Research Article

Transcriptome profiling of four candidate milk genes in milk and tissue

samples of temperate and tropical cattle

Olanrewaju B. Morenikeji1,5*, Mabel O. Akinyemi4,5, Mathew Wheto3, Olawale J. Ogunshola1

Adebanjo A. Badejo2, Clifford A. Chineke1

1Department of Animal Production and Health, Federal University of Technology, Akure. Nigeria.

2Department of Food Science and Technology, Federal University of Technology, Akure. Nigeria.

3Department of Animal Breeding and Genetics, Federal University of Agriculture, Abeokuta,

Nigeria. 4Dept of Animal Science, University of Ibadan

5Animal Genetics and Genomics Laboratory, International Programs, College of Agriculture and

Life Sciences, Cornell University, Ithaca, NY 14853. USA.

*Correspondence

Dr. Olanrewaju B. Morenikeji, Animal Breeding and Genetics Unit,

Department of Animal Production and Health,

School of Agriculture and Agricultural Technology

Federal University of Technology, Akure, OD 234. Nigeria.

E-mail: obmorenikeji@futa.edu obm2@cornell.edu

Abstract

The expression of four genes involved in milk regulation and production in bovine milk and tissue

samples were profiled using quantitative PCR to identify differential gene expression. Our goal

was focus on the differential mRNA expression of milk genes (K-CN, PRL, BLG and PIT-1) in

milk samples and different tissues from four different breeds of ecologically adapted and

geographically separated cattle species. The mRNA expression identified the four milk genes

understudied most up regulated in mammary gland and milk samples as compared with other

tissues. The expression of PIT-1 gene in the brain was identified to have influenced the expression

of PRL and K-CN in the mammary and milk samples. Among the four genes, PRL had the highest

mRNA expression (144.19-fold change) in Holstein followed by K-CN with 100.89-fold change

while the smallest relative expression for most genes in this study are in the range of 0.79 to 7.35-

fold difference. White Fulani cattle was identified to have a higher expression for K-CN, PRL and

BLG compared with Angus and Ndama cattle while Holstein cattle is on top of the list on the basis

of the gene expression and gene regulation for all the four genes in this study. Also, White Fulani

and Holstein are in the same cluster based on their mRNA expression for milk genes. Our data

showed the first evidence of the molecular identification of indigenous White Fulani cattle has the

potential for higher milk production.

Keywords: Cattle, milk genes, gene expression, quantitative PCR

Introduction

Milk production varies greatly among cattle of different breeds and prevalent environmental

condition. Compared with Bos taurus which is raised in most temperate parts of the world, Bos

indicus found in the warm regions have been termed as low yielding. Large scale genetic studies

have revealed that several genes contribute to the production of milk and its components. The

environment and the animal’s condition contribute to the expression of genes underlying the milk

traits (Cole et al. 2009, Hayes et al. 2010, Wellmann and Bennewitz 2011 and Cui et al. 2014).

Gene expression of a trait depends on which condition, at what stage, and where the gene is most

up regulated or down regulated in the animal. Thus, it is important to understand the transcriptome

profile of the gene(s) that control a trait under these conditions (Liu et al. 2015 and Yang et al.

2015). Different species have shown varying abundant mRNA expression in milk and mammary

gland tissues (Farrell et al. 2004, Cane et al. 2005, Smolenski et al. 2007 and Whelehan et al.

2014). Differential gene expressions in high and low milk producing cows at varying stages of

lactation have been studied with the use of RNA-seq technology (Cui et al., 2014 and Yang et al.,

2015). Studies have shown that the mRNA transcripts in milk could be a sign of the mammary

gland tissue trancriptome. (Farrell et al. 2004, Graulet et al. 2007, Girard and Matte 2005,

Smolenski et al. 2007, Medrano et al. 2010, Wickramasinghe et al. 2011 and Ran et al. 2016).

Medrano et al. (2010) identified similarities between the mammary gland and somatic cells in milk

and stated that most genes expressed in the mammary gland were also present in the milk somatic

cells but a higher number of gene expression was found in in the MSC compared to the mammary

gland. Also, Wickramasinghe et al. (2012) observed that milk somatic cells (MSC) have a higher

milk gene transcripts compared to that of the mammary gland. The expression patterns of tissue-

specific genes, which have important functions in milk production, could aid in identifying

potential molecular markers for cattle milk yield and qualities (Baik et al. 2009, Fu et al. 2012 and

Welkard et al. 2012). Although previous studies have identified variation in milk gene expression

in different stages of lactation but variation within strains of animal at same stage and

environmental influence has not been fully studied.

A high producing and sensitive animal can take the advantage of nonfluctuating environments but

may not perform well in poor, fluctuating environments

The expression level of milk genes and their contribution to the overall milk trait has not been

studied in Nigerian cattle. Likewise, the measurement of the environmental impact on the milk

gene expression profile is yet to be determined in indigenous cattle in Nigeria. Therefore, this

study focus on interrogating the expression profile of four candidate milk genes in bovine milk

samples, mammary tissues, liver, brain and muscle through quantitative PCR (qPCR). Also, to

examine the comparative analysis of mRNA transcription of these genes between different breeds

from the temperate and tropical cattle milk samples.

