Next Article in Journal
IL-1R8 as Pathoimmunological Marker for Severity of Canine Chronic Enteropathy
Previous Article in Journal
Prevalence of Reticulocytosis in the Absence of Anemia in Dogs with Cardiogenic Pulmonary Edema Due to Myxomatous Mitral Valve Disease: A Retrospective Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Veterinary Sciences
Volume 9
Issue 6
10.3390/vetsci9060294
vetsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Single Nucleotide Polymorphisms, Gene Expression and Economic Evaluation of Parameters Associated with Mastitis Susceptibility in European Cattle Breeds

by
Ahmed I. Ateya
1,*,
Samer S. Ibrahim
1 and
Mona M. Al-Sharif
2
1
Department of Husbandry & Development of Animal Wealth, Faculty of Veterinary Medicine, Mansoura University, Mansoura 35516, Egypt
2
Department of Biology, College of Science, University of Jeddah, Jeddah 21589, Saudi Arabia
*
Author to whom correspondence should be addressed.
Vet. Sci. 2022, 9(6), 294; https://doi.org/10.3390/vetsci9060294
Submission received: 21 May 2022 / Revised: 7 June 2022 / Accepted: 10 June 2022 / Published: 14 June 2022
Browse Figure
Versions Notes

Abstract

:
The objective of this study was to explore single nucleotide polymorphisms (SNPs), gene expression and economic evaluation of parameters associated with mastitis susceptibility in Holstein and Brown Swiss dairy cows. Two hundred and forty Holstein and Brown Swiss dairy cows (120 cows of each breed) were used in this study. The investigated dairy cows in each breed were allocated into two equal-sized groups (60 cows each); mastitis tolerant and affected groups. PCR-DNA sequencing of SELL, ABCG2, SLC11A1, FEZL, SOD1, CAT, GPX1, and AhpC/TSA revealed nucleotide sequence variations in the form of SNPs associated with mastitis tolerance/susceptibility in investigated Holstein and Brown Swiss dairy cows. Levels of SELL, SLC11A1 and FEZL gene expression were significantly up-regulated in mastitic Holstein and Brown Swiss dairy cows than in tolerant ones. Meanwhile, ABCG2, SOD1, CAT, GPX1, and AhpC/TSA genes were significantly downregulated. Regarding the economic parameters, significant differences were recorded for net returns and a reduction in the percentage of net profit, as the higher values of net returns were recorded for tolerant dairy cows than mastitic ones in both breeds; moreover, the net profit was reduced by 39% and 27% in mastitic Holstein and Brown Swiss dairy cows, respectively, when compared to tolerant ones. The results herein confirmed the potential significance of investigated genes as candidates for mastitis tolerance/susceptibility in Holstein and Brown Swiss dairy cows. Mastitis also has detrimental impacts on economic efficiency in dairy farms.

1. Introduction

Mastitis is an inflammatory and important economic disease of the livestock that occurs in response to a bacterial, chemical, thermal or mechanical injury [1]. Following infection of the mammary gland, macrophages and epithelial cells release cytokines that cause the migration of neutrophils, monocytes and other leukocytes from the blood to the site of infection in the mammary tissue. Mastitis is also considered one of the most costly production-related diseases in the dairy industry [2]. The cost associated with mastitis in Europe, according to current estimates, is EUR 1.55 billion per year (European Union http://www.sabre-eu.eu/ (accessed on 1 January 2020)). Economic losses due to mastitis may reach up to USD 35 million, per year, worldwide [3,4]. The frequency and cost of mastitis, and rising public concerns for animal welfare, have made mastitis one of the most important diseases for the dairy sector [5].
Mastitis is a costly disease due to the increasing expenditure to reduce its level and reduction of output. Mastitis is responsible for 38% of total direct losses, and remains the most common disease of economic importance in the dairy industry. Subclinical mastitis has 70% of total financial losses that are associated with decreased milk production, deterioration of milk quality, costs of health management, milk disposal, advanced culling, and the addition of labour requirements [6,7]. The economic loss estimates of clinical mastitis differ largely between farms [8]; mastitis causes also serious economic losses in the dairy sector from the most to the least organised herds [9]. Mastitis is a multifactorial disease, closely related to the environment in which the cows are kept, and its severity can be mild, moderate and severe or permanent [10]. For all decision-makers, the economic benefits and incentives induce the perfection of mastitis management. [11]. There are certain other limitations as the evolvement of antibiotic-resistant mutant bacteria by genetic shift and genetic drift, and therefore vaccinations are not 100% effective; studies have been attempted with bovine mastitis prevention by inoculation of lactic acid bacteria at the dry-off period [12].
The immunity is divided into two, namely the innate and adaptive immunity, in mammals. Innate immunity is present both in vertebrates and invertebrates, whereas adaptive immunity is only present in vertebrates [13]. Host genetic resistance is mainly sustained by innate immunity, providing protection against pathogens without being vaccinated or exposed to diseases [14]. Despite the worldwide efforts to improve herd management practices, mastitis control in the dairy industry remains inadequate. Thus, there is a high demand for measures which would allow us to reduce the incidence of this disease. According to Heringstad et al. (2003), genetic improvement of mastitis resistance can be achieved by traditional breeding [15]; however, it is also known that this trait is lowly heritable and unfavourably correlated with milk production traits [16,17]. Resistance to mastitis is a complex function involving various pathways contributed by numerous candidate genes [18]. Recent developments in genome sequencing technologies applied to livestock have facilitated the identification of copy number variations (CNVs) and millions of SNPs in a relatively cost-efficient manner. This has enabled researchers to describe the genomic landscape of livestock species [19,20,21], and to combine these whole-genome sequence data with phenotypic information for genomic prediction [22] or in genome-wide association studies to identify variations associated with various traits [23,24].
Although there is little information on SELL, ABCG2, SLC11A1, and FEZL gene polymorphisms and their association with mastitis susceptibility in dairy cattle, previous studies reported this association in one breed using RFLP and SSCP genetic markers with opposing results [25,26,27,28,29]. Based on the current knowledge, there were no previous studies revealing the association of SNPs and expression profile of immune and antioxidant markers in two breeds of dairy cows. Consequently, the aim of the current work was to revealing the association of SNPs and expression profile of SELL, ABCG2, SLC11A1, FEZL, SOD1, CAT, GPX1, and AhpC/TSA genes with mastitis tolerance/susceptibility in Holstein and Brown Swiss dairy cows. Another aim was to evaluate some economic parameters associated with mastitis susceptibility in these two breeds.

2. Material and Methods

2.1. Animals and Experimental Samples

In total, 240 Holstein and Brown Swiss dairy cows (120 cows of each breed) were used in this study. Animals belonged to the same private farm located at Ismailia desert road, Ismailia Governorate, Egypt and shared the same environment. The experiment was carried out between December 2021 and February 2022. Animals were in the third lactation season and were raised in a commercial dairy herd of approximately 450 animals. Cows were 3 years of age on average and 450 kg of average body weight. Animals were housed in a cubicle (free-stall/feedlot) barn with straw-bedded stalls, and a slatted floor that was scraped regularly; they were fed a total mixed ration (TMR), milked twice/day and artificially inseminated. The investigated dairy cows were subjected to thorough clinical examination according to the standard protocols. The investigated dairy cows in each breed were allocated into two equal-sized groups (60 cows each) according to their health status and incidence of mastitis. The first group included clinically healthy cows and was assigned as mastitis tolerant group (have a previous history of mastitis resistance in the previous lactations, i.e., mastitis never observed in the previous lactations). The second group comprised cows demonstrating mastitis and was assigned as mastitis affected group (high body temperature, low appetite, swollen and tender udder, reddish and yellowish milk colour and bad odour, clotted milk, teat cracks). California mastitis test was also used for screening for the mastitis incidence in the investigated cows). Five millilitres of blood was collected from each cow in all groups via jugular vein puncture. The samples were collected into a vacutainer tube containing EDTA as an anticoagulant to yield whole blood for DNA and RNA extraction. Blood samples were kept frozen at −20 °C until subsequent DNA extraction. To avoid hydrolysis of RNA, freshly collected blood samples were sent without delay for RNA extraction. All procedures were performed in accordance with the guidelines of Mansoura University and were approved by the Ethical Committees. Research Ethics Committee, Faculty of Veterinary Medicine, Mansoura University approved the protocol of the study (code R/124).

