP gene alleles M and W as in Buchanan et al [29]; FASN gene alleles A and G according to Roy et al [30]; and SCD1 gene alleles C and T according to the methods of Inostroza et al [31]. AGPAT6 gene alleles C and T were genotyped by using fragment analysis as in Littlejohn et al [22–32]. The sequences of primers used in the PCR and restriction endonucleases used for genotyping are given in Supplementary Table S1. The genotype and allelic frequencies were calculated (Table 1).

Table 1

Frequencies of genotypes and alleles

RESULTS AND DISCUSSION

Some of the included genes have been studied for a long time, such as caseins, while others have only been studied for a brief time. The analyses were mostly focused on the relation to milk or meat performance, including quality. In this work, we analyzed the impact of the polymorphisms on the microbial quality of the milk; other factors were also tested.

As shown in Table 2, a significant effect of gene polymorphisms on the SCS was rare. Other than genotypes in CSN2 at p<0.05, the other genes did not show an impact on the SCS. Similarly, the effect of alleles was mostly nonsignificant. Only the differences between B and C alleles in the CSN1S1 gene, A and K alleles in the DGAT1 gene were significant at p<0.05. In CSN1S1 gene, B allele was better (lower SCS), and the genotype BB was better than BC as well, even though nonsignificantly. Regarding DGAT1 gene polymorphisms, the genotype and allele differences had the same tendency, i.e., genotype AA was nonsignificantly better than genotype KA, and allele A was better than allele K. In the CSN2 gene, genotype A¹A¹ was worse than genotypes A¹A² and A²A², but the tendency of the allele effect was opposite. However, the difference between alleles was nonsignificant and thus must be interpreted cautiously. For DGAT1, Sanders et al [44] reported the influence of K and A alleles and polymorphism in the promoter on the SCS, but their haplotypes did not show a significant impact. In addition, the interaction of the polymorphisms did not affect the SCS. The authors did not find a dominance effect of KA polymorphism on the SCS. Other authors did not find significant differences among DGAT1 genotypes, but the effect of allele substitution was significant [45]. Others refer to the significance of a DGAT1 allele substitution effect for the milk and fat yields and fat and protein percentages, but not for the SCS [46]. Another source refers to the significant influence of the CSN3 polymorphisms on the SCS [47]. However, in this context, the relationship of the SCS and the occurrence of clinical mastitis must be mentioned. In some cases, even when the impact of the polymorphisms on some genes on the SCS was stated, their effect on mastitis occurrence was not found [10,48].

Table 2

Somatic cell score according to genotype

To eliminate false positive results, also the Bonferroni correction was used, when the significancy threshold is divided by the number of genes in the analysis, the p value must be <0.005 in our case. This criterion was not met in any polymorphism analysed, it confirms the insignificancy of their effect on the SCS in cow’s milk. As the number of analyses from each cow varied from one to five, we made the analysis excluding the cows with only one sample. The results were only negligibly different from those from the all dataset (data not shown).

The combined effect of farm and year was significant, and the differences were substantial (Table 3). These findings emphasize the importance of conditions in particular dairy farms, although in our research, the system of farming was similar: the cows were kept in free stall barnss and were fed by conserved fodder in all farms. At the beginning of lactation, the SCS was lowest, and thereafter, it showed increases in the 2nd and 3rd phases, where the effect of lactation stage was significant (Figure 1). Similarly, the month of the year was significant, as there was a clear tendency for worse microbial quality in April, with the best in June to August (Figure 2). Many authors stated, that season as well as month had a significant effect on the SCC values in bulk milk [49–51]. Their results demonstrated that mastitis risks rise with increasing age or parity and during summer, late spring and early autumn [49]. Similarly Arsoy [51] found higher values of SCC in the summer months, although there are also works where they found the opposite results [50].

Table 3

Effect of different factors on the somatic cell score (p-values)

Figure 1

Somatic cell score among lactation stages. The values in columns are means, the bars are standard deviations, SCS is somatic cell score. Early is day 1 to 100, medium day 101 to 200, late day 201 to 305. ^A,B The differences are significant at p<0.01. ^a,b The differences are significant at p<0.05.

Figure 2

Somatic cell score among months during years. The values in columns are means, the bars are standard deviations, SCS is somatic cell score. ¹⁾ There were no milk samples in January and September. ²⁾ The differences February vs March; April vs March, May, July, August, December; June vs November, December are significant at p<0.05. The differences June vs February, April, May, August, October are significant at p<0.01.

We also tested other effects. Breed and lactation parity (Figure 3) were not found to be significant.

Figure 3

Somatic cell score among lactation orders. The values in columns are means, the bars are standard deviations, SCS is somatic cell score. ¹⁾ The effect of lactation order was nonsignificant.

Finally, we evaluated the phenotypic correlations of SCS to milk constituents (Table 4). The correlation with lactose content was negative and significant, while the correlation to protein was positive and significant. The correlations of SCS to casein, NFS and urea contents were positive, low and nonsignificant, and the correlations to ketones were negative, low and nonsignificant. The correlations of SCS to citric acid and acetone were also negative, low and nonsignificant, but the p values were nearing the significance threshold. This was similar to the correlation of SCS to fat percentage, but its correlation was positive. The significant relationship between the SCS and some constituents may indicate that the udder responds to the changing health status. By all means, it is applicable for the lactose percentage. Its correlation coefficient is relatively high and significant. As confirmed by other authors, the worsening health status of the mammary gland reflects the change in lactose content [52]. The authors point to the decreasing dry matter content by reducing the milk lactose and the fat, casein, and calcium in the milk of cows with subclinical mastitis. Conversely, the content of whey protein increases, as does the SCC. Mastitis is genetically correlated with lactose yield, and as the amount of the synthesized lactose is the key regulator of milk volume, this result confirms that high-producing cows are more genetically susceptible to mastitis [53,54]. Therefore, the lactose content in milk could be potentially used as an indicator to improve udder health.

Table 4

Pearson correlations for somatic cell score and content of milk constituents on the day of sampling

CONCLUSION

Generally, the impact of ten polymorphisms in nine genes was weak; only CSN2 genotypes A¹/A² showed significance, and alleles of CSN1S1 and DGAT1. The importance of farm management for milk quality was confirmed, as was the importance of lactation stage and month of the year. The lactose content in milk is a good indicator of changed health status of the mammary gland.

SUPPLEMENTARY MATERIAL

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