Materials and Methods

Sample collection

Milk samples and tissues from mammary gland, liver, brain and muscle were harvested from 3

animals per breed (n = 12) of two Tropical Nigerian cattle (White Fulani and N’dama) and two

Temperate American cattle (Angus and Holstein). These breeds are representative from two

regional scenarios of environmental conditions which they are adapted to. A total of three

biological replicates, four breeds from two climatic scenarios were compared using quantitative

real-time PCR (qPCR). In brief, samples from White Fulani and N’dama cattle were obtained from

the milking herd and abattoir in Ondo State, Nigeria while that of Angus and Holstein cattle were

obtained from the milking herd and slaughter house in Pennsylvania State, United States of

America (USA). Both samples were collected during the dry (summer) season of the year. The

milk samples were obtained from cows at 90 days which is the peak stage of lactation and the

animals were of uniform age between 3 to 4 years old. The tissue samples were immediately put

into RNAlater (Ambion, TX) to protect the integrity of RNA before further laboratory analyses.

Selection of candidate genes and assay design

Based on the previously identified genes affecting milk production trait in cattle according to

Ahmadi et al. (2008), four genes which include Prolactin (PRL), Beta-lactoglobulin (BLg), kappas-

casein (K-CN) and pituitary-specific transcription factor(PIT-1) were selected and profiled for

comparative gene expression in bovine samples. Specific gene primer pairs were designed to

amplify products of 80–120 bp with an optimal melting temperature of 60oC and a GC content

between 50 and 60 %. In order to avoid genomic DNA amplification, the reverse primer for each

primer pair was designed spanning a predicted exon–exon junction and the primer pairs were

optimized using gradient PCR to validate the annealing temperatures which were calculated in the

primer design. The primer pairs and their sequences are shown in Table 1 The glyceraldehyde- 3-

phosphate dehydrogenase (GAPDH) and Beta Actin (ACTINB) genes were selected and used as

endogenous reference. SYBR Green® chemistry detection system was chosen for qPCR

amplification and quantification.

Total RNA isolation and cDNA synthesis

Triplicate tissue samples (30mg) from bovine tissues (mammary gland tissues, liver, and muscle)

were individually ground using a Qiagen Tissue Lyzer II while the TRIzol/chloroform protocol

(Life Technologies, USA) was used to extract the total RNA following the manufacturer’s

instructions and DNase treated with DNase I Amplification Grade (InvitrogenTM) before the

reverse transcription. Likewise, the milk samples were pelleted by centrifuging at 1800 rpm at

4°C for 10 minutes and RNA was extracted from the pellet of milk cells and treated with DNase I

Amplification Grade (InvitrogenTM) before the reverse transcription and purified using Qiagen

RNeasy Minikit. The quantity, quality and integrity of RNA samples were assessed by Qubit® 2.0

Fluorometer (Life Technologies, USA) and gel electrophoresis. cDNA was synthesized in 20 µl

reaction volumes with Oligo-dT and random primers using iScriptTM cDNA Synthesis Kit (Bio-

Rad) as recommended by the manufacturer for whole transcriptome analysis. The reaction mixture

was mixed and incubated at 37oC for 1 hour. The reaction was finalized by heating the mixture at

70oC for 10 minutes and chilled on ice. The integrity of the cDNA was examined by PCR and

1.5% agarose gel electrophoresis.

qPCR conditions and assay optimization

qPCR was performed using SYBR Green® detection chemistry. Three biological replicates per

breed from the same cDNA used for testing specific primer pairs of 4 selected milk genes (PRL,

BLg, K-CN and PIT-1) and the two housekeeping genes (GAPDH and ACTINB). For each gene,

the samples were run in a 96 well plate on a CFX96 TouchTM Real-Time PCR Detection Systems

(Bio-Rad). PCRs were performed in 10 µl total volumes per well containing 5 µl of 2x iQTM

SYBR® Green supermix (BioRad, Hercules, CA, USA), 1 µl of cDNA (0.05µg of RNA

equivalents), 0.5 µl (10 µM) of each gene-specific primer and 3 µl of DNase-free H2O. The

amplification conditions followed the manufacturer’s protocol. The PCR products from the assay

were run on 1.5% agarose gels to detect the specificity of the amplified fragment. This yielded a

single correct sized band. For melt curve analysis, a protocol with temperatures that varied from

65 °C to 95 °C with increments of 0.5 °C for 5 seconds and continuous fluorescent measurements

was used. The relative amount of all mRNAs was calculated using the comparative 2−ΔΔ Cq method.

Three biological replicates and two technical replicates per biological replicate were performed

for each experiment according to Robert et al. (2010). A duplicate No-template control (NTC)

reaction was run for each primer pair in all our qPCR experiments to confirm that there was no

contamination with exogenous material.

Data analysis and Results

Results of the real-time PCR data were represented as Cq values, where Cq was defined as the

threshold cycle number of PCRs at which amplified product was first detected. The amount of

target has an inverse correlation with the values of Cq (the lower the amounts of target, the higher

the Cq values and vice versa). The values of relative normalized expression were calculated for

each gene using the comparative 2−ΔΔ Cq method and a common threshold or Cqcutoff was set. The

N-fold differential expression in the target genes in each sample was expressed as 2−ΔΔ Cq. This

study defined increased mRNA expression as N-fold ≥2.0, "normal" expression as N-fold ranging

from 0.5001 to 1.9999, and decreased mRNA expression as N-fold ≤0.5.