2.2. DNA Extraction and Polymerase Chain Reaction (PCR)

Genomic DNA was extracted from whole blood was done using a Gene JET whole blood genomic DNA extraction kit following the manufacturer procedure (Thermo Scientific, Lithuania). The quality, purity, and concentration of DNA were assessed using a Nanodrop before further analysis. PCR was carried out for amplification of fragments of SELL, ABCG2, SLC11A1, FEZL, SOD1, CAT, GPX1, and AhpC/TSA genes. The primer sequences were designed according to the PubMed published sequence of Bos taurus. The primers used in the amplification are shown in Table 1. The polymerase chain reaction mixture was done in a final volume of 100 μL in a thermal cycler. Each reaction volume contained 5 μL DNA, 43 μL H2O (d.d water), 50 μL PCR master mix (Jena Bioscience, Germany), and 1 μL of each primer. The reaction mixture was subjected to an initial denaturation temperature of 95 °C for 4 min. The cycling proceeded for 35 cycles of 95 °C for 1 min for denaturation, one minute for annealing temperatures (as shown in Table 1), extension at 72 °C for 1 min and a final extension at 72 °C for 10 min. Samples were held at 4 °C. Representative PCR results were detected by agarose gel electrophoresis, and then fragment patterns were visualized under UV using a gel documentation system.

2.3. DNA Sequencing and Polymorphism Detection

Before DNA sequencing, removing primer dimmers, nonspecific bands and other impurities was done. As described by Boom et al. [30] purification of PCR products with the expected size (target bands) was carried out using a PCR purification kit following the manufacturer procedures (Jena Bioscience # pp-201×s/Germany, Jena, Germany). Quantification of PCR product was carried out using Nanodrop (Uv-Vis spectrophotometer Q5000/USA) in order to yield high products and to ensure enough concentrations and purity of the PCR products [31]. To detect SNPs in tolerant and mastitis-affected dairy cows, PCR products with target band were sent for DNA sequencing in forward and reverse directions. These products were sequenced with an ABI 3730XL DNA sequencer (Applied Biosystems, Waltham, MA, USA) according to the enzymatic chain terminator technique developed by Sanger et al. [32].
DNA sequencing data were analyzed with Chromas 1.45 and BLAST 2.0 software [33]. Differences were classified as SNPs between PCR products of studied genes and reference sequences available in GenBank. On the basis of an alignment of sequences, variation of amino acid sequence of the investigated genes between enrolled dairy cows was identified using the MEGA4 software [34].

2.4. Total RNA Extraction, Reverse Transcription and Quantitative Real-Time PCR

Total RNA was extracted from the blood of investigated dairy cows using Trizol reagent following the manufacturer’s instructions (RNeasy Mini Ki, Catalogue No. 74104). The amount of extracted RNA was quantified and qualified using NanoDrop® ND-1000 Spectrophotometer. The cDNA of each sample was synthesized following the manufacture protocol (Thermo Fisher, Catalog No, EP0441, Waltham, MA, USA). Assessment of the expression pattern of SELL, ABCG2, SLC11A1, FEZL, SOD1, CAT, GPX1, and AhpC/TSA genes was carried out using quantitative RT-PCR using SYBR Green PCR Master Mix (2x SensiFastTM SYBR, Bioline, CAT No: Bio-98002, Toronto, ON, Canada). Relative quantification of mRNA level was performed by real-time PCR using SYBR Green PCR Master Mix (Quantitect SYBR green PCR kit, Catalog No, 204141). Primer sequences were designed according to the PubMed published sequence of Bos taurus, as shown in Table 2. The housekeeping gene ß. actin was used as a constitutive control for the normalisation. The reaction mixture was carried out in a total volume of 25 µL consisting of total RNA 3 µL, 4 µL 5 × Trans Amp buffer, 0.25 µL reverse transcriptase, 0.5 µL of each primer, 12.5 µL 2 × Quantitect SYBR green PCR master mix and 8.25 µL RNase free water. The final reaction mixture was placed in a thermal cycler and the following program was carried out: reverse transcription at 50 °C for 30 min, primary denaturation at 94 °C for 8 min followed by 40 cycles of 94 °C for 15 s, annealing temperatures as shown in Table 2, and 72 °C for 30 s. At the end of the amplification phase, a melting curve analysis was performed to confirm the specificity of the PCR product. The relative expression of each gene per sample in comparison with ß. actin gene was carried out and calculated according to the 2−ΔΔCt method [35,36].

2.5. Economic Parameters

The economic data were obtained from the accurate farm records and the structured questionnaire method

2.5.1. Total Variable Costs (TVC)

Total variable costs (TVC) included labor, feed, veterinary management and uncertainly costs that include the dead animal value and costs related to production [37]. Service costs were calculated as: services number till conception × cost of one service.

2.5.2. Total Fixed Costs (TFC)

Total Fixed costs (TFC) included land, building and equipment depreciation. The buildings depreciation rate was calculated on the basis of 25 years, whereas the equipmentdepreciation was calculated on the basis of 5 years [38]. The rent value was used directly during the calculation in cases where the farms are not owned [39].
Depreciation rate = value of asset / age of asset (year).

2.5.3. Total Costs (TC)

The total costs (TC) included the sum of total variable costs and total fixed costs [40].
TC = TVC + TFC.

2.5.4. Total Return

The total return was calculated by the equation described by Fidan, 2010 [41]. All prices were also determined according to the market price during the study period.
Total return (TR) = total milk sale (kg) + return of new calves sale + return of fecal matter sale (m3).

2.5.5. Net Income (Net Return)

The net income was calculated by the following equation as described by Atallah, 2004 [42].
Net income = total return−total costs.

2.5.6. Reduction Percentage in Net Profit

The reduction percentage of net profit was calculated through the difference between the net return in healthy animals and that in mastitic animals for each breed. Then, the latter difference is compared to the net return of healthy dairy cows to know the percentage and to evaluate the impact of mastitis on the profitability of dairy cows [43].

2.6. Statistical Analysis

Ho: Single nucleotide polymorphisms, gene expression and economic evaluation approaches could not explore mastitis tolerance/susceptibility in Holstein and Brown Swiss dairy cows.
HA: Single nucleotide polymorphisms, gene expression and economic evaluation approaches could explore mastitis tolerance/susceptibility in Holstein and Brown Swiss dairy cows.
Statistical parameters were expressed as mean ± standard deviation (SD). Data were statistically analysed using SPSS program version 23 (One-way ANOVA test for comparing between the studied groups’ mean and multiple comparison Tukey‘s HSD test for estimation of post-hoc differences between means). A difference was considerably significant at P< 0.05. Chi-square analysis was carried out to assess the significant distribution in identified SNPs of genes between the total two hundred and forty (120 of each breed) mastitis tolerant and affected dairy cows. A discriminant analysis model was carried out to check the significance of different determinants to classify tolerant and affected dairy cows as a dependent variable using the gene expression profile of investigated genes as an independent variable. To discriminate between mastitic and tolerant cows based on expression profile of investigated genes, a univariate general linear model (GLM) was used to test the interaction effect of gene type and mastitis tolerance/susceptibility on gene expression results parameter, where data represented this as a mean ± SE.

3. Results

3.1. PCR-DNA Sequencing of Investigated Genes

PCR-DNA sequencing of SELL (809-bp), ABCG2 (756-bp), SLC11A1 (450-bp), FEZL (813-bp), SOD1 (334-bp), CAT (268-bp), GPX1 (534-bp), and AhpC/TSA (480-bp) revealed nucleotide sequence variations in the form of SNPs associated with mastitis tolerance/susceptibility in investigated Holstein and Brown Swiss dairy cows. Nucleotide sequence variation of the investigated genes between tolerant and affected dairy cows and reference sequences available in GenBank confirmed all identified SNPs (Figures S1–S8). Chi-square analysis revealed a significant distribution in the identified SNPs; where a significant difference was detected in frequencies of the investigated genes between the tolerant and affected Holstein and Brown Swiss dairy cows (Table 3).

