Validation of Reference Genes for Real Time PCR Normalization in Milk Somatic Cells of Holstein Dairy Cattle

Document Type: Research Articles


1 Department of Animal Science, Faculty of Agriculture, Yasouj University, Yasouj, Iran

2 Cellular and Molecular Research Center, Yasouj University of Medical Science, Yasouj, Iran


Real time-qPCR is the most reliable method for evaluation of mRNA expression levels. However, to obtain accurate results, selection of suitable reference genes is necessary for normalizing the real-time qPCR data. The aim of this research was to validate the expression stability of three potential reference genes (ACTB, GAPDH and UXT) in milk somatic cells of Holstein dairy cattle under different lactation stages. For this purpose two types of milk samples from eighteen healthy cows at three lactation stages (early, middle and late of lactation cycle) and four mastitic cows were included in this experiment. Total RNA was extracted from the milk somatic cells and then cDNA was synthesized. Real-time polymerase chain reaction (PCR) performed for actb, gapdh and UXT genes as candidate reference genes. Then, the real-time PCR results were analyzed with BestKeeper program. The evaluation of selected genes by real-time PCR revealed that all genes were expressed in the healthy and mastitic dairy cows. In addition, the UXT and GADPH genes displayed the lowest and highest values of expression level, respectively. The ACTB gene was considered as the most suitable internal controls as it was stably expressed in milk somatic cells regardless of dairy cows conditions. Taken together, our results could help to select suitable reference gene for the normalization of expression levels in milk somatic cells of dairy cattle.



Quantitative real-time PCR (qPCR) technique is considered to be the most accurate and reliable method for gene expression analysis. It has the advantages of sensitivity, real time detection of reaction progress, speed of analysis and precise quantification of the material in the sample (Gachon et al. 2004). The qPCR is a multistage process and the accuracy of obtained results depends on several factors including the quality, stability and input of RNA, the efficiency of reverse transcription, primer performance, reference genes, PCR steps and method chosen for data analysis (Bustin, 2002; Bustin and Nolan, 2004; Pfaffl, 2001; Skern et al. 2005; Fleige and Pfaffl, 2006; Derveaux et al. 2010). Among them, the choice of suitable reference genes to normalize data is a great importance to obtain accurate results. A suitable reference gene should be expressed at a constant level among samples, and its expression is assumed to be unaffected by the experimental conditions (Bustin, 2002). The use of unsuitable reference genes may lead to errors in quantification and, then, the expression data may lead to misinterpretation. Reference gene validation was carried out in different organs of dairy and beef cattle, such as adipose tissue (Saremi et al. 2012), liver, kidney, pituitary and thyroid (Lisowski et al. 2008), milk somatic cells (Varshney et al. 2012; Verbeke et al. 2015), mammary gland (Bionaz and Loor, 2007; Bougarn et al. 2011), oocyte (Macabelli et al. 2014; Mahdipour et al. 2015) and whole blood samples of cows (Devrim et al. 2012; Kishore et al. 2013; Kizaki et al. 2013). Several statistical procedures or software packages have been reported to evaluate the stability expression in candidate reference genes, such as geNorm (Vandesompele et al. 2002), NormFinder (Andersen et al. 2004), BestKeeper (Pfaffl et al. 2004) and Stability index (Brunner et al. 2004), with the ranking of candidate reference genes depending upon the selected software. Up to now, based on our knowledge, there is no any report for validation of reference genes in milk somatic cells of Holstein dairy heifers. Therefore, the aim of this study was to evaluate the stability of β-actin, related to cell structure (ACTB), glyceraldehyde-3-phosphate dehydrogenese, related to carbohydrate metabolism (GAPDH) and ubiquitously-expressed transcript, related to activation of transcriptional activation (UXT) genes in milk somatic cells of Holstein dairy cows at first lactation under different lactation stages using BestKeeper program.