Comparative gene expression variation of bovine tissues was calculated for individual genes based

on quantification cycle (Cq) values and real-time PCR efficiencies (E). The PCR efficiency (E)

for the reactions ranged from 91% to 100% for the four genes tested in this study (Table 1). Fig 1

and 2 show the specificity and reliability of the assay. The specificity of the amplifications was

indicated by the single-peak melting peaks of the PCR products while the dissociation curves

showed no peak corresponding to primer dimers or nonspecific products for any of the target genes

or endogenous control and thus validate the specificity of the assay.

Comparative gene expression analysis in bovine samples

The expression levels obtained by the real-time PCR analysis for the genes under study were

calculated as Cq and compared with the house keeping genes as internal control by the Bio-Rad

CFX manager Software. Hierarchical cluster analysis and visualization were also generated. The

mean Cq values for each sample were pooled across the four breeds so as to determine the overall

expression from all samples. It is observed from Fig 3 and 4 that the PIT-1 is strongly expressed

in brain but slightly expressed in the milk and the liver while Blg, K-CN and PRL genes are strongly

expressed in the mammary gland but moderately expressed in the milk sample. PIT-1 is also

moderately expressed in the milk sample. The clustergram in Fig.4 shows the relative normalized

expression and the hierarchical relationship of expressed genes across the samples. A closer

relatedness was observed in the expression of K-CN and PRL than Blg among the bovine samples

in this study, while PIT-1 gene is distantly related in expression as compared with other genes.

Based on these expression, mammary gland tissue and milk sample were closely related in their

gene expression than the brain and the liver.

Comparative analysis of bovine milk transcriptome in indigenous and foreign cattle breeds

In the further analysis, the expression profile of Blg, K-CN, PIT-1, and PRL genes were compared

among cattle strains using milk samples from two representative animals from hot climatic region

(N’dama and White Fulani) vs. animals from cold climatic region (Angus and Holstein). Overall,

the range of mRNA expression form the quantitative PCR method in this study was very broad

(Table 2). Among the four genes, PRL had the highest mRNA expression (144.19-fold change) in

Holstein milk sample followed by K-CN with 100.89-fold change while the smallest relative

normalize expression for most genes in this study were in the range of 0.79 to 7.35-fold difference.

From Table 2 and Figure 5, significantly lower expression of the four genes (Blg, K-CN, PIT-1

and PRL) as observed in Angus and N’dama cattle. Among the four breeds, N’dama cattle showed

the least expression of the Blg gene with relative normalized expression value of 0.79-fold while

Angus showed the least expression for K-CN gene with a value of 2.73-fold. Likewise, Table 2

and Figure 6 showed the gene regulation of the bovine milk expression in the four breeds in this

study. The expression regulation analysis revealed that PRL gene is significantly (p<0.001) down-

regulated in N’dama and White Fulani with a value of -7.31 and -5.75 respectively while K-CN

gene is significantly down regulated in Angus cattle with a regulation value of -4.49. However, K-

CN and PRL genes are significantly up regulated in Holstein cattle with a regulation value of

139.25 and 108.37 followed by PIT-1 and Blg genes with a regulation value of 54.78 and 38.06

accordingly. Also, PIT-1 and K-CN genes are significantly up regulated in White Fulani cattle with

a regulation value of 25.95 and 21.04 respectively. Holstein cattle is on top of the list based on the

gene expression for all the four genes in this study followed by the white Fulani cattle only that

the PRL gene is observed to be down regulated, while the genes were lowly expressed or down

regulated in Angus and N’dama cattle breeds (Table 2 and Figure 5). The extreme variation in

mRNA expression in this study strongly suggests that there is heterogeneity in cattle adaptation

among breeds across populations in different geographical location and that gene expression

profiles varied in animal from one region to another.

Furthermore, the comparison of milk gene expression in four breeds of animals in this study

revealed a significantly (p < 0.001) higher expression in Bos taurus animals with K-CN gene on

top of the list of expression while lesser milk gene expression was found in Bos indicus regardless

of the climatic region they come from. Also, the milk gene expression profile in animals from the

two climatic regions (2 from temperate and 2 from tropical climate regions) showed that the

animals from the temperate region had a higher milk gene expression when compared to their

tropical counterparts.

Figure 7 shows clustergram and the heat map of the mRNA expression of four genes using

hierarchical cluster of log base 2 form of the relative normalized expression of BLG, PRL, K-CN,

and PIT-1 genes in bovine milk transcriptome. The relative normalized expression falls within the

range of -7.17 and 7.17 N-fold change for all the genes. From the clustergram, it is observed that

a closer relatedness exists in the expression of K-CN and PIT-1 genes while BLG and PRL genes

are also closely related in their expression. K-CN and BLG genes are distantly related in their

expression as compared with PRL and PIT-1 genes. Based on these milk gene transcriptome,

Holstein, White Fulani and N’dama cattle breeds were closely related in their gene expression than

Angus cattle although Holstein and White Fulani cattle were more related in their milk mRNA

expression as compared to the other breeds. The heat map showed that the four genes in this study

(BLG, PRL, K-CN, and PIT-1) are strongly expressed and over represented in Holstein breed while

K-CN and PIT-1 genes were the only two genes strongly expressed in White Fulani breed. The

genes were lowly expressed and showed no change in Angus and N’dama cattle.