3.2. Gene Expression Pattern of Immune and Antioxidant Markers

The gene expression profile of immune and antioxidant markers is depicted in Figure 1. Levels of SELL, SLC11A1 and FEZL gene expression were significantly up-regulated in mastitic Holstein and Brown Swiss dairy cows in comparison to tolerant ones; meanwhile, ABCG2, SOD1, CAT, GPX1, and AhpC/TSA genes were significantly down-regulated.
There was a significant interaction between the type of gene and mastitis tolerance/susceptibility in each breed on mRNA levels of investigated markers. Among all genes evaluated in the mastitis-affected dairy cows, the highest possible level of mRNA was identified for SELL in Holstein (2.58 ± 0.15) and Brown Swiss (1.85 ± 0.11) dairy cows; whereas, the lowest level was for CAT (0.36 ± 0.03) and ABCG2 (0.52 ± 0.07) in Holstein and Brown Swiss, respectively. In the same respect, among all genes evaluated in the tolerant dairy cows, the highest possible level of mRNA was identified for ABCG2 (2.15 ± 0.15) and SOD1 (1.98 ± 0.08) in Holstein and Brown Swiss dairy cows, respectively; whereas, the lowest level was for SCL11A1 in Holstein (0.47 ± 0.08) and Brown Swiss (0.34 ± 0.09).

3.3. Economic Evaluation of Parameters Associated with Mastitis Susceptibility in Holstein and Brown Swiss Breeds

The results depicted in Table 4 cleared that service, treatment, total veterinary management and the total costs differed significantly among tolerant and mastitic Holstein and Brown Swiss dairy cows (p < 0.05). The higher total costs were recorded in mastitic Holstein dairy cows than both tolerant and mastitic Brown Swiss ones. The total returns decreased in the mastitic dairy cows compared to the healthy ones in both breeds.
Significant differences were recorded for net returns and reduction in the percentage of net profit, as the higher values of net returns were recorded for tolerant dairy cows than mastitic ones in both breeds; moreover, the net profit was reduced by 39% in mastitic Holstein cow when compared to tolerant ones; in contrast, the net profit was reduced by 27% in mastitic Brown Swiss dairy cows in comparison to tolerant ones.

4. Discussion

The major concern of breeding is getting more robust cows [44]. Until recently, less attention has been paid to health traits, including immune response as a result of livestock species, rather they have been mainly selected for production traits [15]. Genetic improvement of mastitis resistance can be achieved by traditional breeding by the selection of favourable animals on the basis of phenotype; however, it is also known that this trait is lowly heritable and unfavourably correlated with milk production traits [16,17]. The candidate gene approach focuses more on identifying genes that are connected to mastitis through activities such as recognition of pathogens, leukocyte recruitment, migration, pathogen elimination and resolution [45,46].
In this context, PCR-DNA sequencing for fragments of SELL (809-bp), ABCG2 (756-bp), SLC11A1 (450-bp), FEZL (813-bp), SOD1 (334-bp), CAT (268-bp), GPX1 (534-bp), and AhpC/TSA (480-bp) genes revealed nucleotide sequence variations in the form of SNPs between mastitis tolerant and affected Holstein and Brown Swiss dairy cows. Chi-square analysis revealed a significant distribution in the identified SNPs. As far as we are concerned, this is the first study revealing SNPs in SELL, ABCG2, SLC11A1, FEZL, SOD1, CAT, GPX1, and AhpC/TSA genes as candidates for mastitis tolerance/susceptibility in Holstein and Brown Swiss dairy cows. Interestingly, our results indicated that the polymorphisms identified are reported here for the first time in investigated genes when compared with matched GenBank reference sequence. Although, there is little information on SELL, ABCG2, SLC11A1, and FEZL genes polymorphisms and their association with mastitis susceptibility in dairy cattle, previous studies reported these association in one breed using RFLP and SSCP genetic markers with opposing results [25,26,27,28,29]. Unlike previous studies, this context explored polymorphisms via SNP genetic marker to compare mastitis incidence in two breeds (Holstein and Brown Swiss) of dairy cattle. SNP genetic marker revolutionizes previous achievements in conservation decisions, biodiversity assessment and genetic characterization of breeds [47]. SNPs analysis could also explain the history of European cattle more accurately than other markers [48,49,50,51]. Particular importance is also attributed to SNPs in the search for linkages between a marker with a specific location in the genome and an unknown gene locus. The search for such associations is important because they allow a phenotypic effect to be assessed by identifying its genetic basis [49,51].
Dusza et al. [27] studied SELL gene polymorphism and its association with clinical mastitis and milk production in Polish Holstein-Friesian cattle. Strong associations were observed between SELL gene polymorphism and milk production traits (milk yield, milk fat percentage, and milk protein percentage); however, the polymorphism in the analysed gene had no influence on the resistance or susceptibility of cows to clinical mastitis. Chen et al. [52] reported also polymorphisms in the SELL gene cluster were associated with fertility and survival time in Holstein Friesian cows. Yue et al. [25] reported an association between ABCG2 gene polymorphisms and somatic cell scores (SCS) in Holstein cattle; however, contrasting results were reported by Asadollahpour et al. [26] who elaborated that genetic polymorphisms in the ABCG2 gene had no effect on SCS. Komisarek et al. [53] reported a significant effect of ABCG2 gene polymorphisms on estimated breeding values for milk fat production traits, calving-to-first insemination interval, and non-return rate in Polish Holstein-Friesian cattle. Genetic effects of ABCG2 polymorphism on milk production traits were also reported in the Holstein cattle [54,55]. Zhang et al. [28] elicited that SLC11A1 gene polymorphisms are not associated with somatic cell score and milk yield in Chinese Holstein. SLC11A1 gene polymorphisms were also associated with tolerance /susceptibility to bovine tuberculosis [56,57]. Ghada et al. [29] reported an association between FEZL gene polymorphisms and mastitis susceptibility in Holstein cattle. Somasundaram et al. [58] also indicated that polymorphism of the FEZL gene was associated with mastitis resistance in Indian cattle. Darwish et al. [59] indicated that SNPs in the SOD gene were associated with postpartum disorders susceptibility in Barki sheep.