Eighteen healthy Holstein dairy cows at first lactation were classified according to their lactation stages (6 at 7-10, 6 at 140-150 and 6 at 290-295 days after parturition). The selection criteria was somatic cell count (SCC) less than 350000/mL milk for early lactation stage and SCC < 100000/mL for middle and late lactation stages. In addition of healthy cows, four dairy cows at first lactation with clinical mastitis were also included in this experiment. In healthy cows, one liter of milk sample representing all four quarters was collected in sterile tubes. The milk samples from cows with mastitis were collected from the quarter with clinical mastitis immediately after the onset of clinical signs and before drug treatment. Then, the milk sample was centrifuged for 20 min at 1500 g at 4 ˚C. The cell pellet was washed in phosphate-buffered saline (PBS) pH 7.4 twice and centrifuged for 20 min at 4 ˚C and 220 gaccording to Liebe (1996). The pellets were resuspended with 500 µL phosphate-buffered saline (PBS)- ethylenediaminetetraacetic acid (EDTA) and kept at -40 ˚C until RNA isolation. Total RNA was extracted using Denazist kit according to the manufacturer’s protocol. All samples were DNase I (Cinnagen) treated to eliminate DNA genomic contamination. RNA quality was assessed by electrophoresis on 1% agarose gel stained with ethidium bromide. All RNA samples were reverse transcribed using AccuPower® RocketScriptTM RT PreMix kit (Bioneer) and random hexamer primers (Takapozist) according to the manufacture instructions. The final volume was adjusted to 50 µL with RNase free water. The amplified cDNA samples were then stored at -20 ˚C until use in real-time PCR. The primers sequences and their characteristics are shown in Table 1. Real-time PCR using CFX96 (BIORAD, USA) was performed on 3 candidate endogenous control genes for each of the 22 bovine milk cell samples using the HotTaq EvaGreen qPCR kit (Cinnagen). A PCR mix (10 mL) was prepared to give the end concentrations: 5 mL water, 1 mL each of the forward and reverse primers (10 pm), 1 mL of cDNA and 2 mL of HotTaq EvaGreen qPCR master mix. All reactions were performed in duplicate. The amplification conditions were based on reference papers (Table 1). In each reaction of real-time PCR, the cycle number at which the fluorescence rises appreciably above the background fluorescence is determined as crossing point (CP). Subsequently, a melting step was performed, consisting of 95 ˚C for 5 s. 65 ˚C for 5 s. and slow heating at a rate of 0.5 ˚C per 5 second up to 95 ˚C, with continuous fluorescence measurement, and finally followed by cooling down to 25 ˚C. Expression stability of potential references genes are evaluated by BestKeeper program. To identify the most stable reference gene, this program use raw CP values and amplification efficiencies. The stability estimation of reference genes expression is based on the inspection of calculated variations (standard deviation (SD) and coefficient of variation (CV) values). According to the variability observed, reference genes can be ordered from the most stably expressed, exhibiting the lowest variation, to the least stable one, exhibiting the highest variation. Any studied gene with the SD higher than 1 can be considered inconsistent (Pfaffl et al. 2004). In addition, pearson correlations between each individual gene and the BestKeeper index (geometric mean between CP values of stable genes) were calculated as the BestKeeper correlation coefficient. Genes with the highest BestKeeper correlation coefficient were considered the most stably expressed (Bonefeld et al. 2008; Mehta et al. 2010). For each primer, the efficiency of qPCR and R2 were estimated using a standard curve obtained from a pooled cDNA of all samples serially diluted 10-fold over 6 measuring points with two replications.



The screening of three potential references genes by real-time PCR showed that all genes were expressed in the healthy (at different lactation stages) and mastitic Holstein dairy cows. Gene expression levels of the candidate reference gene (expressed in CP values) are displayed in Table 2. In this study, the expression levels of UXT and GADPH genes revealed lowest and highest values, respectively.


