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Note that a Preprint of this article also exists, first published February 18, 2016.


Reverse transcription quantitative PCR (RT-qPCR) is considered the ideal method in gene expression studies because of its high sensitivity, time efficiency, and reliability (Derveaux, Vandesompele & Hellemans, 2010; Pfister, Tatabiga & Roser, 2011). In gene expression analysis using RT-qPCR, different starting amounts of messenger RNA between samples and different efficiencies of reverse transcriptases and polymerases can be adjusted by relative quantification, which uses a reference gene (often the housekeeping gene, HKG) as an internal control to calculate target gene (e.g., cytokine gene) expression levels. HKG is required for the maintenance of basic cellular function, and is expressed in all types of cells (Pfaffl, 2004), and its expression level is described as stable. However, Brinkhof et al. (2006) reported that, in dogs, the most stable control genes were ribosomal protein S5 in the liver, kidney, and mammary glands, beta 2-microglobulin (B2M) in the left ventricle, and ribosomal protein L8 (RPL8) in the prostate, indicating each tissue type has its specific stably-expressed HKG even within the same species. Vorachek, Bobe & Hall (2013) and Vorachek et al. (2013) reported that for neutrophils, the most stable gene was glucose-6-phosphate dehydrogenase (G6PD) in sheep, while in bovine calves, the most stable genes were phosphoglycerate kinase I (PGK1) and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta (YWHAZ); however, G6PD was ranked fifth in 10 genes tested. It has been suggested that using an inappropriate reference gene could lead to incorrect normalized data, leading to misinterpretation of the results (Dheda et al., 2005). Therefore, selecting a suitable reference gene is needed when studying a new species or tissue type.

Cytokine gene expression research has been conducted in both free-ranging and human-cared cetaceans. Studying the correlation between cytokine gene expression and pollutants in free-ranging cetaceans can make these mammals useful sentinels for indicating the environmental status (Beineke et al., 2007; Buckman et al., 2011). Cytokine gene expression analysis has also been used as a diagnostic tool in analyzing immune status and stress induced by capture–release assessment in dolphins (Mancia, Warr & Chapman, 2008). Moreover, it has been used to evaluate the effectiveness of vaccine treatment and implicate the best duration for vaccination in human-cared cetaceans (Sitt et al., 2010). Most of the cetaceans in human care facilities have been trained to undergo voluntary blood collection, and the examination frequency can be increased when intensive monitoring is needed. The quantitative analysis of cytokine gene expression in cetacean blood could offer information, in addition to regular blood examination, for estimating the immune status of the animal and facilitating the medical treatment and health management. The most important first step to obtain an accurate assessment of cytokine gene expression in cetacean blood samples is determining the most stably expressed HKG as the reference gene. The purpose of this study is to select the reference gene in blood samples from beluga whales (Delphinapterus leucas), which are one of the most commonly found cetacean species in human care. It would provide fundamental and practical information for the quantitative analysis of cytokine gene expression and contribute to preventive medicine and early diagnosis in human-cared cetaceans.

Materials and Methods

Sample collection and preservation

The voluntary blood collection of beluga was performed in accordance with international guidelines, and the protocol has been reviewed and approved by Council of Agriculture of Taiwan (Approval number 1020727724). Sixty blood samples from four beluga whales (15 from each one) in National Museum of Marine Biology and Aquarium in Taiwan were taken monthly routine or occasionally assessment from 2011 to 2013. It has been suggested to include samples in different experimental groups or different conditions for reference gene selection (Dheda et al., 2005). Samples were from beluga whales with various body conditions including clinically healthy condition (30 samples from four animals), inflammation (six samples from four animals), skin lesions (nine samples from two animals), and internal diseases with various abnormalities in blood work and cytology (15 samples from three animals). Five hundred microliter EDTA-anticoagulated whole blood was fixed in 1.3 mL RNAlater® (Ambion, Foster City, CA, USA) within 5 min after drawn. Samples were stored at 4 °C in the first 24 h, and then moved to −20 °C for long-term storage.