In the present study, a real-time PCR was carried out to quantify the mRNA level of SELL, ABCG2, SLC11A1, FEZL, SOD1, CAT, GPX1, and AhpC/TSA genes in tolerant and susceptible Holstein and Brown Swiss dairy cows to mastitis. Our findings revealed that the Gene expression profile of SELL, SLC11A1 and FEZL genes expression were significantly up-regulated in mastitic Holstein and Brown Swiss dairy cows than tolerant ones; meanwhile, ABCG2, SOD1, CAT, GPX1, and AhpC/TSA genes were significantly downregulated. Our study is the first to detect immune and antioxidant mRNA levels in tolerant and susceptible Holstein and Brown Swiss dairy cows to mastitis using real-time PCR approach. Previous studies explored gene polymorphism using genetic markers such as RFLP and SSCP [25,26,27,28,29]; however, our study was designed to overcome the limitations of previous work by investigating polymorphism in studied genes using SNP genetic markers and gene expression. Consequently, SELL, ABCG2, SLC11A1, FEZL, SOD1, CAT, GPX1, and AhpC/TSA regulation mechanisms are well understood in the mastitis tolerant and affected Holstein and Brown Swiss dairy cows. To the best of our knowledge, this study is the first that declared the gene expression profile of immune and antioxidant markers associated with mastitis tolerance/susceptibility in dairy cattle. In terms of the gene expression profile of immune and antioxidant markers in ruminants, Darwish et al. [59] elicited that mRNA levels of SOD and CAT genes were significantly downregulated in ewes affected with postpartum disorders than in tolerant ones. Asadpour et al. [60] studied a differential expression of the antioxidant genes during clinical mastitis of cows caused by Staphylococcus aureus and Escherichia coli, where the expression profile of SOD in mastitis milk induced by S. aureus was significantly up-regulated compared with E. coli. In addition, the mRNA levels of GPx in mastitis milk due to E. coli were significantly over expressed compared to S. aureus. Ateya et al. [61] elaborated that the profound alteration in the gene expression profile of antioxidant genes could be a biomarker that helps follow-up health during the peri-parturient period in dromedary camels.
L-selectin is one of the most important adhesion molecules expressed on polymorphnuclear cells; it is coded by the SELL gene, which is responsible for neutrophil attachment to endothelium [62]. L-selectin mediates the migration of activated circulating PMN across the blood–milk barrier in the process of diapedesis through the endothelium of the mammary gland [63]. ATP binding cassette sub family G member 2 (ABCG2) gene encodes a transporter protein that facilitates the transport of medicines through the cell membrane by binding ATP; it has a crucial role in the protection of various cells and tissues against xenotoxins and/or endotoxins [64]. The expression of this gene in the mammary significantly increases during lactation compared to the dry period [65] which could decipher its up-regulation in tolerant Holstein and Brown Swiss dairy cows rather than mastitic ones. Solute Carrier Family 11 Member 1 (SLC11A1) is a trans-membrane protein and was reported to be one of the best-known potential candidate genes that promote innate immunity against different intracellular pathogens [66]. Forebrain embryonic zinc finger-like gene (FEZL) was identified as a QTL influencing mastitis resistance [67]; it also has an immune function, as it plays an important antimicrobial role by controlling the neutrophilic migration to the site of mammary gland infection [68]. Semaphorine 5A (SEMA5A) is one of the target genes of FEZL and is represented as an extensive family of widely expressed secreted and membrane-associated proteins [69]. When cows are infected with mastitis, FEZL, as a transcription factor, is able to induce tumour necrotic factor-α (TNF-α) and interleukin-8 (IL-8) through enhancing SEMA5A [70].
The field of oxidative stress in ruminant medicine is still in the early stages of development. The protective mechanisms of antioxidants are to scavenge or detoxify ROS, block their production, or sequester transition metals that are the source of free radicals [71]. These mechanisms include enzymatic and non-enzymatic antioxidant defenses produced in the body, namely, endogenous as superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx) [72] and others supplied with the diet, namely, exogenous as polyphenols [73]. Alkyl hydroperoxide reductase/Thiol-specific antioxidant (AhpC/TSA) plays a role in cell protection against oxidative stress by detoxifying peroxides and sulphur-containing radicals, and acts as a sensor of hydrogen peroxide-mediated signalling events [74]. Despite oxidative stress has been associated with numerous conditions, there is a great deal to be discovered about its role in ruminant health and production. Measuring of enzymes and vitamins antioxidants in biological samples is considered also one of the popular methods for the detection of oxidative stress [75]. The marked alteration of the expression pattern of immune (SELL, ABCG2, SLC11A1, and FEZL) and antioxidant (SOD1, CAT, GPX1, and AhpC/TSA) markers in dairy cows with mastitis may be attributed to severe inflammation that damages the affected tissue and cytotoxic radicals and pro-inflammatory cytokines that are released by the phagocytic cells [76,77]; moreover, the extra quantity of ROS in the absence of an optimal total antioxidant leads to the predominance of ROS, and compromises the immune system [78].
Regarding the economic evaluation of parameters associated with mastitis susceptibility. The higher total costs were recorded in mastitic Holstein dairy cows than both tolerant and mastitic Brown Swiss ones. The total returns decreased in the mastitic dairy cows compared to the healthy ones in both breeds. Significant differences were recorded for net returns and reduction in net profit percentage, as the higher values of net returns were recorded for tolerant dairy cows than mastitic ones in both breeds; moreover, the net profit was reduced by 39% and 27% in mastitic Holstein and Brown Swiss dairy cows, respectively, when compared to tolerant ones. These results may be attributed to the high costs of drugs, disinfectants and veterinary supervision used in the treatment course of diseased animals [7]; moreover, the lower total costs in mastitic Brown Swiss cows compared with other cows is owing to their resistance being slightly higher than Holstein, and thus the rapid recovery of diseased animals [79]. Our results are in harmony with those of Eltarabany and Ali [11] who concluded that the total variable costs and total costs increase significantly in mastitic cows compared to tolerant ones; additionally, Wolfova et al. [80] reported that the losses of mastitis were owed to the direct financial losses including losses from cost of drugs treatment, service cost, veterinary service, herdsman’s time, the cost for an extra milking machine, losses of the reduced amount of milk and losses of discarded milk during the treatment course.