Table 1 Primers characteristics of 3 potential reference genes in real time PCR



Gene expression variation was calculated for all three candidate reference genes based on CP-values and displayed as the standard deviation (SD) and coefficient of variation (CV). BestKeeper highlighted ACTB as the reference gene with the least overall variation from the three candidate genes with an SD of 0.96 (Table 2), which represents an acceptable 1.95 fold change in expression. The variation in expression of UXT and GADPH genes was greater than two-fold (SD greater than 1.0). Among the examined three candidate reference genes, UXT lies in middle with respect to its stability (Figure 1). Subsequently, pair-wise correlation between genes and also correlation between each gene and the BestKeeper index were calculated (Table 3). Correlations between the three genes ranged from -0.05 for UXT/ACTB to 0.44 for GADPH/ACTB. The highest correlation between candidate genes and the BestKeeper index was obtained for GADPH gene (0.88) that was as the least stable expressed reference gene. However, the ACTB gene also displayed significant correlation with the BestKeeper index (P<0.01). A number of authors have studied expression profiles of housekeeping genes in milk somatic cells of cattle, goat and yak using qRT-PCR, but based on our knowledge; this is the first report that the validation of housekeeping gene was investigated in Holstein dairy cows at first lactation in three lactation stages. The three candidate housekeeping genes that were evaluated in this experiment were selected from three studies investigating gene expression in milk somatic cells of Holstein dairy cattle (Table 1). PCR amplification products were obtained for three housekeeping genes in all samples but UXT was displayed consistently high CP values (greater than 35) in both sample groups and suggesting it is not expressed in sufficient quantity to be used as an effective housekeeping gene in milk somatic cells (Table 2). Very low level of amplification was also observed for UXT gene in milk somatic cell of Sahiwal dairy cattle through lactation (Varshney et al. 2012). In similar study, GAPDH showed the highest expression, whereas the expression of UXT was the lowest in mammary epithelial cells of buffalo during all stages of lactation (Yadav et al. 2012). In the present study, ACTB was the most stable reference genes in Holstein dairy heifers at different conditions, making it as a suitable reference gene for normalization of real-time PCR data in milk somatic cells. Actins are the main structural protein of cytoplasm and play important role in cell secretion, motility, cytoplasm flow and cytoskeleton maintenance (Hunter and Garrels, 1977). Verbeke et al. (2015) identified ubiquitin C (UBC), ribosomal protein S15a (RPS15A) and ACTB as the most stable genes based on their expression in bovine milk somatic cells. The expression stability of ribosomal protein L4 (RPL4), elongation factor 1 alpha (EEF1A1), GAPDH and ACTB genes were also reported in mammary epithelial cells across different lactation stages of Indian cows (Jatav et al. 2016). Nine candidate reference genes including UXT, GAPDH and ACTB were assessed in milk somatic cells of Sahiwal dairy cattle and results revealed that PPP1R11 (Protein phosphatase 1, regulatory (inhibitor) subunit 11), ACTB, UBC and GAPDH were stably expressed genes among all candidate reference genes (Varsheny et al. 2012).Jarczak et al. (2014) evaluated six potential reference genes and found that peptidylprolyl isomerase A (PPIA) and ribosomal phosphoprotein P0 (RPLP0) are the most suitable internal controls as they were stably expressed in goat milk somatic cells regardless of disease status. In addition, the expression stability of ten commonly used reference genes such as ACTB and GAPDH were examined in milk somatic cells from goats in mammary gland challenged with Staphylococcus aureus and in milk somatic cells from healthy controls. TheGlucose 6-phosphate dehydrogenase (G6PD), Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide (YWHAZ), and ACTBgenswere recommended as reference genes to normalize the qPCR data (Modesto et al. 2013). Bai et al. (2014) investigated the transcriptional stability of 10 candidate reference genes in milk somatic cells of lactating yak, including the genes of our study. Four genes, ribosomal protein S9 (RPS9), PPP1R11, UXT, and mitochondrial ribosomal protein L39 (MRPL39), were identified as being the most stable genes in milk somatic cells of lactating yak. Expression evaluation of nine candidate reference genes including ACTB, GAPDH and UXT was investigated in bovine mammary gland during the lactation cycle. UXT, RPS9 and RPS15 displayed the most expression stability across cow and time (Bionaz and Loor, 2007). Bonnet et al. (2013) studied eight candidate reference genes including UXT in different bovine and / or caprine tissues.


Table 2 Descriptive statistics of the 3 potential reference genes


GM: the geometric mean and AM: arithmetic mean.

Min: minimal value and Max: maximal value.

SD: standard deviation and CV: coefficient of variance.

Min (x-fold) and Max (x-fold): the extreme values of expression levels expressed as an absolute x-fold over- or under-regulation coefficient.

SD [± x-fold]: standard deviation of the absolute regulation coefficient.

r2: coefficient of correlation estimated by qPCR.

E: polymerase chain reaction (PCR) efficiency.


Figure 1 Expression stability of the 3 candidate reference genes in milk somatic cells of dairy heifers



Table 3 Repeated pair-wise correlation analysis of three candidate reference genes and correlation analysis reference genes versus BestKeeper index


r: coefficient of correlation.


In bovine, UXT, initiation factor 3 subunit K (EIF3K) and TBP were the most stable genes in mammary gland while in caprine, genes with the highest stability in the mammary gland were UXT, PPIA and MRPL39. The most stable genes in bovine and caprine mammary gland were UXT, EIF3K and ceroid- lipofuscinosis neuronal 3 (CLN3). Finot et al. (2011) reported that among six potential reference gene such as ACTB and GAPDH, the genes encoding for ribosomal proteins, 18S rRNA and RPLP0 presented the best expression stability in caprine mammary gland. Evaluation of appropriate housekeeping genes in the bovine mammary gland tissue samples and epithelial cell revealed that UXT and GAPDH were the most stable reference genes (Jedrzejczak and Szatkowska, 2014). The results of our study showed unstable expression with respect to the one of the most commonly used reference gene, i.e. GAPDH. Historically, GAPDH gene has been used quite frequently as single endogenous control gene in the most of studies on bovine gene expression. In the other hand, the expression of this reference gene has been shown to highly unstable in bovine mammary gland (Kadegowda et al. 2009). Therefore, to correct interpretation of qPCR results, evaluation of GAPDH as reference gene in any tissue of interest is mandatory.