RNA extraction and cDNA synthesis

Total RNA of the samples was extracted using Ribo-Pure™ -Blood kit reagent (Ambion) according to the manufacturer’s instructions. RNA Armor™ Reagent (ProTech, Pittsburgh, PA, USA) was added into RNA solution to eliminate contaminated RNase. RNA concentration was determined using Qubit™ fluorometer with Quant-iT™ RNA Assay Kit (Invitrogen, Carlsbad, CA, USA). RNA quantity of all samples was adjusted into 100 ng to keep all the samples on the same starting basis. RNA was treated with genomic DNA (gDNA) wipeout solution (Qiagenen, Valencia, CA, USA) before added into reverse transcription working solution. RNA samples after gDNA elimination were tested using qPCR directly to ensure no residue gDNA, which would interfere the analysis of mRNA expression. QuantiTect® Reverse Transcription kit (Qiagen), provided blend of oligo-dT and random primers, was used for cDNA synthesis. Complementary DNA and the remaining extracted RNA were put into −80 °C for long-term storage.

Table 1:
Function, symbol and name of HKGs in this study.
Function Gene Name
Carbohydrate metabolism GAPDH Glyceraldehyde-3-phosphate dehydrogenase
PGK1 Phosphoglycerate kinase 1
LDHB Lactate dehydrogenase B
Ribosomal protein RPS9 Ribosomal protein S9
RPL4 Ribosomal protein L4
RPL8 Ribosomal protein L8
RPL18 Ribosomal protein L18
RPS18 Ribosomal protein S18
MHC B2M β-2-microglobin
Transporter TFRC Transferrin receptor
Cytoskeleton ACTB β-actin
Signal YWHAZ Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta
Others HPRT1 Hypoxantine phosphoribosyltransferase 1
DOI: 10.7717/peerj.1810/table-1
Table 2:
Name, accession number, primer sequence, probe number, amplicon size, efficiency and R2 of 13 candidate HKGs.
HKG name Accession number Primer Sequence (5′ − 3′) UPL Probe Number Amplicon Size (bp) Threshold Efficiency (%) ± SD R2
ACTB AB603937.1 F-AGGACCTCTATGCCAACACG 157 75 0.02 97.69 ± 1.15 0.999
B2M DQ404542.1 F-GGTGGAGCAATCAGACCTGT 93 78 0.035 95.81 ± 0.61 0.999
GAPDH DQ404538.1 F-CACCTCAAGATCGTCAGCAA 119 81 0.02 97.03 ± 1.32 1.000
HPRT1 DQ533610.1 F-GTGGCCCTCTGTGTGCTC 120 81 0.012 98.17 ± 1.44 0.999
LDHB AB477024.1 F-TCGGGGGTTAACCAGTGTT 161 78 0.005 100.49 ± 1.58 0.995
PGK1 DQ533611.1 F-CACTGTGGCCTCTGGCATA 108 84 0.015 95.47 ± 0.31 0.999
RPL4 DQ404536.1 F-CAGCGCTGGTCATGTCTAAA 119 108 0.035 96.91 ± 0.98 0.999
RPL8 GQ141092.1 F-CCATGAATCCTGTGGAGCAT 131 65 0.02 101.39 ± 2.47 0.997
RPL18 DQ403041.1 F-GCAAGATCCTCACCTTCGAC 93 104 0.02 96.55 ± 0.39 1.000
RPS9 EU638307.1 F-CTGACGCTGGATGAGAAAGAC 155 77 0.02 98.96 ± 1.39 0.999
RPS18 DQ404537 F-GTACGAGGCCAGCACACC 114 90 0.02 98.46 ± 1.23 0.999
TFRC DQ533608.1 F-TCCTTTCCGACATATCTTCTGG 106 73 0.02 97.79 ± 2.49 0.996
YWHAZ DQ404539 F-TCTCTTGCAAAAACGGCATT 135 76 0.003 98.35 ± 0.66 0.992
DOI: 10.7717/peerj.1810/table-2