5. Conclusions

Our findings highlight the significance of SNPs in SELL, ABCG2, SLC11A1, FEZL, SOD1, CAT, GPX1, and AhpC/TSA genes as genetic markers and predisposing factors for mastitis tolerance/susceptibility in Holstein and Brown Swiss breeds; these findings suggest that variability in these genes could be used as proxy biomarkers for such disorders in dairy European cattle. The variable expression pattern of investigated genes in tolerant and susceptible Holstein and Brown Swiss dairy cows to mastitis could be a reference guide; it could be also a biomarker to follow up the health status of dairy cows and to predict the most susceptible risk time for disease occurrence. Consequently, building up an effective management protocol could be obtained to improve health via good breeding and vaccination regimens. Finally, mastitis had detrimental impacts on economic efficiency in dairy farms due to the reduction of milk yield, milk returns and increase in treatment costs.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/vetsci9060294/s1.

Author Contributions

A.I.A. conceived, designed the experiment, collected blood samples, performed PCR and wrote the manuscript. S.S.I. analysed data and contributed to writing the manuscript. M.M.A.-S. performed DNA sequencing and contributed to writing the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

All procedures were performed in accordance with the guidelines of Mansoura University and were approved by the Ethical Committees. Research Ethics Committee, Faculty of Veterinary Medicine, Mansoura University approved the protocol of the study (code R/124).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors acknowledge staff members of Husbandry and Development of Animal Wealth Department, Faculty of Veterinary Medicine, Mansoura University, Egypt.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Schukken, Y.H.; Günther, J.; Fitzpatrick, J.; Fontaine, M.C.; Goetze, L.; Holst, O.; Leigh, J.; Petzl, W.; Schuberth, H.J.; Sipka, A.; et al. Host-response patterns of intramammary infections in dairy cows. Vet. Immunol. Immunopathol. 2011, 144, 270–289. [Google Scholar] [CrossRef] [PubMed]
  2. Oviedo-Boyso, J.; Valdez-Alarcón, J.J.; Cajero-Juárez, M.; Ochoa-Zarzosa, A.; López-Meza, J.E.; Bravo-Patiño, A.; Baizabal-Aguirre, V.M. Innate immune response of bovine mammary gland to pathogenic bacteria responsible for mastitis. J. Infect. 2007, 54, 399–409. [Google Scholar] [CrossRef] [PubMed]
  3. Giraudo, J.A.; Calzolari, A.; Rampone, H.; Rampone, A.; Giraudo, A.T.; Cristina Bogni, C.; Larriestra, A.; Nagel, R. Field Trials of a Vaccine Against Bovine Mastitis. 1. Evaluation in Heifers. J. Dairy Sci. 1997, 80, 845–853. [Google Scholar] [CrossRef]
  4. Pereira, U.P.; Oliveira, D.G.; Mesquita, L.R.; Costa, G.M.; Pereira, L.J. Efficacy of Staphylococcus aureus vaccines for bovine mastitis: A systematic review. Vet. Microbiol. 2011, 148, 117–124. [Google Scholar] [CrossRef] [PubMed]
  5. Thompson-Crispi, K.; Atalla, H.; Miglior, F.; Mallard, B.A. Bovine mastitis: Frontiers in immunogenetics. Front. Immunol. 2014, 5, 493. [Google Scholar] [CrossRef] [PubMed]
  6. Albenzio, M.; Taibi, L.; Muscio, A.; Sevi, A. Prevalence and etiology of subclinical mastitis in intensively managed flocks and related changes in the yield and quality of milk. Small Rum. Res. 2002, 43, 219–226. [Google Scholar] [CrossRef]
  7. Patnaik, S.; Prasad, A.; Ganguly, S. Mastitis, an Infection of Cattle Udder: A Review. J. Chem. Bio. Phys. Sci. Sec. B. 2013, 3, 2676–2678. [Google Scholar]
  8. Hogeveen, H.; Huijps, K.; Lam, T.J. Economic aspects of mastitis. New developments. New Zealand Vet. J. 2011, 59, 16–23. [Google Scholar] [CrossRef]
  9. Halasa, T.; Huijps, K.; Sters, O.; Hogeveen, H. Economic effects of bovine mastitis and mastitis management: A review. Vet. Q. 2007, 29, 18–31. [Google Scholar] [CrossRef]
  10. Mekibib, B.; Furgasa, M.; Abunna, F.; Megersa, B.; Regassa, A. Bovine Mastitis: Prevalence, Risk Factors and Major Pathogens in Dairy Farms of Holeta Town, Central Ethiopia. Vet. World 2010, 3, 397–403. [Google Scholar] [CrossRef]
  11. El-tarabany, M.S.; Ali, M.A. Incidence, Production and Economic Losses of Clinical Mastitis in Egyptian Holstein Cows. Indian J. Appl. Anim. Sci. 2015, 5, 813–818. [Google Scholar]
  12. Pellegrino, M.; Berardo, N.; Giraudo, J.; Nader-Macías, M.E.F.; Bogni, C. Bovine mastitis prevention: Humoral and cellular response of dairy cows inoculated with lactic acid bacteria at the dry-off period. Benef. Microbes 2017, 8, 589–596. [Google Scholar] [CrossRef] [PubMed]
  13. Takeda, K.; Akira, S. Roles of toll-like receptors in innate immune responses. Genes Cells 2001, 6, 733–742. [Google Scholar] [CrossRef] [PubMed]
  14. Prakash, O.; Kumar, A.; Sonwane, A.; Rathore, R.; Singh, R.V.; Chauhan, A.; Kumar, P.; Renjith, R.; Yadav, R.; Bhaladhare, A.; et al. Polymorphism of cytokine and innate immunity genes associated with bovine brucellosis in cattle. Mol. Biol. Rep. 2014, 41, 2815–2825. [Google Scholar] [CrossRef] [PubMed]
  15. Heringstad, B.; Rekaya, R.; Gianola, D.; Klemetsdal, G.; Welgel, K.A. Genetic change for clinical mastitis in Norwegian cattle: A threshold model analysis. J. Dairy Sci. 2003, 86, 369–375. [Google Scholar] [CrossRef] [Green Version]
  16. Heringstad, B.; Klemetsdal, G.; Ruane, J. Selection for mastitis resistance in dairy cattle: A review with focus on the situation in the Nordic countries. Livest. Prod. Sci. 2000, 64, 95–106. [Google Scholar] [CrossRef]
  17. Negussie, E.; Stranden, I.; Mantysaari, E.A. Genetic analysis of liability to clinical mastitis, with somatic cell score and production traits using bivariate threshold–linear and linear–linear models. Livest. Sci. 2008, 117, 52–59. [Google Scholar] [CrossRef]
  18. Hayes, B.J.; Pryce, J.; Chamberlain, A.J.; Bowman, P.J.; Goddard, M.E. Genetic architecture of complex traits and accuracy of genomic prediction: Coat colour, milk-fat percentage, and type in Holstein cattle as contrasting model traits. PLoS Genet. 2010, 6, 39. [Google Scholar] [CrossRef] [Green Version]
  19. Choi, J.-W.; Liao, X.; Stothard, P.; Chung, W.H.; Jeon, H.J.; Miller, S.P.; Choi, S.; Lee, J.K.; Yang, B.; Lee, K.T.; et al. Whole-genome analyses of Korean native and Holstein cattle breeds by massively parallel sequencing. PLoS ONE 2014, 9, 101–127. [Google Scholar] [CrossRef]
  20. Szyda, J.; Frąszczak, M.; Mielczarek, M.; Giannico, R.; Minozzi, G.; Nicolazzi, E.L.; Kamiński, S.; Wojdak-Maksymiec, K. The assessment of inter-individual variation of whole-genome DNA sequence in 32 cows. Mamm. Genome 2015, 26, 658–665. [Google Scholar] [CrossRef] [Green Version]