In conclusion, this study identified that ACTB gene can be used as reference genes in genes expression studies on bovine milk somatic cells as it demonstrated stable expression under different experimental conditions.



This research was supported by Yasouj University.

Andersen C.L., Jensen J.L. and Orntoft T.F. (2004). Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer. Res. 64, 5245-5250.

Bai W.L., Yin R.H., Zhao S.J., Jiang W.Q., Yin R.L., Mas Z.J., Wang Z.Y., Zhu Y.B., Luo G.B., Yang R.J. and Zhao Z.H. (2014). Technical note: selection of suitable reference genes for studying gene expression in milk somatic cell of yak (Bos grunniens) during the lactation cycle. J. Dairy Sci. 97, 902-910.

Bionaz M. and Loor J. (2007). Identification of reference genes for quantitative real-time PCR in the bovine mammary gland during the lactation cycle. Physiol. Genomics. 29, 312-319.

Bonefeld B.E., Elfving B. and Wegener G. (2008). Reference genes for normalization: a study of rat brain tissue. Synapse. 62, 302-309.

Bonnet M., Bernard L., Bes S. and Leroux C. (2013). Selection of reference genes for quantitative real-time PCR normalisation in adipose tissue, muscle, liver and mammary gland from ruminants. Animal. 7, 1344-1353.

Bougarn S., Cunha P., Gilbert F.B., Meurens F. and Rainard P. (2011). Technical note: validation of candidate reference genes for normalization of quantitative PCR in bovine mammary epithelial cells responding to inflammatory stimuli. J. Dairy Sci. 94, 2425-2430.

Brunner A.M., Yakovlev I.A. and Strauss S.H. (2004). Validating internal controls for quantitative plant gene expression studies. BMC Plant. Biol. 4, 14-21.

Bustin S.A. (2002). Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J. Mol. Endocrinol. 29, 23-29.

Bustin S.A. and Nolan T. (2004). Pitfalls of quantitative real-time reverse-transcription polymerase chain reaction. J. Biomol. Tech. 15, 155-166.

Derveaux S., Vandesompele J. and Hellemens J. (2010). How to do successful gene expression analysis using real-time PCR. Methods. 50, 227-230.

Devrim A.K., Sozmen M., Yigitarslan K., Sudagidan M., Kankavi O. and Atabay H.I. (2012). Assessment of TNF-alpha and leptin gene expression by RT-PCR in blood of cows with left abomasal displacement. Rev. Med. Vet. 163, 368-372.

Finot L., Marnet P.G. and Dessauge F. (2011). Reference gene selection for quantitative real-time PCR normalization: application in the caprine mammary gland. Small Rumin. Res. 95, 20-26.

Fleige S. and Pfaffl M.W. (2006). RNA integrity and the effect on the real-time qRT-PCR performance. Mol. Asp. Med. 7, 126-139.

Gachon C., Mingam A. and Charrier B. (2004). Real-time PCR: what relevance to plant studies? J. Exp. Bot. 55, 1445-1454.

Hunter T. and Garrels J.I. (1977). Characterization of themRNAs for alpha-, beta- and gammaactin. Cell. 12, 767-781.

Jarczak J., Kaba J. and Bagnicka E. (2014). The validation of housekeeping genes as a reference in quantitative real time PCR analysis: application in the milk somatic cells and frozen whole blood of goats infected with caprine arthritis encephalitis virus.Gene. 549, 280-285.

Jatav P., Sodhi M., Sharma A., Mann S., Kishore A., Shandilya U.K. and Kumar S. (2016). Identification of internal control genes in milk derived mammary epithelial cells during lactation cycle of Indian zebu cow. Anim. Sci. J. 87(3), 344-353.

Jedrzejczak M. and Szatkowska I. (2014). Bovine mammary epithelial cell cultures for the study of mammary gland functions. In vitro. Cell. Dev. Biol. Anim. 50, 389-398.

Kadegowda A.K.G., Bionaz M., Thering B.J., Piperova L.S., Erdman R.A. and Loor J.J. (2009). Identification of internal controls for quantitative PCR in mammary tissue of lactating cows receiving lipid supplements. J. Dairy Sci. 92(5), 2007-2019

Kishore A., Sodhi M., Khate K., Kapila N., Kumari P. and Mukesh M. (2013). Selection of stable reference genes in heat stressed peripheral blood mononuclear cells of tropically adapted Indian cattle and buffaloes. Mol. Cell. Probes. 27, 140-144.