Discussion

The main interest of this study focused on examining differentially expressed milk genes in bovine

tissues and milk transcriptome at the peak of lactation stage; to have a deeper insight of the

comparative expression of genes from different tissue samples and comparative difference in milk

gene expression profile among four breeds of cattle from two different climatic regional scenarios.

The mRNA expression profiled for the four genes in this study showed a wide range from 0.05-

fold to 100-fold difference and above. Our results reassured that the differential expression

profiles for these four genes are consistent with the previous quantitative measurement of milk

traits in cattle experiment (Ahmadi et al. 2008, Medrano et al. (2010), Jimenez-Montero et al.

2011, Wickramasinghe et al. 2012 and Yang et al. 2015).

We selected GAPDH and ACTINB as the internal control genes for this study because of the report

on their stability from other authors. It is very important to select a right housekeeping gene for

internal control during qPCR for gene expression studies. Likewise, many authors have shown that

it is a good practice to use more than one housekeeping genes when studying gene expression for

normalization in the analysis (Barber et al. 2005, Banda et al. 2007, Robert et al. 2010 and Forni

et al. 2011). The extreme variation in mRNA expression in this study strongly suggests that there

is heterogeneity in cattle adaptation among breeds across populations in different geographical

location and that genes are differentially expressed from one tissue to another in the same animal

in the same lactation stage.

Analysis of bovine milk gene transcriptome across different tissue and milk samples

The transcriptome profiles of BLG, PRL, and K-CN were strongly expressed in the mammary gland

tissue except for the PIT-1 which is lowly expressed suggesting and strengthening the role that

mammary gland plays in the milk secretion in animal’s body. The biological actions of prolactin

in mammary gland for example have been characterized extensively, where it has an essential role

both in the differentiation of the gland during pregnancy and in the regulation of milk protein gene

expression (Bayat and Houdebine 1993, Sheehy et al. 2004, Cui et al. 2014). The report of Hou et

al. (2009) and Medrano et al. (2010) corroborate the high expression of milk genes from the

mammary gland at the peak lactation stage of cows.

Several studies have established composite responses which contain multiple sites for many

transcription factors that mediate the developmental and hormonal regulation of milk gene

expression in mammary epithelial cells (Veerkamp et al. 2003 and Kuljeet et al. 2010). Rosen et

al. (2000) reported that signal transduction pathways was activated by lactogenic hormones and

cell-substratum interactions which activate transcription factors and change chromatin structure

and milk protein gene expression.

Following the mammary gland, the mRNA expression of the four genes were likewise highly

expressed in milk samples in this study buttressed the closer relatedness in the mRNA expression

profile between mammary gland and the milk sample. This is expected since the mammary gland

is the factory for the milk production. Previous studies of Medrano et al. (2010) and

Wickramasinghe et al. (2012) identified similarities in gene expression between the mammary

gland and the milk somatic cells. Bioinformatics predictions of genes encoding secreted proteins

up-regulated during lactation in the mammary gland suggest that as many as 300 different proteins

may be found in milk (Abby et al. 2009, Kuljeet et al. 2010 and Cánovas et al. 2010). However,

caseins are the major proteins in milk, representing about 80% of total milk protein content

justifying the higher expression of K-CN in the milk samples in this study. This also corroborate

the studies of Medrano et al (2010) and Wickramasinghe et al. (2012) who observed a higher

expression of K-CN in both milk and mammary gland at the 90 days of lactation.

The significantly lower expression of the four genes in the liver suggests that the organ plays a

lesser role or low participation in the whole process of milk production and that milk gene

expression profile are only localized and strongly expressed in the associated organs for milk

production. On the other hand, PIT-1 gene which was highly expressed in the brain confirms the

role of pituitary hormonal secretion and regulation of milk gene expression in the body. PIT-1 is

the critical cell specific transcription factor for the activity expression of prolactin (PRL) and

growth hormone genes (Bona et al. 2004 and Hodne et al. 2010), thyroid-stimulation hormone b-

subunit (TSH-b) (Steinfelder et al. 1991), growth hormone releasing hormone receptor (GHRHR)

genes (Lin et al. 1994). Thus it has been selected and used as candidate genetic marker for growth,

carcass and milk yield traits in many studies (Cohen et al. 1997, Ursula and Ken, 2016). PIT-1 is

responsible for pituitary development and hormone expression in mammals (Laurie et al. 1996

Hodne et al. 2010). Also, it controls transcription of prolactin (PRL), growth hormone (GH),

(Mehmannavaz et al. 2009), and PIT-1 gene itself (Rhodes et al. 1993). PIT-1 polymorphism has

been reported to be associated with milk yield and conformation traits in cattle (Riaz et al. 2008,

Medrano et al. 2010 and Ankur et al. 2015). Our study has confirmed the importance of PIT-1

gene in regulation of milk protein gene expression and has provided clue into their role in

mammary gland development and differentiation. Additionally, the significant higher expression

of PRL gene from the mammary and milk samples in this study pointed to the fact that it is

influenced by the PIT-1 gene which is also highly expressed in the brain confirming its association

in the regulation and expression of these genes. This corroborates the report of Laurie et al. (1996)

and Kuljeet et al. (2010) that PIT-1 gene affects the expression pattern of prolactin.