Primer and probe design

Sequences of the 13 candidate cetacean HKGs (ACTB, B2M, GAPDH, HPRT1, LDHB, PGK1, RPL4, RPL8, RPL18, RPS9, RPS18, TFRC, YWHAZ) were obtained from bottlenose dolphin, striped dolphin, beluga whale, killer whale and fin whale (Balaenoptera physalus) from GenBank (Tables 1 and 2). Besides 11 HKGs have been evaluated or used in previous studies (Beineke et al., 2004; Beineke et al., 2007; Buckman et al., 2011; Mancia, Warr & Chapman, 2008; Martinez-Levasseur et al., 2013; Müller et al., 2013; Sitt et al., 2008; Sitt et al., 2010; Spinsanti et al., 2006; Spinsanti et al., 2008), the other 2 genes that could participate in other different cell functions were also included (Echigoya et al., 2009; Kullberg et al., 2006). Primers and corresponding UPL probes were designed and chosen using Roche UPL design software (ProbeFinder, v.2.49) based on Primer3 software (Table 2). All designed primer pairs were checked by in silico PCR algorithm in ProbeFinder, which searches the relevant genome and transcriptome for possible mis-priming sites for either of the PCR primers. Before qPCR experiment, the specificity of primers of 13 candidate genes was confirmed using Fast-Run Hotstart PCR kit (Protech) and electrophoresis.

Quantitative PCR

Quantitative PCR was conducted on 48-well reaction plates using Eco Real-Time PCR System (Illumina, San Diego, CA, USA). Reactions were prepared in a total volume of 10 µl containing 3 µl 12-fold-diluted cDNA, 0.4 µl of each 10 µM primer, 0.2 µl of UPL probe (Roche), 5 µl FastStart Essential DNA Probes Master (Roche, Risch-Rotkreuz, Switzerland) and 1 µl of RNase/DNase-free sterile water (ProTech). The thermocycling conditions were set as follows: polymerase activation at 95 °C for 10 min, followed by 45 cycles of denaturation at 95 °C for 10 s, and combined primer annealing/elongation at 60 °C for 30 s. All reactions including no template control (NTC) and plate control were carried out in triplicate. The plate control is a well that carries the same reaction components on every plate, and the quantification cycle (Cq) data from the plate control wells was measuring variation. A consistent Cq value of plate control across plates was obtained allowing the data combination from multiple plates into a single study data set. Baseline value was automatically determined for all plates using Eco Software V4.0. Thresholds for each HKG were determined manually (Table 2). The Cq values in triplicate with standard deviation (SD) <0.5 were averaged as raw Cq value. The five-point (10-fold) standard curve of each probe and primer pair was generated from serial dilution of a nucleic acid template. The PCR amplification efficiency (E) and R2 of each probe and primer pair were calculated from the slope of a standard curve using the following equation: E = (10(−1∕slope) − 1) × 100%. The average of at least three E values for each HKG was used as gene-specific E for following relative quantity transformation. This study was conducted according to MIQE (Minimum information for publication of quantitative real-time PCR experiments) guidelines (Bustin et al., 2009).

Data analysis

Corrected Cq values (Cq corr) were transformed from raw Cq values using ΔCq formula, Cq corr = Cqmin − log2E−ΔCq, modified from Fu et al. (2013), where ΔCq is the Cq value of a certain sample minus the Cq value of the sample with the highest expression (lowest Cq, Cqmin) of each HKG as calibrator. Stability of all HKGs were evaluated and ranked using algorithms geNorm (Vandesompele et al., 2002), NormFinder (Andersen, Jensen & Ørntoft, 2004), comparative ΔCt method (Silver et al., 2006) and Bestkeeper (Pfaffl et al., 2004) based on a web-based analysis tool RefFinder (http://www.leonxie.com/referencegene.php) (Xie et al., 2011). RefFinder calculated the geometric mean based on rankings obtained from each algorithm and provides the final comprehensive ranking. Thirty samples were randomly selected from the 60 samples, and the results of HKG ranking using 30 and 60 samples were analyzed comparatively.