  21. Mielczarek, M.; Fraszczak, M.; Giannico, R.; Minozzi, G.; Williams, J.L.; WojdakMaksymiec, K.; Szyda, J. Analysis of copy number variations in Holstein-Friesian cow genomes based on whole-genome sequence data. J. Dairy Sci. 2017, 100, 5515–5525. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. VanRaden, P.M.; Tooker, M.E.; O’Connell, J.R.; Cole, J.B.; Bickhart, D.M. Selecting sequence variants to improve genomic predictions for dairy cattle. Genet. Select. Evol. 2017, 49, 32. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Sanchez, M.-P.; Govignon-Gion, A.; Croiseau, P.; Fritz, S.; Hozé, C.; Miranda, G.; Martin, P.; Barbat-Leterrier, A.; Letaïef, R.; Rocha, D.; et al. Within-breed and multi-breed GWAS on imputed whole-genome sequence variants reveal candidate mutations affecting milk protein composition in dairy cattle. Genet. Select. Evol. 2017, 49, 68. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Sanchez, M.-P.; Govignon-Gion, A.; Ferrand, M.; Gelé, M.; Pourchet, D.; Amigues, Y.; Fritz, S.; Boussaha, M.; Capitan, A.; Rocha, D.; et al. Whole-genome scan to detect quantitative trait loci associated with milk protein composition in 3 French dairy cattle breeds. J. Dairy Sci. 2016, 99, 8203–8215. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Yue, W.; Fang, X.; Zhang, C.; Pang, Y.; Xu, H.; Gu, C.; Shao, R.; Lei, C.; Chen, H. Two novel SNPs of the ABCG2 gene and its associations with milk traits in Chinese Holsteins. Mol. Biol. Rep. 2011, 38, 2927–2932. [Google Scholar] [CrossRef]
  26. Asadollahpour, N.H.; Ansari, M.S.; Edriss, M.A. Effect of LEPR, ABCG2 and SCD1 gene polymorphisms on reproductive traits in the Iranian Holstein cattle. Reprod. Domest. Anim. 2014, 49, 769–774. [Google Scholar] [CrossRef]
  27. Dusza, M.; Pokorska, J.; Makulska, J.; Kulaj, D.; Cupial, M. L-selectin gene polymorphism and its association with clinical mastitis, somatic cell score, and milk production in Polish Holstein-Friesian cattle. Czech J. Anim. Sci. 2018, 63, 256–262. [Google Scholar] [CrossRef] [Green Version]
  28. Zhang, C.; Wang, Y.; Chen, H.; Gu, C.; Fang, X. SLC11A1 gene polymorphisms are not associated to somatic cell score and milk yield in Chinese Holstein. Vet. Immunol. Immunopathol. 2009, 127, 389–392. [Google Scholar] [CrossRef]
  29. Ali, G.E.; Ibrahim, M.A.; Zaki, S.M. Association assessment of single nucleotide polymorphism in Forebrain Embryonic Zinc Finger-Like (FEZL) gene with mastitis susceptibility in Holstein cattle (Bos taurus). Large Anim. Rev. 2019, 25, 163–171. [Google Scholar]
  30. Boom, R.; Sol, C.J.; Salimans, M.M.; Jansen, C.L.; Wertheim-van Dillen, P.M.; Noordaa, J.V.D. Rapid and simple method for purification of nucleic acids. J. Clin. Microbiol. 1990, 28, 495–503. [Google Scholar] [CrossRef] [Green Version]
  31. Boesenberg-Smith, K.A.; Pessarakli, M.M.; Wolk, D.M. Assessment of DNA Yield and Purity: An Overlooked Detail of PCR Troubleshooting. Clin. Microbiol. Newsl. 2012, 34, 1–6. [Google Scholar] [CrossRef]
  32. Sanger, F.; Nicklen, S.; Coulson, A.R. DNA sequencing with chain-terminating inhibitors. Proc. Natl. Acad. Sci. USA 1977, 74, 5463–5467. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman, D.J. Basic local alignment search tool. J. Mol. Biol. 1990, 215, 403–410. [Google Scholar] [CrossRef]
  34. Tamura, K.; Dudley, J.; Nei, M.; Kumar, S. MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) Software Version 4.0. Mol. Biol. Evol. 2007, 24, 1596–1599. [Google Scholar] [CrossRef] [PubMed]
  35. Livak, K.J.; Schmittgen, T.D. Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT. Methods 2001, 25, 402–408. [Google Scholar] [CrossRef]
  36. Pfaffl, M.W. A new mathematical model for relative quantification in real-time RT–PCR. Nucleic Acids Res. 2001, 29, e45. [Google Scholar] [CrossRef]
  37. Atallah, S.T. Economic and Productive Efficiency of Veterinary Management in Dairy Farms. Ph.D. Thesis, Medicine Alexandria University, Alexandria, Egypt, 1997. [Google Scholar]
  38. Muhammad, D.F. Production Performance and Economic Appraisal of Commercial Layers in District Chakwal. Ph.D. Thesis, NWFP Agriculture University, Peshawar, Pakistan, 2002. [Google Scholar]
  39. Atallah, S.T. Study the economic and productive efficiency of some broiler farms in relation to ration constituents. Minoufyia Vet. J. 2000, 1, 169–183. [Google Scholar]
  40. Tom, K. Production and Technical Efficiency on Australian Dairy Farms; ACT: Canberra, Australia, 2000. [Google Scholar]
  41. Fidan, H. Cattle fattening systems and environmental regulations in Turkey. J. Appl. Biosci. 2010, 25, 1579–1584. [Google Scholar]
  42. Atallah, S.T. Effect of cattle diseases on reproductive, productive and economic efficiency of dairy farms. Minufiya Vet. J. 2004, 1, 99–114. [Google Scholar]
  43. El-Tahawy, A.S. Factors Affecting the Profitability of Fish Farms and Their Relation to Veterinary Management. M.V.Sc. Thesis, Alexandria University, Faculty of Veterinary Medicine, Alexandria, Egypt, 2004. [Google Scholar]
  44. Mallard, B.A.; Emam, M.; Paibomesai, M.; Thompson-Crispi, K.; Wagter-Lesperance, L. Genetic selection of cattle for improved immunity and health. Jpn. J. Vet. Res. 2015, 63, 37–44. [Google Scholar]
  45. Olsen, H.G.; Knutsen, T.M.; Lewandowska-Sabat, A.M.; Grove, H.; Nome, T.; Svendsen, M.; Arnyasi, M.; Sodeland, M.; Sundsaasen, K.K.; Dahl, S.R.; et al. Fine mapping of a QTL on bovine chromosome 6 using imputed full sequence data suggests a key role for the group specific component (GC) gene in clinical mastitis and milk production. Genet. Select. Evol. 2016, 48, 79. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Fang, X.; Zhao, Z.; Jiang, P.; Yu, H.; Xiao, H.; Yang, R. Identification of the bovine HSL gene expression profiles and its association with fatty acid composition and fat deposition traits. Meat Sci. 2017, 131, 107–118. [Google Scholar] [CrossRef] [PubMed]
  47. Groeneveld, L.F.; Lenstra, A.J.; Eding, H.; Toro, A.M.; Scherf, B.; Pilling, D.; Negrini, R.; Finlay, K.E.; Jianlin, H.; Groeneveld, E.; et al. Genetic diversity in farm animals e a review. Anim. Genet. 2010, 41, 6–31. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Gautier, M.; Faraut, T.; Moazami-Goudarzi, K.; Navratil, V.; Foglio, M.; Grohs, C.; Boland, A.; Garnier, J.G.; Boichard, D.; Lathrop, G.M.; et al. Genetic and haplotypic structure in 14 European and African cattle breeds. Genetics 2007, 177, 1059–1070. [Google Scholar] [CrossRef] [PubMed]
  49. McKay, D.S.; Schnabel, D.R.; Murdoch, M.B.; Matukumalli, K.L.; Aerts, J.; Coppieters, W.; Crews, D.; Neto, D.E.; Gil, A.C.; Gao, C.; et al. An assessment of population structure in eight breeds of cattle using a whole genome SNP panel. BMC Genet. 2008, 9, 1–9. [Google Scholar] [CrossRef] [Green Version]
  50. Socol, C.T.; Iacob, L.; Mihalca, I.; Criste, F.L. Molecular and population genetics tools for farm animal genetic resources conservation: Brief overview. J. Anim. Sci. Biotechnol. 2015, 48, 95–102. [Google Scholar]
  51. Svensson, E.; Anderung, C.; Baubliene, J. Tracing genetic change over time using nuclear SNPs in ancient and modern cattle. Anim. Genet. 2007, 38, 378–383. [Google Scholar] [CrossRef]
  52. Chen, X.; Zhang, S.; Cheng, Z.; Cooke, J.S.; Werling, D.; Wathes, D.C.; Pollott, G.E. Polymorphisms in the selectin gene cluster are associated with fertility and survival time in a population of Holstein Friesian cows. PLoS ONE 2017, 1812, 0175555. [Google Scholar] [CrossRef] [Green Version]
  53. Komisarek, J.; Dorynek, Z. Effect of ABCG2, PPARGC1A, OLR1 and SCD1 gene polymorphism on estimated breeding values for functional and production traits in Polish Holstein-Friesian bulls. J. Appl. Genet. 2009, 50, 125–132. [Google Scholar] [CrossRef]