Comparative analysis of milk genes transcriptome in milk samples of four breeds

The four genes (BLG, PRL, K-CN and PIT-1) were quantitated and identified to be most

significantly up regulated in the milk sample of Holstein cattle from the qPCR analysis. This

buttresses the fact that Holstein cattle are known to be high yielding milk animal as compared with

other breeds. Holstein cattle have been reported to have a high expression of these genes according

to the studies of Cui et al. (2014) and Yang et al (2015). Also, a significantly up regulated mRNA

expression of milk genes was observed in the White Fulani animal as compared to Angus and

N’dama animals. The up regulation of the milk genes identified White Fulani cattle to be a

potential milk producing animal from the tropical region and can be selected for improvement

through selection and breeding programme in the future. Furthermore, the milk gene transcriptome

profile as observed between Holstein and White Fulani cattle in this study justified the closer

relatedness observed in the clustering analysis though they are of different breeds and from

different regional climate but their milk gene expression patterns are similar. Also, variation in

milk gene expression of these two breeds suggest that environment could contribute to the gene

expression pattern in these two animals. Temperate climate adapted Holstein showed a

significantly higher expression than White Fulani animal being from the tropical climate. This

suggests the fact that the expression of these genes is associated with the influence of climatic

difference and adaptation of these animals in the two different regional scenarios in this study. The

high gene expression profile observed in Holstein cattle also buttress the reason why the animal is

well adapted to the colder climate and thrive well in this region compared with other breeds. This

in agreement with Rhoads et al. (2009) and Shwartz et al. (2009) that when Chinese Holstein cattle

are exposed to high ambient temperature, especially high humidity, thermoregulatory processes

were impaired and cattle cannot dissipate an adequate quantity of heat to maintain body thermal

balance or normal body temperature. Thus, their milk production and reproduction rate will sharply

decrease. On the contrary, White Fulani from the hot climatic region has a good potential for

draught and well adapted to high temperature is identified in this study for higher milk gene

expression. This potential can be improved on for a better milk production as the animal can thrive

well in the tropics.

Altogether, the results from this study indicated that the differential expression profile of milk

genes in different samples and among different breed across 2 climatic regional scenarios are

significantly different. This pointed to the fact that there are genetic differences in the gene

expression profiles at the physiological and cellular levels which have been documented in several

studies on Bos indicus and Bos taurus (Paula-Lopes et al. 2003, Hansen 2004, Lacetera et al. 2006,

Wickramasinghe et al. 2012 and Yang et al. 2015). Those studies confirm a genetic linkage

between species, breed, and individual differences to gene expression profile at the cellular level.

Interestingly, Angus and N’dama cattle from different climatic region and different breeds had a

similar significantly lower mRNA expression profile as compared to Holstein and White Fulani.

Therefore, there is a closer relatedness in the hierarchical clustering analysis between these two

breeds. These justified the reason why these two breeds are regarded as beef animal rather than

milk producing animals.

Conclusion

The gene expression profile of four milk genes using quantitative PCR showed that the genes were

differential expressed from one tissue to another in the same animal. The extreme variation in

mRNA expression in the milk among the animal breeds strongly suggests that there is

heterogeneity in cattle adaptation across populations in different geographical location.

Additionally, mammary gland strongly expressed three out of four milk genes (BLG, PRL and K-

CN) considered in this study suggesting and strengthening the role that mammary gland play in

the milk secretion in animal’s body. Also, higher expression of PRL and K-CN in both mammary

and milk samples is associated to the high expression of PIT 1 gene in the brain. Apart from

Holstein cattle, the significant up regulated expression of the milk genes in White Fulani animal

gave the first evidence of molecular identification of its potential for future selection and breeding

for a better milk producing animal especially in Nigeria that can cope with the changing climate

due to the current trend of global warming. Our results provide an insight into the potential and

identify key genes that contribute to improved milk production which could be through genomic

assisted breeding programmes that will lead to improving the economic and environmentally

suitable dairy production.

References

Abby T., Mike B. and Harjinder S. 2009 Milk Proteins: from expression to food. American

Journal of Human Biology 21, 852-860.

Ahmadi M., Mohammadi Y., Darmani-kuhi H., Osfoori R. and Qanbari S. 2008 Association of

milk protein genotypes with production traits and somatic cell count of Holstein cows.

Journal of Biological Science 8(7), 1231-1235.

Ankur C., Madhu T., Satyendra P.S., Deepak S., Sumit K., Rakesh G., Amitav B. and Vijay S.

2015 PIT-1 gene polymorphism with milk production traits in Sahiwal cattle. The Indian

Journal of Animal Sciences 85(6), 610-612.

Baik M., Etchebarne B.E., Bong J. and Vander Haar M.J. 2009 Gene expression profiling of

liver and mammary tissues of lactating dairy cows. Asian-Aust. J. Anim. Sci. 22(6), 871-884.

Banda M., Bommineni A., Thomas R.A., Luckinbill L.S. and Tucker J.D. 2007 Evaluation and

validation of house-keeping genes in response to ionizing radiation and chemical exposure

for normalizing RNA expression in real-time PCR. Mutat Res/Genet Toxicol. Environ.

Mutagen 649, 126-134.

Barber R.D., Harmer D.W., Coleman R.A. and Clark B.J. 2005 GAPDH as a housekeeping

gene: analysis of GAPDH mRNA expression in a panel of 72 human tissues. Physiological

Genomics 21, 389-395.