Kizaki K., Shichijo-Kizaki A., Furusawa T., Takahashi T., Hosoe M. and Hashizume K. (2013). Differential neutrophil gene expression in early bovine pregnancy. Reprod. Biol. Endocrinol. 11, 10.

Lee J.W., Bannerman D.D., Paape M.J., Huang M.K. and Zhao X. (2006). Characterization of cytokine expression in milk somatic cells during intramammary infections with Escherichia coli or Staphylococcus aureus by real-time PCR. Vet. Res. 37, 219-229.

Leutenegger C.M., Alluwaimi A.M., Smith W.L., Perani L. and Cullor J.S. (2000). Quantitation of bovine cytokine mRNA in milk cells of healthy cattle by real-time TaqMan polymerase chain reaction. Vet. Immunol. Immunopathol. 77, 275-287.

Liebe A. (1996). Interrelation between somatic cell counts and concentrations of growth factors, and mastitis in cows kept in different housing systems. Ph D. Thesis. Technical University of Munich, Munich, Germany.

Lisowski P., Pierzchala M., Goscik J., Pareek C.S. and Zwierzchowski L. (2008). Evaluation of reference genes for studies of gene expression in the bovine liver, kidney, pituitary, and thyroid. J. Appl. Genet. 49, 367-372.

Macabelli C.H., Ferreira R.M., Gimenes L.U., de Carvalho N.A.T., Soares J.G., Ayres H., Ferraz M.L., Watanabe Y.F., Sangalli J.R., Smith L.C., Baruselli P.S., Meirelles F.V. and Chiaratti M.R. (2014). Reference gene selection for gene expression analysis of oocytes collected from dairy cattle and buffaloes during winter and summer. PLoS One. 9, e93287.

Mahdipour M., Van Tol H.T., Stout T.A. and Roelen B.A. (2015). Validating reference microRNAs for normalizing qRT-PCR data in bovine oocytes and preimplantation embryos. BMC Dev. Boil. 15(1), 1-11.

Mehta D., Menke A. and Binder E.B. (2010). Gene expression studies in major depression. Curr. Psychiatry. Rep. 12, 135-144.

Modesto P., Peletto S., Pisoni G., Cremonesi P., Castiglioni B., Colussi M. Caramelli M., Bronzo V., Moroni P. and Acutis P.L. (2013). Evaluation of internal reference genes for quantitative expression analysis by real-time reverse transcription-PCR in somatic cells from goat milk. J. Dairy Sci. 96, 7932-7944.

Pfaffl M.W. (2001). A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids. Res. 29, e45.

Pfaffl M.W., Tichopad A., Prgomet C. and Neuvians T.P. (2004). Determination of stable housekeeping genes, differentially regulated target genes and sample integrity:BestKeeper-Excel-based tool using pair-wise correlations. Biotechnol. Lett. 26, 509-515.

Saremi B., Sauerwein H., Danicke S. and Mielenz M. (2012). Technical note: identification of reference genes for gene expression studies in different bovine tissues focusing on different fat depots. J. Dairy Sci. 95, 3131-3138.

Skern R., Frost P. and Nilsen F. (2005). Relative transcript quantification by quantitative PCR: roughly right or precisely wrong? BMC Mol. Biol. 6, 10-18.

Vandesompele J., De Preter K., Pattyn F., Poppe B., Van Roy N., De Paepe A. and Speleman F. (2002). Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome. Biol. 3(7), 1-11

Varshney N., Mohanty A.K., Kumar S., Kaushik J.K., Dang A.K., Mukesh M., Mishra B.P., Kataria R., Kimothi S.P., Mukhopadhyay T.K., Malakar D., Prakash B.S., Grover S. and Batish V.K. (2012). Selection of suitable reference genes for quantitative gene expression studies in milk somatic cells of lactating cows (Bos indicus). J. Dairy Sci. 95, 2935-2945.

Verbeke J., Van Poucke M., Peelman L. and De Vliegher S. (2015). Differential expression of CXCR1 and commonly used reference genes in bovine milk somatic cells following experimental intramammary challenge. BMC Genet. 16(1), 1-9.

Yadav P., Singh D.D., Mukesh M., Kataria R.S., Yadav A., Mohanty A.K. and Mishra B.P. (2012). Identification of suitable housekeeping genes for expression analysis in mammary epithelial cells of buffalo (Bubalus bubalis) during lactation cycle. Livest. Sci. 147, 72-76.