  54. Soltani-Ghombavani, M.; Ansari-Mahyari, S.; Rostami, M.; Ghanbari-Baghenoei, S.; Edriss, M.A. Effect of polymorphisms in the ABCG2, LEPR and SCD1 genes on milk production traits in Holstein cows. S. Afr. J. Anim. Sci. 2016, 46, 196–203. [Google Scholar] [CrossRef] [Green Version]
  55. Alima, M.A.; Xiea, Y.; Fana, Y.; Wua, X.; Suna, D.; Zhanga, Y.; Zhanga, S.; Zhanga, Y.; Zhanga, Q.; Liub, L. Genetic effects of ABCG2 polymorphism on milk production traits in the Chinese Holstein cattle. Appl. Anim. Res. 2013, 41, 333–338. [Google Scholar] [CrossRef]
  56. Holder, A.; Garty, R.; Elder, C.; Mesnard, P.; Laquerbe, C.; Bartens, M.-C.; Salavati, M.; Shabbir, M.Z.; Tzelos, T.; Connelly, T.; et al. Analysis of Genetic Variation in the Bovine SLC11A1 Gene, Its Influence on the Expression of NRAMP1 and Potential Association with Resistance to Bovine Tuberculosis. Front. Microbiol. 2020, 11, 1420. [Google Scholar] [CrossRef] [PubMed]
  57. Kadarmideen, H.N.; Ali, A.A.; Thomson, P.C.; Müller, B.; Zinsstag, J. Polymorphisms of the SLC11A1 gene and resistance to bovine tuberculosis in African Zebu cattle. Anim. Genet. 2011, 42, 656–658. [Google Scholar] [CrossRef] [PubMed]
  58. Somasundaram, R.K.; Gupta, I.K.; Raja, N.; Periasamy, K.; Ramasamy, S. Polymorphism of Bovine Forebrain Embryonic Zinc Finger Like (FEZL) Gene Associated with Resistance to Mastitis in Indian Cattle. Inter. J. Livest. Res. 2020, 10, 144–149. [Google Scholar] [CrossRef]
  59. Darwish, A.; Ebissy, E.; Ateya, A.; El-Sayed, A. Single nucleotide polymorphisms, gene expression and serum profile of immune and antioxidant markers associated with postpartum disorders susceptibility in Barki sheep. Anim. Biotech. 2021, 1–13. Available online: https://pubmed.ncbi.nlm.nih.gov/34406916/ (accessed on 20 September 2021). [CrossRef]
  60. Asadpour, R.; Zangiband, P.; Nofouzi, K.; Saberivand, A. Differential expression of antioxidant genes during clinical mastitis of cow caused by Staphylococcus aureus and Escherichia coli. Vet. Arh. 2021, 91, 451–458. [Google Scholar] [CrossRef]
  61. Ateya, A.; El-Sayed, A.; Mohamed, R. Gene expression and serum profile of antioxidant markers discriminate periparturient period time in dromedary camels. Mamm. Res. 2021, 66, 603–613. [Google Scholar] [CrossRef]
  62. Kishimoto, T.K.; Rothlein, R. Integrins, ICAMs, and selectins: Role and regulation of adhesion molecules in neutrophil recruitment to inflammatory sites. Adv. Pharmacol. 1994, 25, 117–169. [Google Scholar]
  63. Diez-Fraille, A.; Mehrzad, J.; Meyer, E.; Duchateau, L.; Burvenich, C. Comparison of L-selectin and Mac-1 expression on blood and milk neutrophils during experimental Escherichia coli-induced mastitis in cows. Am. J. Vet Res. 2004, 65, 1164–1171. [Google Scholar] [CrossRef]
  64. Litman, T.; Brangi, M.; Hudson, E.; Fetch, P.; Abati, A.; Ross, D.D.; Miyake, K.; Resau, J.H.; Bates, S.E. The multidrug-resistant phenotype associated with overexpresion of the new ABC half-transporter, MXR (ABCG2). J. Cell. Sci. 2000, 113, 2011–2021. [Google Scholar] [CrossRef]
  65. Farke, C.; Mayer, H.H.; Bruckmaier, R.M.; Albrecht, C. Differential expression of ABC transporters and their regulatory genes during lactation and dry period in bovine mammary tissue. J. Dairy Sci. 2008, 75, 406–414. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Kumar, N.; Ganguly, I.; Singh, R.; Deb, S.M.; Kumar, S.; Sharma, A.; Mitra, A. DNA polymorphism in SLC11A1 Gene and its association with Brucellosis resistance in Indian Zebu (Bos Indicus) and crossbred (Bos Indicus × Bos Taurus) Cattle. Asian-Australas. J. Anim. Sci. 2011, 24, 898–904. [Google Scholar] [CrossRef]
  67. Sugimoto, M.; Itoh, T.; Gotoh, Y.; Kawahara, T.; Moriya, H.; Uchimura, Y.; Sugimoto, Y. Short communication: Enhanced clinical mastitis resistance in Holsteins with a FEZL p.Gly105 (12_13) polymorphism. J. Dairy Sci. 2010, 94, 2103–2107. [Google Scholar] [CrossRef] [PubMed]
  68. Sugimoto, M.; Uchiza, M.; Kuniyuki, M. Effects of a Forebrain Embryonic Zinc Finger-Like p.Gly105 (12_13) polymorphism on mastitis resistance: An embryo-transfer study. J. Dairy Sci. 2011, 94, 2103–2107. [Google Scholar] [CrossRef] [PubMed]
  69. Sugimoto, M.; Fujikawa, A.; Womack, J.E.; Sugimoto, Y. Evidence that bovine forebrain embryonic zinc finger-like gene influences immune response associated with mastitis resistance. Proc. Natl. Acad. Sci. USA 2006, 103, 6454–6459. [Google Scholar] [CrossRef] [Green Version]
  70. Tam, K.J.; Hui, D.H.F.; Lee, W.W.; Dong, M.; Tombe, T.; Jiao, I.Z.F.; Khosravi, S.; Takeuchi, A.; Peacock, J.W.; Ivanova, L.; et al. Semaphorin 3C drives epithelial-to-mesenchymal transition, invasiveness, and stem-like characteristics in prostate cells. Sci. Rep. 2017, 7, 11501. [Google Scholar] [CrossRef] [Green Version]
  71. Masella, R.; Di Benedetto, R.; Varì, R.; Filesi, C.; Giovannini, C. Novel mechanisms of natural antioxidant compounds in biological systems: Involvement of glutathione and glutathione-related enzymes. J. Nutr. Biochem. 2005, 16, 577–586. [Google Scholar] [CrossRef]
  72. Glasauer, A.; Chandel, N.S. Targeting antioxidants for cancer therapy. Biochem. Pharmacol. 2014, 92, 90–101. [Google Scholar] [CrossRef]
  73. Caverly, J.M.; Diamond, G.; Gallup, J.M.; Brogden, K.A.; Dixon, R.A.; Ackermann, M.R. Coordinated Expression of Tracheal Antimicrobial Peptide and Inflammatory-Response Elements in the Lungs of Neonatal Calves with Acute Bacterial Pneumonia. Infect. Immun. 2003, 71, 2950–2955. [Google Scholar] [CrossRef] [Green Version]
  74. Wadleya, A.J.; Aldred, S.; Coles, S.J. An unexplored role for Peroxiredoxin in exercise-induced redox signalling? Redox Biol. 2016, 8, 51–58. [Google Scholar] [CrossRef] [Green Version]
  75. Celi, P. The role of oxidative stress in small ruminants’ health and production. Rev. Bras. Zootec. 2010, 39, 348–363. [Google Scholar] [CrossRef] [Green Version]
  76. Cuschieri, J.; Maier, R.V. Oxidative stress, lipid rafts, and macrophage reprogramming. Antioxid. Redox Signal 2007, 9, 1485–1497. [Google Scholar] [CrossRef] [PubMed]
  77. Rinaldi, M.; Moroni, P.; Paape, M.J.; Bannerman, D.D. Evaluation of assays for the measurement of bovine neutrophil reactive oxygen species. Vet. Immunol. Immunopathol. 2007, 115, 107–125. [Google Scholar] [CrossRef] [PubMed]
  78. Sordillo, L.M.; Aitken, S.L. Impact of oxidative stress on the health and immune function of dairy cattle. Vet. Immunol. Immunopathol. 2009, 128, 104–109. [Google Scholar] [CrossRef]
  79. Blöttner, S.; Heins, B.J.; Wensch-Dorendorf, M.; Hansen, L.B.; Swalve, H.H. Short communication: A comparison between purebred Holstein and Brown Swiss × Holstein cows for milk production, somatic cell score, milking speed, and udder measurements in the first 3 lactations. J. Dairy Sci. 2011, 94, 5212–5216. [Google Scholar] [CrossRef]
  80. Wolfova, M.; Stıpkova, M.; Wolf, J. Incidence and economics of clinical mastitis in five Holstein herds in the Czech Republic. Prevent. Vet. Med. 2006, 77, 48–64. [Google Scholar] [CrossRef]
Vetsci 09 00294 g001 550
Figure 1. Relative expression patterns of SELL, ABCG2, SLC11A1, FEZL, SOD1, CAT, GPX1, and AhpC/TSA genes in the tolerant and mastitis-affected Holstein and Brown Swiss dairy cows. a, b, c, d, ab, cd means small alphabetic letters show significance when p < 0.05.