Bayat S.M. and Houdebine L.M. 1993 Effect of various protein kinase inhibitors o the

induction of milk protein gene expression by prolactin. Mol. Cell Endocrinol. 92(1), 127-

134.

Cane K. N., Arnould J. P. and Nicholas K. R. 2005 Characterisation of proteins in the milk of

fur seals. Comparative Biochemistry and Physiology—Part B: Biochemistry and

Molecular Biology 141, 111 – 20.

Cánovas A., Rincon G., Islas-Trejo A., Wickramasinghe S. and Medrano J.F. 2010 SNP

discovery in the bovine milk transcriptome using RNA-Seq technology. Mamm

Genome, 21(11-12):592–598.

Cohen L.E., Wondisford F.E. and Radovick S. 1997 Role of Pit-1 in the gene expression of

growth hormone, prolactin and thyrotropin. Endocrinol. Metab. Clin. North Am. 25, 523-

540.

Cole J. B., vanRaden P. M., O’Conell J. R., van Tassell C. P. and Sonstergard T. S. 2009

Distribution and location of genetic effects for dairy traits. Journal of Dairy Science 92,

2931–2946.

Cui X., Hou Y., Yang S., Xie Y., Zhang S., Zhang Y., Zhang Q., Lu X., Liu G.E. and Sun D.

2014 Transcriptional profiling of mammary gland in Holstein cows with extremely

different milk protein and fat percentage using RNA sequencing. BMC Genomics, 15:226-

241.

Daetwyler H.D., Pong-Wong R., Villanueva B. and Woolliams J.A. 2010 The impact of

genetic architecture on genome-wide evaluation methods. Genetics 185, 1021-1031.

Farrell H. M. Jr., Jimenez-Flores R., Bleck G. T., Brown E. M., Butler J. E., Creamer L. K.,

Hicks C. L., Hollar C. M., Ng-Kwai-Hang K. F. and Swaisgood H. E. 2004 Nomenclature

of the proteins of cows’ milk sixth revision. Journal of Dairy Science 87, 1641 – 1674.

Forni S., Aguilar I. and Misztal I. 2011 Different genomic relationship matrices for single-step

analysis using phenotypic, pedigree and genomic information. Genet. Sel. Evol. 43, 1-7.

Fu J., Wolfs M.G., Deelen P., Westra H.J., Fehrmann R.S., Te Meerman G.J., Buurman W.A.,

Rensen S.S., Groen H.J., Weersma R.K., Van den Berg L.H., Veldink J., Ophoff R.A.,

Snieder H., van Heel D., Jansen R.C., Hofker M.H., Wijmenga C. and Franke L.

2012 Unraveling the regulatory mechanisms under l Robert tissue-dependent genetic

variation of gene expression. PLoS Genet. 8 e1002431.

Girard C. and Matte J. 2005. Folic acid and vitamin B12 requirements of dairy cows: A

concept to be revised. Livestock Production Science 98, 123 – 133.

Graulet B., Matte J. J., Desrochers A., Doepel L., Palin M. F. and Girard C. L. 2007 Effects of

dietary supplements of folic acid and vitamin B12 on metabolism of dairy cows in early

lactation. Journal of Dairy Science 90 (7), 3442 – 3455.

Hansen P. J. 2004 Physiological and cellular adaptations of zebu cattle to thermal stress.

Animal Reproduction Science 82, 349–360.

Hayes B. J., Pryce J., Chamberlain A. J., Bowman P. J. and Goddard M. E. 2010 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 Genetics 61,100

-139.

Hodne, K., Haug, T.M. and Weltzien, F.A. (2010) Single-cell qPCR on dispersed primary

pituitary cells—an optimized protocol. BMC Mol. Biol. 11: 82

Hou M., Li, Q. and Huang T. 2009 Microarray analysis of gene expression profiles in the

bovine mammary gland during lactation. Science China Life Science 53(2), 248-256.

Jiménez-Montero J.A., González-Recio O. and Alenda R. 2011 Genotyping strategies for

genomic selection in small dairy cattle populations. Animal 6, 1216–1224.

Kuljeet S., Richards A.E., Kara M.S. and Kerst S. 2010 Egipenetic Regulation of Milk

production in Dairy Cows. Journal of Mammary Gland Biology and Neoplasia,

15(1):101-112.

Lacetera N., Bernabucci U., Scalia D., Basiricò L., Morera P. and Nardone, A. 2006 Heat

stress elicits different responses in peripheral blood mononuclear cells from Brown Swiss

and Holstein cows. Journal of Dairy Science 89, 4606–4612.

Laurie E.C., Fredric E.W. and Sally R. 1996 Role of PIT-1 in the gene expression of growth

hormone, prolactin and thyrotropin. Endocrinology and Metabolism Clinics of North

America, 25(3):523-540.

Lin C., Lin S.C., Chang C.P. and Rosenfeld M.G. 1994 Pit-1 dependent expression of the

receptor for growth hormone releasing factor mediates pituitary cell growth. Nature 360,

765-768.

Liu, W., Lauladerkind, S.J.F., Hayman, G.T., Wang, S., Nigam, R., Smith, J.R., De Pons, J.,

Dwinell, M.R. and Shimoyama, M. 2015. Onto Mate: a test-mining tool aiding curation

at the rat genome database. Database, 1-8. Doi:10.1093/database/bau/129.

Marchini J. and Howie B. 2010 Genotype imputation for genome-wide association studies.

Nat. Rev. Genet. 11, 499-511.

Medrano J.F., Rincon G. and Islas-Trejo A. 2010 Comparative analysis of bovine milk and

mammary gland transcriptome using RNA-seq. In 9th World congress on genetics applied

to livestock production. Leipzig, German, August 1–6, paper no. 0852.

Mehmannavaz Y., Amirinia C., Bonyadi M. and Torshizi R.V. 2009 Effects of bovine

prolactin gene polymorphism within exon 4 on milk related traits and genetic trends in

Iranian Holstein bulls. African Journal of Biotechnology 8(19), 4797-4801.

Paula-Lopes F. F., Chase C.C. Jr., Al-katanani Y.M., Kriinger C.E., Rivera R.M., Tekin S.,

Majewski A.C., Ocon O.M., Olson T.A. and Hansen P. J. 2003 Genetic divergence in

cellular resistance to heat shock in cattle: differences between breeds developed in

temperate versus hot climates in responses of preimplantation embryos, reproductive tract

tissues and lymphocytes to increased culture temperatures. Reproduction 125, 285–294.

Raiz M.N., Malik N.A., Nasreen F. and Qureshi J.A. 2008 Molecular marker assisted study of

kappa-casein gene in Nili-Ravi (Buffalo) breed of Pakistan. Pakistan Vet. J. 28(3), 103-

106.

Ran L., Pier L.D. and Eveline M.I. 2016 Comparative Analysis of the miRNome of bovine

milk fat, whey and cells. PLoS ONE 11(4), 1-12.

Rhoads M. L., Rhoads R. P., VanBaale M. J., Collier R. J., Sanders S. R., Weber W. J., Crooker

B. A. and Baumgard L. H. 2009 Effects of heat stress and plane of nutrition on

lactating Holstein cows: I. production, metabolism and aspects of circulating

somatotropin. Journal of Dairy Science 92, 1986–1997.

Rhodes S.J., Chen R., DiMattia, G.E., Scully K.M., Kalla K.A., Lin S.C., Yu V.C. and

Rosenfeld M.G. 1993 A tissue-specific enhancer confers Pit-1 dependent morphogen

inducibility and autoregulation on the Pit-1 gene. Gene Development 7, 913-932.

Robert R. K., Mikael K. and Ales T. 2010 Statistical aspects of quantitative real-time PCR

experiment design. Elsevier Methods 50, 231-236.

Rosen R., Brown C., Heiman J., Leiblum S., Meston C.M., Shabsigh R., Ferguson D. and

D’Agostino R. 2000. The Female Sexual Function Index (FSFI): A multidimensional

self-report instrument for the assessment of female sexual function. Journal of Sex and

Marital Therapy 26, 191–208.

Sheehy P.A., Della-Vedova J.J., Nicholas K.R. and Wynn P.C. 2004 Hormone dependent milk

protein gene expression in bovine mammary explants from biopsies at different stage of

pregnancy. J. Dairy Res. 71(2): 135-140.

Shwartz G., Rhoads M. L., VanBaale M. J., Rhoads R. P. and Baumgard L. H. 2009 Effects of

a supplemental yeast culture on heat-stressed lactating Holstein cows. Journal of

Dairy Science 92, 935–942.

Smolenski G., Haines S., Kwan F. Y., Bond J., Farr V., Davis S. R., Stelwagen K. and

Wheeler T. T. 2007 Characterization of host defense proteins in milk using a proteomic

approach. Journal of Proteome Research 6, 207 – 215.

Steinfelder H.J., Hauser P., Nakayama Y., Radovick S., McClaskey J. H., Taylor T., Weintraub

B.D. and Wondisford F.E. 1991 Thyrotropin-releasing hormone regulation of

human TSHß expression: role of a pituitary specific transcription factor (Pit-1/GHF-1)

and potential interaction with a thyroid hormone-inhibitory element. Proceedings of

National Academy of Science USA 88, 3130-3134.

Ursula K. and Ken K.Y. 2016 Pituitary physiology and diagnostic evaluation. Williams

Textbook of Endocrinology (Thirteenth Edition), pp. 176-231.

Veerkamp R.F., Beerda B. and Vander L.T. 2003 Effects of genetic selection for milk yield on

energy balance, levels of hormones, and metabolites in lactating cattle, and possible links

to reduced fertility. Livestock Prod. Sci. 83, 257-275.

Wellmann R. and Bennewitz J. 2011 The contribution of dominance to the understanding of

quantitative genetic variation. Genetic Research 93, 139–154.

Whelehan, C.J., Reidy, A.B., Meade, K.G., Eckersall, P.D., Chapwanya, A., Narcianda, F.,

Lloyd, A.T. and O’Faralley, C. 2014. Characterization and expression profile of bovine

cathelicidin gene receptoire in mammary tissue. BMC Genomics 15,128-140.

Wickramasinghe S., Hua S., Rincon G., Islas-Trejo A., German J.B., Lebrilla C.B. and

Medrano J.F. 2011 Transcriptome Profiling of bovine milk oligosaccharide metabolism

genes using RNA-Sequencing. PloS One, 6(4):e18895.

Wickramasinghe S., Rincon G., Islas-Trejo A., Medrano J.F. 2012 Transcriptional profiling

of bovine milk using RNA sequencing. BMC Genomics, 13:45.

Yang J., Jiang J., Liu X., Wang H., Guo G., Zhang Q. and Jiang L. 2015 Differential

expression of genes in milk of dairy cattle during lactation. Animal Genetics, 47:174-180.

Received 6 October 2017; revised 11 October 2018; accepted 16 October 2018

Table 1 Oligonucleotide primer sequences used for qPCR and the amplification efficiencies

Gene

symbol

5’-3’ sequence Annealing

Temperature

qPCR

amplicon

size (bp)

GC

(%)

qPCR reaction

efficiencies (%)

PRL F: ATAGGACGAGAGCTTCCTGGT

R: TTCTGCGACGAACCTTTGCT

60.89

60.82

76 55.00

52.38

94

BLg F: ATAGGACGAGAGCTTCCTGGT

R: TTCTGCGACGAACCTTTGCT

60.00 95 55.00 100

KCa F: TGGAAAGGCCAACTGAACCT

R: CCTTGTGACCGTCAGCTCTT

59.44

59.97

52 60.00

52.38

92

PIT-1 F: AAACCATCATCTCCCTTCTT

R: AATGTACAATGTGCCTTCTGA

59.19

59.75

97 63.16

55.00

100

GAPDH F: ACCACTTTGGCATCGTGGAG

R: GGGCCATCCACAGTCTTCTG

63.00 85 55.00

60.00

92

ACTINB F: GAGCGGGAAATCGTCCGTGAC

R:GTGTTGGCGTAGAGGTCCTTGC

60.00 92 62.00

59.00

94

Table 2: mRNA expression of four genes in bovine milk across two regional representative breeds

SAMPLE Target Mean Cq Normalized

Expression

Relative

Normalized

Expression

Regulation Compared to

Regulation

Threshold

P-Value Exceeds P-

Value

Threshold

ANGUS BLG 28.07 3.32 1.3 2.85 No change 0.00018 No

K-CN 30.83 8.07 2.73 -4.49 Downregulated 0.24835 Yes

PIT-1 32.62 0.14 6.34 2.55 No change 0.00061 No

PRL 28.38 2.68 7.2 1.9 No change 0.00032 No

HOLSTEIN BLG 20.14 126.4 38.06 38.06 Up regulated 0.00010 No

K-CN 16.29 27.47 100.89 139.25 Up regulated 0.00006 No

PIT-1 27.16 240.23 38.71 54.78 Up regulated 0.00001 No

PRL 16.87 288.96 144.19 108.37 Up regulated 0.00059 No

NDAMA BLG 32.42 0.02 0.79 2.92 No change 0.00011 No

K-CN 30.11 0.45 3.12 2.58 No change 0.00021 No

PIT-1 31.91 1.04 7.35 8.35 No change 0.46225 Yes

PRL 29.21 0.02 4.02 -7.31 Downregulated 0.39834 Yes

WHITEF. BLG 23.92 68.89 20.74 7.74 Up regulated 0.00007 No

K-CN 27.16 4.01 21.04 21.04 Up regulated 0.00012 No

PIT-1 28.47 61.8 25.95 25.95 Up regulated 0.00001 No

PRL 20.29 146.91 35.75 -5.75 Down regulated 0.00005 No

Exceeds P-Value Threshold: Yes, indicates not statistically significant gene expression difference at P < 0.01 level

No indicates statistically significant gene expression difference at P < 0.01 level

Mean Cq: Average or mean quantification cycle i.e. cycle at which quantification was first detected.

Fig.1 Verification of amplification specificity by single-peak melt peak of the qPCR products

Fig. 2 Verification of amplification by melt curves of the qPCR products. Melt curve analysis of the amplicons

shows a single, sharp melt curve per gene. Cq, quantification cycle; RFU, relative fluorescence units.

Fig.3 The relative normalized expression of BLG, PRL, K-CN and PIT-1 in the bovine brain, liver, milk

and mammary gland samples.

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

160.00

Brain Liver Milk Mammary gland

Rel

ativ

e N

orm

aliz

ed E

xpre

ssio

n

Bovine milk gene expression profile in different samples

BLG PRL K-CN PIT-1

Fig. 4 The heat map of mRNA expression of four milk genes in four bovine samples (Brain, Liver,

Mammary gland and Milk) using hierarchical cluster. Genes with increased expression are shown in red;

while those with decreased mRNA expression are in shown in green. The grid segment color indicates up

regulation (red), down regulation (green), or no change in regulation (black). The lighter the color, the

greater the degree of regulation. The relative normalized expression fall within the range of -6.79 and 6.79

N-fold change for all the genes.

Fig. 5 The expression profile of BLG, PRL, K-CN, and PIT-1 genes in bovine milk.

Fig. 6 The gene regulation pattern of BLG, PRL, K-CN, and PIT-1 genes in bovine milk.

Fig.7 Heat map showing the mRNA expression of four genes in four breeds using hierarchical cluster.

Genes with increased expression are shown in red; while those with decreased mRNA expression are in

shown in green. The grid segment color indicates up regulation (red), down regulation (green), or no change

-20

0

20

40

60

80

100

120

ANGUS N'DAMA WHITEF HOLSTEIN

Reg

ula

tio

n

Bovine Milk Gene Regulation

BLG PRL K-CN PIT-1

in regulation (black). The lighter the color, the greater the degree of regulation. The relative normalized

expression fall within the range of -7.17 and 7.17 N-fold change for all the genes.