E values of the 13 candidate HKGs were between 95.47% and 101.39% that fit the strict acceptable range of 95%–105%, and R2 values were 0.992–1.000 that meet the standard of >0.99 (Table 2). According to the mean Cq value of 60 tested samples, the 13 candidate genes can be divided into two groups: high expression level (Cq < 25) and low expression level (Cq > 25; Fig. 1). ACTB showed the highest expression level (Cq = 22.08), while HPRT1 showed the lowest expression level (Cq = 31.48). All HKGs except TFRC displayed a small difference between the maximum and minimum Cq values (<5 cycles). The SD of the Cq value for the plate controls in all experiment was 0.33 (SD < 0.5 is acceptable); therefore, the data of all the plates was combined as one data set.

Expression levels of candidate HKGs in the tested beluga blood samples (n = 60).

Figure 1: Expression levels of candidate HKGs in the tested beluga blood samples (n = 60).

Values are given as qPCR cycle threshold numbers (Cq values). Dots represent mean Cq values and whiskers the range of Cq values in the 60 samples.

The commonly used reference gene exploring algorithm, geNorm, calculates the M value for gene expression stability based on the geometric mean; a lower M value signifies better stability. The gene with highest M value (the least stable gene) is excluded, and the highest M value gene among the rest of the candidates is continuously excluded to obtain a stability ranking order. M values of all the genes were below the default cut-off value (M = 1.5), showing good stability for all the genes tested in both 60- and 30-sample groups (Tables 3 and 4). Another value, pairwise variation V, is used to determine the number of reference genes that are required for data analyses. V2/3 values in the 60 and 30 groups were 0.102 and 0.103 (Fig. 2), respectively, which were below the default cut-off value (0.15). It indicated that using two HKGs as reference genes is enough to obtain reliable normalized results in relative quantification. Based on geNorm analysis, ACTB, RPL4, PGK1, and B2M were the most stable HKGs in both the 60 and 30 groups (Fig. 3).

Table 3:
Results of stability among 13 candidate genes computed by four algorithms using 60 beluga blood samples.
Comprehensive ranking Delta CT BestKeeper NormFinder geNorm
HKGs Geomean of ranking value Rank Average of SD Rank SD Rank Stability value Rank M value Rank
RPL4 2.3 1 0.562 2 0.523 7 0.319 2 0.336 1
PGK1 2.38 2 0.556 1 0.595 8 0.296 1 0.386 4
B2M 3.08 3 0.614 5 0.474 3 0.418 6 0.336 1
ACTB 3.57 4 0.569 3 0.522 6 0.326 3 0.345 3
RPL18 4.6 5 0.587 4 0.509 4 0.34 4 0.478 7
RPL8 4.82 6 0.664 9 0.423 1 0.499 10 0.46 6
RPS18 4.86 7 0.634 7 0.45 2 0.466 8 0.435 5
RPS9 6.82 8 0.629 6 0.712 9 0.416 5 0.507 8
YWHAZ 8.43 9 0.649 8 0.728 10 0.454 7 0.541 9
LDHB 9.64 10 0.74 12 0.519 5 0.594 12 0.6 12
HPRT1 10.19 11 0.674 10 0.761 12 0.493 9 0.564 10
GAPDH 11 12 0.684 11 0.759 11 0.511 11 0.58 11
TFRC 13 13 0.956 13 0.88 13 0.857 13 0.655 13
DOI: 10.7717/peerj.1810/table-3
Table 4:
Results of stability among 13 candidate genes computed by four algorithms using 30 beluga blood samples.
RefFinder Delta CT BestKeeper NormFinder geNorm
HKGs Geomean of ranking value Rank Average of SD Rank SD Rank Stability value Rank M value Rank
PGK1 2.21 1 0.552 1 0.647 8 0.26 1 0.343 3
ACTB 2.45 2 0.593 3 0.561 6 0.356 2 0.331 1
RPL4 2.74 3 0.591 2 0.564 7 0.362 4 0.331 1
RPL8 4.43 4 0.678 8 0.402 1 0.51 8 0.432 6
RPL18 4.53 5 0.616 4 0.557 5 0.359 3 0.469 7
B2M 4.56 6 0.637 6 0.491 3 0.451 6 0.364 4
RPS18 4.7 7 0.642 7 0.431 2 0.473 7 0.403 5
RPS9 6.71 8 0.625 5 0.788 9 0.372 5 0.522 9
LDHB 7.52 9 0.705 10 0.497 4 0.529 10 0.493 8
YWHAZ 9.72 10 0.703 9 0.92 11 0.513 9 0.563 10
GAPDH 10.74 11 0.732 11 0.87 10 0.558 11 0.595 11
HPRT1 12 12 0.738 12 0.951 12 0.565 12 0.617 12
TFRC 13 13 1.023 13 0.975 13 0.926 13 0.68 13
DOI: 10.7717/peerj.1810/table-4
Pairwise variations generated by geNorm algorithm: (A) 60 samples; (B) 30 samples.

Figure 2: Pairwise variations generated by geNorm algorithm: (A) 60 samples; (B) 30 samples.

Stability values and ranking orders determined by four algorisms and RefFinder: (A) 60 samples; (B) 30 samples.

Figure 3: Stability values and ranking orders determined by four algorisms and RefFinder: (A) 60 samples; (B) 30 samples.

The NormFinder program calculates the stability value based on the analysis of gene expression data and ranks the potential reference genes. Lower values are assigned to the most stable genes. The ranking results of NormFinder were essentially identical in both the 60 and 30 groups showing that PGK1, ACTB, RPL4, and RPL18 were the most stable. The program BestKeeper estimates the expression stability by performing a pairwise correlation analysis of Cq values of each pair of candidate genes. BestKeeper analysis showed that the SDCq value of all HKGs (0.423–0.880) were <1, indicating that these genes were basically stably expressed. The most stable genes identified in the BestKeeper analysis in both the 60 and 30 groups were RPL8, RPS18, and B2M. The comparative ΔCt analysis is similar to the geNorm program in that the pairs of genes are compared using Cq differences, and those genes are either stably expressed or co-regulated if the ΔCq values between the pairs of genes remain constant for all samples tested. The best choice in comparative ΔCt analysis in the 60 and 30 groups was PGK1, RPL4, and ACTB. According to RefFinder, the most stable HKGs in the 60 group were RPL4, PGK1, B2M, and ACTB, while the most stable HKGs in the 30 group were PGK1, ACTB, RPL4, and RPL8 (Fig. 3).


The four algorithms used to assess the stability of HKGs, geNorm, NormFinder, BestKeeper, and comparative ΔCt represent feasible strategies, although none of them are currently considered to be the best. BestKeeper uses raw Cq data instead of the relative expression level employed by geNorm and NormFinder for selecting the least variable gene, and it has been shown that this may lead to the different outputs among these three methods (Scharlaken et al., 2008). Comparative ΔCt and geNorm, which use a pairwise comparison approach, identified the most stable genes by assuming that HKGs are not co-regulated. This may lead to incorrect ranking results when co-regulated genes are included in the analysis (He et al., 2008). The NormFinder is likely less affected by co-regulated HKGs because it considers systematic variations through a model-based approach (Andersen, Jensen & Ørntoft, 2004). In this study, the HKG stability orders suggested by the four different algorithms were not identical, particularly with the BestKeeper program, which could be explained by the distinct principles applied by each of these algorithms. Because these algorithms can demonstrate various rankings of the tested HKGs, in this study RefFinder was used to comprehensively evaluate and rank HKGs based on the rankings from different algorithms.

The four most stable HKGs (RPL4, PGK1, B2M, and ACTB) in RefFinder were also in high-ranking orders in NormFinder, geNorm, and comparative ΔCt, although the ranking in BestKeeper appeared inconsistent with that in the other three algorithms. The SDCq value of these four HKGs (0.474–0.595) showed in the BestKeeper analysis was essentially low indicating these genes were stably expressed. B2M encodes for beta-2-microglobulin protein, which is a part of major histocompatibility complex class I molecule. It was shown that a decrease in B2M expression is associated with a significant increase in leukocyte counts in dogs (Piek et al., 2011), and therefore it might not be an appropriate reference gene for immunology studies. As a result of this report, RPL4, PGK1, and ACTB are strongly recommended for use in future RT-qPCR studies using beluga blood samples. It has been proposed that the reliability of the normalization factor would increase with the number of stably expressed HKGs included in the calculations (Vandesompele et al., 2002). However, in this study the inclusion of more HKGs further reduced the V values. The V2/3 value indicated that it is not needed to include more than two genes into the normalization factor because this would not dramatically improve normalization. Furthermore, it was suggested that one could preferentially choose to use HKGs that have the same expression levels as the target gene in an experimental application to enhance the uniformity of the analysis (Spinsanti et al., 2006). According to mean Cq values, PGK1 was classified in the low expression level group (mean Cq > 25) and the other two genes in the high expression level group (mean Cq < 25). Therefore, it is recommended to use RPL4 and PGK1 for low-expression gene studies, such as cytokine expression studies when using beluga blood samples, and RPL4 and ACTB for high-expression gene studies.

In previous studies on reference gene selection in cetaceans, 30 skin biopsy samples in striped dolphins (Stenella coeruleoalba) (Spinsanti et al., 2006), 20 skin biopsy samples from seven blue whales (Balaenoptera musculus), seven fin whales (Balaenoptera physalus), and six sperm whales (Physeter macrocephalus) (Martinez-Levasseur et al., 2013), and 75 blood samples in bottlenose dolphins (Tursiops truncatus) (Chen et al., 2015) were used. Some practical points, such as available sample sizes and costs of expression stability experiments, may have an effect on the reference gene selection experiments. There is a unique opportunity in this study to compare the HKG expression stability values of 30- and 60-sample groups. The three most stable HKGs were PGK1, ACTB, and RPL4 in RefFinder when only 30 randomly selected beluga blood samples were used. The result is consistent with that using 60 samples, only differing in the ranking order of the most stable genes. These three HKGs were the most stable expression genes in geNorm, NormFinder, and comparative ΔCt, and the SDCq value (0.564–0.647) showed that they were also stably expressed. The result indicated that using only 30 beluga blood samples with various body conditions could select reliable HKGs as reference genes. Chen et al. (2015) showed similar results that using 35 bottlenose dolphin blood samples could perform reference gene selection, and PGK1, HPRT1, and RPL4 are superior reference genes. PGK1 and RPL4 are recommended as reference genes in both beluga whales (in this study) and bottlenose dolphins (Chen et al., 2015), and it provides essential information and facilitates future reference gene studies. However, there is still not enough evidence to say that these two genes are the most stable genes in blood samples from toothed whales. Further studies are needed to identify if there are universal reference genes applicable for an accurate normalization of gene expression in cetacean blood samples because of the important value of these animals in various captive environments and the significant susceptibility to environmental degradation in free-ranging species. Cytokine gene expression studies using cetacean blood samples have been conducted using several different HKGs as reference genes, including GAPDH and YWHAZ in harbor porpoises (Beineke et al., 2004; Beineke et al., 2007; Müller et al., 2013), GAPDH in bottlenose dolphins (Mancia, Warr & Chapman, 2008), and RPS9 in bottlenose dolphins, beluga whales, and Pacific white-sided dolphins (Lagenorhynchus obliquidens) (Sitt et al., 2008; Sitt et al., 2010). RPS9 could potentially be a suitable reference gene when studying beluga blood samples because in this study it was is ranked in the middle using NormFinder and comparative ΔCt, and its values in geNorm and BestKeeper were below the default value, indicating basically good expression stability.

We reported the essential background information for the selection of reference genes in RT-qPCR studies of beluga blood samples. A total of 13 candidate HKGs were evaluated, and a suite of best reference genes were recommended to accurately normalize and quantify gene expression in beluga whale blood. To the best of our knowledge, this is the first study to investigate reference gene selection in beluga whales. This investigation is an important basis for future clinical immunology studies in cetaceans.

Supplemental Information

Raw data of Ct values in qPCR, 60 samples

DOI: 10.7717/peerj.1810/supp-1

Raw data of Ct values in qPCR, 30 samples

DOI: 10.7717/peerj.1810/supp-2