Figure 1. Relative expression patterns of SELL, ABCG2, SLC11A1, FEZL, SOD1, CAT, GPX1, and AhpC/TSA genes in the tolerant and mastitis-affected Holstein and Brown Swiss dairy cows. a, b, c, d, ab, cd means small alphabetic letters show significance when p < 0.05.
Vetsci 09 00294 g001
Table
Table 1. Forward and reverse primer sequence, length of PCR product and annealing temperature for genes used in PCR-DNA sequencing.
Table 1. Forward and reverse primer sequence, length of PCR product and annealing temperature for genes used in PCR-DNA sequencing.
PrimerForwardReverseAnnealing Temperature (°C)Length of PCR Product (bp)Reference
SELL5’-GAAAGAAAGTAAGCCTTTCTGG -3′5′-CCAGAAAGGCTTACTTTCTTTC-3′60809Current study
ABCG25’-AAAGCTTGCGAAGTGAGGCTGA-3′5′-GTAATAAGCTCCATTGCAATAC-3′62 756Current study
SLC11A15′-GCTTGCCATGCCCGTGAGGGGCT-3′5′-TAGTAGAGATGGCAGACCTCGC-3′64450Current study
FEZL5’-GATTGGACCGTCTCAATTATACA-3′5′-CTGTGTGTTGAGGAGACCGGAC-3′62 813Current study
SOD15′-GCTTGCCATGCCCGTGAGGGGCT-3′5′-GAATCCAGCCACAGCCCCAGC-3′60334Current study
CAT5′-CTATCCTGACACTCACCGCCAC-3′5′-GAAAGTCCGCACCTGAGTGACAT-3′64 268Current study
GPX15′-GGTCGCCCGCCTTTTAAAAGCAG-3′5′-TCGGTCATGAGAGCAGTGGCG-3′64 534Current study
AhpC/TSA5′-TAAGAATTGTTTAAACTGAAA-3′5′-TATGATTCAGCAGTTTTAAGTC-3′62480Current study
-SELL—Selectin L; ABCG2—ATP binding cassette sub-family G member 2; SLC11A1—solute carrier family 11 A1; FEZL—Forebrain Embryonic Zinc Finger-Like; SOD1—Superoxide dismutase 1; CAT; Catalase; GPX1—Glutathione peroxidase 1; and AhpC/TSA—alkyl hydroperoxide reductase/thiol-specific antioxidant.
Table
Table 2. Oligonucleotide primers sequence, accession number, annealing temperature and PCR product size of genes used in real-time PCR.
Table 2. Oligonucleotide primers sequence, accession number, annealing temperature and PCR product size of genes used in real-time PCR.
GenePrimerProduct Length (bp)Annealing Temperature (°C)Accession NumberSource
SELLF5′-CAACAGGAAGAGTAAGGAGGAC-3
R5′-TTGTCCATGGCCGCTGCATGAC-3′		15160NM_174182.1 Current study
ABCG2F5′-CTGAAGGAGCTGTGTTAAGT-3′
R5′-CCAGAATGGCATTGAGGCCAG-3′		14462EU570105.1Current study
SLC11A1F5′-TGTGGCTGGATTCAAACTGCTC-3′
R5′-AGATGGCAGACCTCGCCCAAGT-3′		12362NM_174652.2Current study
FEZLF5′-CGTGTGCTGCAAGGCCGAGCTG-3′
R5′-GCGGAGTCCAGGTAGTTGAAGTA-3′		13862NM_001038198.2Current study
SOD1F5′-GGAAGCTGTGGGCCTTCACGG-3′
R5′-CCAGCCTGAAGATCCGACTCA-3′		8864NM_174615.2Current study
CATF5′-TATCCTGACACTCACCGCCA-3′
R5′-CGCTGGTAGTTGGCCACTCGA-3′		9262MK423993.1Current study
GPX1F5′-CTGGATTCGGAAACGGATACC-3′
R5′-ACGTTCTCAATGAGCAGCACCT-3′		16460NM_174076.3Current study
AhpC/TSAF5′-TCTGAATCTATTTTCATGTGTA-3′
R5′-CCACCAATGTTTCCTTACTTA-3′		12462XM_005210409.4Current study
ß. actinF5′-CTAGGCACCAGGGCGTAATG-3′
R5′-CCGTGCTCAATGGGGTACTT-3′		10960AF191490.1Current study
-SELL—Selectin L; ABCG2—ATP binding cassette sub-family G member 2; SLC11A1—solute carrier family 11 A1; FEZL—Forebrain Embryonic Zinc Finger-Like; SOD1—Superoxide dismutase 1; CAT; Catalase; GPX1—Glutathione peroxidase 1; and AhpC/TSA—alkyl hydroperoxide reductase/thiol-specifc antioxidant.
Table
Table 3. Distribution of SNPs, type of mutation in investigated genes for mastitis tolerant and affected Holstein and Brown Swiss dairy cows.
Table 3. Distribution of SNPs, type of mutation in investigated genes for mastitis tolerant and affected Holstein and Brown Swiss dairy cows.
GeneSNPsTolerant n = 120Mastitic n = 120TotalType of MutationAmino Acid Number and TypeChi Valuep-Value
Holstein
n = 60		Brown Swiss
n = 60		Holstein
n = 60		Brown Swiss
n = 60
SELLA226G34--34/240Non-synonymous77 M to V21.26<0.0001
C260T41--41/240Non-synonymous87 P to L25.64<0.0001
G338A-38-38/240Non-synonymous113 R to K23.76<0.0001
T695C-28-28/240Non-synonymous232 L to P17.51<0.0001
ABCG2A91G--1818/240Non-synonymous31 T o A11.26<0.0001
T108G-32 32/240Non-synonymous36 H to Q20.01<0.0001
G630A-38-38/240Synonymous210 T23.76<0.0001
SLC11A1A160 G-33-33/240Non-synonymous54 T to A20.63<0.0001
A218G-29-29/240Non-synonymous73 Y to C18.13<0.0001
A230C-23-23/240Non-synonymous77 E to A14.38<0.0001
FEZLT262A2231-53/240Non-synonymous88 C to N33.14<0.0001
G263A-29--29/24018.13<0.0001
T760C27--27/240Non-synonymous254 S to P16.88<0.0001
SOD1G88A-42-42/240Non-synonymous30 G to R26.26<0.0001
A160G2638-64/240Non-synonymous54 T to A40.02<0.0001
A218G-33-33/240Non-synonymous73 Y to C20.63<0.0001
A230C-28-28/240Non-synonymous77 E to A17.51<0.0001
CATC202T-362359/240Non-synonymous68 L to F36.89<0.0001
GPX1G33A-19-19/240Synonymous11 P11.88<0.0001
T375C-27 27/240Synonymous125 P16.88<0.0001
AhpC/TSAG256T2941-70/240Non-synonymous86 V to F43.77<0.0001
A298G-27-27/240Non-synonymous100 K to E16.88<0.0001
-SELL—Selectin L; ABCG2—ATP binding cassette sub-family G member 2; SLC11A1—solute carrier family 11 A1; FEZL—Forebrain Embryonic Zinc Finger-Like; SOD1—Superoxide dismutase 1; CAT; Catalase; GPX1—Glutathione peroxidase 1; and AhpC/TSA—alkyl hydroperoxide reductase/thiol-specifc antioxidant. -A—Alanine; C—Cisteine; E—Glutamic acid; F—Phenylalanine; G—Glycine; H—Histidine; K=Lysine; L—Leucine; M—Methionine; N—Asparagine; P—Proline; Q—Glutamine; R—Argnine; S—Serine; T—Threonine; V—Valine; and Y—Tyrosine.
Table
Table 4. Service cost, treatment cost, total veterinary management cost, total cost, total return, net return and reduction in net profit percentage per cow in Holstein and Brown Swiss breeds.
Table 4. Service cost, treatment cost, total veterinary management cost, total cost, total return, net return and reduction in net profit percentage per cow in Holstein and Brown Swiss breeds.
Economic ParametersHolstein n = 120Brown Swiss n = 120
Tolerant n = 60Mastitic n = 60Tolerant n = 60Mastitic n = 60
Service cost (EGP)197.30 ± 9.15 b445.60 ± 7.58 a203.40 ± 8.47 b415.20 ± 11.75 a
Treatment cost (EGP)-480.65± 8.46-395.84 ± 13.90
Veterinary management cost (EGP)690.50 ± 9.45 b850.38 ± 14.59 a670.73 ± 13.98 b795.35 ± 17.65 a
Total cost (EGP)39835.24 ± 199.79 b40978.56 ± 263.82 a36750.53 ± 220.29 b38367.78 ± 264.18 a
Total return (EGP)68579.20 ± 219.26 a58534.90 ± 128.55 c59341.10 ± 123.59 b54757.18 ± 139.51 c
Net Return (EGP)28743.96 ± 157.19 a17556.34 ± 113.75 d22590.57 ± 189.42 b16389.40 ± 120.36 c
Reduction % in Net profit-39-27
- a, b, c, d Means of tolerant and mastitic cows within the same row having different upper-case superscripts are significantly different at p ≤ 0.05.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Ateya, A.I.; Ibrahim, S.S.; Al-Sharif, M.M. Single Nucleotide Polymorphisms, Gene Expression and Economic Evaluation of Parameters Associated with Mastitis Susceptibility in European Cattle Breeds. Vet. Sci. 2022, 9, 294. https://doi.org/10.3390/vetsci9060294

AMA Style

Ateya AI, Ibrahim SS, Al-Sharif MM. Single Nucleotide Polymorphisms, Gene Expression and Economic Evaluation of Parameters Associated with Mastitis Susceptibility in European Cattle Breeds. Veterinary Sciences. 2022; 9(6):294. https://doi.org/10.3390/vetsci9060294

Chicago/Turabian Style

Ateya, Ahmed I., Samer S. Ibrahim, and Mona M. Al-Sharif. 2022. "Single Nucleotide Polymorphisms, Gene Expression and Economic Evaluation of Parameters Associated with Mastitis Susceptibility in European Cattle Breeds" Veterinary Sciences 9, no. 6: 294. https://doi.org/10.3390/vetsci9060294

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop