BioMed Research International
Volume 2018 (2018), Article ID 4953806, 12 pages
https://doi.org/10.1155/2018/4953806

Research Article

Validation of Housekeeping Genes as Reference for Reverse-Transcription-qPCR Analysis in Busulfan-Injured Microvascular Endothelial Cells

Wen Ju,1,2,3 Alhaji Osman Smith,1,3 Tiantian Sun,1,3 Pingping Zhao,¹ Yan Jiang,¹ Lu Liu,¹ Ting Zhang,¹ Kunming Qi,2,3 Jianlin Qiao,1,2,3 Kailin Xu,1,2,3 and Lingyu Zeng1,2,3

¹Key Laboratory of Bone Marrow Stem Cell, Jiangsu Province, Xuzhou 221002, China
²Department of Hematology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221002, China
³Blood Diseases Institute, Xuzhou Medical University, Xuzhou 221002, China

Correspondence should be addressed to Kailin Xu; lihmd@163.com and Lingyu Zeng; zengly2000@163.com

Received 5 July 2018; Revised 10 September 2018; Accepted 17 September 2018; Published 2 October 2018

Academic Editor: Lap Ho

Copyright © 2018 Wen Ju et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Endothelial cells (ECs) could express some important cytokines and signal molecules which play a key role in normal hematopoiesis and repopulation. Busulfan-induced vascular endothelial injury is an important feature after hematopoietic stem cell transplantation (HSCT). But the molecular mechanism of how the injured ECs affect hematopoietic reconstruction is still unknown. It is possibly through modulation of the change of some gene expression. RT-qPCR is one of the most popular methods used to accurately determine gene expression levels, based on stable reference gene (RG) selection from housekeeping genes. So our aim is to select stable RGs for more accurate measures of mRNA levels during Busulfan-induced vascular endothelial injury. In this study, 14 RGs were selected to investigate their expression stability in ECs during 72 hours of EC injury treated with Busulfan. Our results revealed extreme variation in RG stability compared by five statistical algorithms. ywhaz and alas1 were recognized as the two idlest RGs on account of the final ranking, while the two most usually used RGs (gapdh and actb) were not the most stable RGs. Next, these data were verified by testing signalling pathway genes ctnnb1, robo4, and notch1 based on the above four genes ywha, alas1, gapdh, and actb. It shows that the normalization of mRNA expression data using unstable RGs greatly affects gene fold change, which means the reliability of the biological conclusions is questionable. Based on the best RGs used, we also found that robo4 is significantly overexpressed in Busulfan-impaired ECs. In conclusion, our data reaffirms the importance of RGs selection for the valid analysis of gene expression in Busulfan-impaired ECs. And it also provides very useful guidance and basis for more accurate differential expression gene screening and future expanding biomolecule study of different drugs such as cyclophosphamide and fludarabine-injured ECs.

1. Introduction

The vascular endothelial cells (ECs) are particularly vulnerable to toxic effect of preparative regimen drugs, such as Busulfan, cyclophosphamide, and fludarabine which are widely used prior to hematopoietic stem cell transplantation (HSCT) [1]. Several studies have indicated that bone marrow (BM) vascular niche was impaired after HSCT [2–5], which was associated with poor Graft Function [3, 4]. The healthy ECs, their expressed cytokines, and signal molecules in BM microenvironment play an important role in normal hematopoiesis and repopulation [6–8], while the function of the impaired ECs, the changes of expressed cytokines and signal molecules, and how do these changes affect hematopoietic cell function are still unknown. Because our and other previous studies [9, 10] found that ECs are essential to accelerate hematopoietic and immune reconstitution, we speculate that the occurrence of poor Graft Function is most likely related to abnormalities of preparative regimen-injured ECs and their gene expression change.

Busulfan, most widely used in HSCT, has been identified as having potent antitumor activity and inhibitory functions on normal hematopoiesis as well as myelogenous proliferation [11]. Most importantly, our study has shown that pretreatment with Busulfan for HSCT could induce obvious injury to ECs in vivo [2] but we still do not know the biomolecular mechanism. Therefore, in vitro studies of the biomolecular changes on normal and injured endothelial cells need to be clarified firstly, which is important for study on how the impaired ECs control HSC fate in the future.

Reverse-transcription-qPCR is one of the most widely used methods directly evolved from the end-point detection PCR to detect gene expression level under different research conditions because of its time-saving, high sensitivity, and specificity [12–14]. But if this technique is performed in an inappropriate way, especially using incorrect housekeeping genes (HKGs), considerable misinterpretation of results will happen [15]. The HKGs such as actb and gapdh which are found in different cells or tissues, known to maintain cellular functions, are the most widely used RGs. However, their stability varies under different experimental conditions [16, 17]. Moreover, several studies had reported that there is no single reference gene that can maintain its expression level in different experimental conditions [18–20]. Typically, internal control genes show variability in expression levels in different tissues, emphasizing the importance of identification for normalization reference validation selection.

For the biomolecule study of Busulfan on EC injury, identifying the most stable RGs in Busulfan-impaired EC system firstly is of great importance. But, based on our knowledge, stable HKGs selection in the damaged ECs has never been performed. So the purpose of this research is to recognize the most suitable HKGs in impaired ECs, which can be used as reference genes for normalization of qPCR results.

In the this study we used three software types including geNorm, NormFinder, and BestKeeper together with the delta-delta method [21–24] and Comprehensive Ranking methods [15, 25] to identify the most suitable RGs from 14 commonly used HKGs in both normal and impaired ECs. This study revealed the importance of RGs selection for the valid and reproducible analysis of gene expression in Busulfan-impaired ECs. And it also provides a very useful guidance and basis for more accurate differential expression gene screening and future expanding gene expression and biomolecule function study of different drugs such as cyclophosphamide and fludarabine-injured ECs.

2. Methods and Materials

2.1. Cultivation of Cell

The endothelial cells (bEnd.3) were purchased from the Global Bioresource Center of American Type Culture Collection (ATCC) and cultured in a medium which comprised DMEM and 10% FBS and then incubated at 37°C in incubator and humidified at 5% CO₂, respectively. The medium containing DMEM, 10% FBS, and 30mg/L of Busulfan (purchased from Sigma-Aldrich) was added to the ECs and then observed at different time interval at 0, 12, 24, 36, 48, and 72 hours, respectively. All our samples were independently prepared three times.

2.2. Isolation of RNA

RNA was isolated from the normal and Busulfan-injured ECs using TRIZOL Reagent, Invitrogen kit according to the manufacturer’s instructions [26]. The concentration and quality of the extracted RNA were calculated by NanoDrop 2000 spectrophotometer (Thermo, USA). The purity was confirmed by using the absorption ratio which was between 1.8 and 2. The integrity was also evaluated using 0.7% ~ 1.0% agarose gel electrophoresis indicating no contamination of the DNA and degradation of RNA. Genomic DNA contamination was determined by performing qPCR with total RNA as the template; no PCR product appearing on gel proved that no genomic DNA contaminated the total RNA.

2.3. Complementary DNA (cDNA) Synthesis

The intact RNA was reverse transcriptase at once after isolation using Invitrogen reagent M-MLV. Firstly, the RNA was transcribed into first cDNA, using 10μm oligonucleotide dT primer; 10mM dNTP and DEPC-treated water were combined together and preserved at 65°C for 10 minutes with extended temperature of 4°C in the conventional PCR. The transcription mixture of 0.1M DTT, 50,000U M-mlv, and 5x-strand buffer was then incubated at 37°C for 50 minutes, at 70°C for 5 minutes and extended temperature of 4°C. All cDNA was synthesized from 300ng isolated RNA sample in a total volume of 20μL and kept at -20°C until ready for use. All samples were diluted by 1:10 with DEPC water, in order to achieve equal concentration of our samples for RT-qPCR analysis.

2.4. RT-qPCR

The 14 candidate reference genes primers and three target genes (ctnnb1, robo4, and notch1) were designed and purchased from Thermo Scientific and were selected in consideration of different intracellular biological function as shown in Table 1. Primer sequence and information are also shown in Table 1. RT-qPCR was performed using Light Cycler®480 in multiwall plate 96. Each 20 μL reaction contained 10μL of 2× Supermix SYBR Green I Master, 1μL forward and reversed primers, 8μL of distilled water, and 1μL of cDNA. The RT-qPCR program was comprised of predenaturation at 95°C for 1 minute, 40 cycles at 95°C for 20 seconds, 60°C for 15 seconds, and 72°C for 15 seconds.

Table 1: Summary of 14 housekeeping genes and target genes evaluated in this study. Official full name, accession numbers, official full name, primer sequences, and product sizes are shown.

Symbol
Official full name
Physiological functions
Accession numbers
Primer sequence (forward/reverse)
Products size (bp)

gapdh
Glyceraldehyde-3-phosphate dehydrogenase
An enzyme that catalyzed the sixth step of glycolysis, a process in which glucose is converted to pyruvate.
NM_001001303
F:catggccttccgtgttccta
R:gcggcacgtcagatcca
55 [28–30]

actb
Beta-actin
Protein plays a key role in cell motility and cytoskeletal maintenance i.e. the structure and integrity.
NM_007393
F:atgtggatcagcaagcagga
R:aagggtgtaaaacgcagctca
99 [31]

ubc
Ubiquitin C
Protein coded genes are involved in DNA repair and cell cycle regulation.
NM_019639.4
F:ccagtgttaccaccaagaag
R:acccaagaacaagcacaagg
94

eef1a1
eukaryotic translation elongation factor 1 alpha 1
Translation elongation factor
NM_010106
F:tccgattacgacgatgttga
R:agtcgccttggacgttctt
125 [32]

b2m
Beta -2 microglobulin
This gene encodes serum protein found on MHC class I on the surface of all nucleated cells.
NM_009735
F:ttcagtatgttcggcttccc
R:tggtgcttgtctcactgacc
103 [33, 34]

rplp0
Ribosomal protein lateral stalk subunit P0
It is a neutral phosphoprotein at the C-terminal end of ribosomal phosphoproteins
NM_007475
F:ccgatctgcagacacacact
R:accctgaagtgctcgacatc
91 [35]

ywhaz
tyrosine 3-monooxygenase/tryptophan 5-monooxygenase
activation protein, zeta
A central hub protein for many signal transduction pathways and is a major regulator of apoptotic pathways.
NM_011740
F:ctttctggttgcgaagcatt
R:ttgagcagaagacggaaggt
148

hmbs
hydroxymethylbilane synthase
Providing instruction for making the enzyme hydroxymethylbilane synthase.
NM_013551
F:cagggtacaaggctttcagc
R:cggagtcatgtccggtaac
149 [36]

gusb
β-glucuronidase
Providing instruction for producing an enzyme called beta- glucuronidase.
NM_010368
F:actcctcactgaacatgcga
R:ataagacgcatcagaagccg
96 [37]

ppia
Peptidyl prolyl isomerase A
Cyclosporin binding protein /Inhibitor of serine threonine phosphatase
NM_008907
F:cagtgctcagagctcgaaagt
R:gtgttcttcgacatcacggc
109 [38]

tbp
TATA box binding protein
Providing an instruction for making TAXA box binding proteins.
NM_013684
F:ggggtcataggagtcattgg
R:catctcagcaacccacacag
127 [39]

alas1
δ-Aminolevulinate synthase 1
catalyzing the first step of heme biosynthesis
NM_020559
F:gtctgtgccatctgggactc
R:ctgtccacatcagctgtcca
119

hprt1
Hypoxanthine phosphoribosyltransferase 1
providing instructions for producing an enzyme called hypoxanthine phosphoribosyl transferase 1
NM_013556
F:cataacctggttcatcatcgc
R:tcctcctcagaccgctttt
95 [40]

tfrc
transferrin receptor
This gene encodes a cell surface receptor necessary for cellular iron uptake by the process of receptor-mediated endocytosis.
NM_011638
F:gcaccaacagctccaaagtc
R:ccagtgtgggaacaggtctt
133 [41]

ctnnb1
catenin (cadherin associated protein), beta 1
The key function of this protein is to mediate the canonical Wnt signaling pathway, regulate gene transcription and mediate cell-cell adhesion
NM_007614.3
F: gtgcgctgagcttcaggt
R: tcagctcgtgtcctgtgaag
147

robo4
Roundabout guidance receptor 4
Robo4 is a vascular-specific receptor.
NM_028783.3
F:cagcctggttagctcttctgatg
R:gcacgagcaaagtgagtatcagc
57 [42]

notch1
Notch homolog 1
It plays a role in a variety of developmental processes by controlling cell fate decisions
NM_008714.3
F:ttcgtgctcctgttctttgtg
R:gggctctctccgcttcttc
129

The specificity of the primers and the size of the PCR products were checked using 2% agarose gel electrophoresis and gel-red staining. The threshold values (Cp value) were obtained using a fluorescence threshold of 1.0. Then the results were copied into input file, based on the software requirement. The efficiency of each primer was calculated using the standard gradient of RT-qPCR. The standard curve was obtained by plotting the Cp value (y axis) against the logarithm of the total cDNA concentration (x axis).

2.5. Statistical Analysis

The three excel based software types geNorm, NormFinder, and BestKeeper together with the comparative delta-delta Cp method were used to assess the selected RGs stability. Applying the geNorm software, the M value is used to assess the stability of the internal control gene and it was determined by stepwise removal of gene with higher M value. Based on this software, the reference gene with the lowest M value is considered the most stable, while those with higher values indicate least stability. In addition, according to [24], the use of maximum number of genes for normalization based on average pairwise variation of all the reference genes was proposed. The NormFinder software uses mathematical ANOVA, to estimate the stability of the reference gene; like the geNorm software gene with the lowest M value is considered the most stable [23]. The BestKeeper software calculates the standard deviation (SD), coefficient of variation (CV%), and Pearson correlation coefficient (r), respectively. These calculated values are then used to assess the stability of the selected reference gene. In this study, we use the Pearson coefficient (r= 0.936-0.977) to rank the stability of the selected gene [22].

The 2-ΔΔCT method [21] is the easiest method that is used to detect the expression level of genes from RT-qPCR experiments. Using the ΔΔCt method the following steps are observed: Firstly: normalization of reference gene or lower Ct value ΔCt = ΔCt (max) -ΔCt (mini)}; secondly: the lower ΔCT value is used as the control—then ΔΔCt is obtained by ΔCtcontrol-ΔCtreference; the exponential expression is calculated by using ΔCt expression = -ΔΔCt; and thirdly: average replicates and calculating the SD, variance, and CV%. In this study we used the delta-delta Ct method and in order for us to obtain the true fold difference we take the log base 2 of the mean expressed value to even out the scale of up- and downregulated gene. Downregulated gene has a scale of 0-1, while upregulated gene has scale of 1-infinity.

3. Results

3.1. Primer Efficiency and Specificity Detection

Firstly, we identified expression level of the 14 RGs following the experimental procedure for qPCR using SYBR Green I Master [27]. The specificity of all the primers was evaluated using the melting curve with one single peak as shown in (Figure 1(a)). The PCR product and the band size for each primer were determined using 2% agarose gel electrophoresis. Gel imaging demonstrated that the size of all the amplified products was as expected; the bands were clear and there was no nonspecific banding as shown in Figure 1(b). In addition, there was no obvious primer dimer generation during PCR amplification with dH₂O or RNA as template.

(a)

(b)

Figure 1: Examination of primer specificity and size of RT-qPCR amplification productions. (a) Melting curve analysis of the 14 reference genes amplicons after the RT-qPCR reactions. Only one peak for each primer was shown, suggesting high specificity of primers. (b) Examination of primer specificity and amplicon size. The high specificity with only one band as expected of each RT-qPCR amplification production using 2% agarose gel electrophoresis was shown.

The primer pair efficiency ranged from 88.7% to 113% and coefficient (r²) values were between 0.989 and 1.0 as shown in Table 2. The expression levels of each gene were detected as Cp or CT value. Figure 2 showed the different expression level between 19.9 and 30.4 cycles. actb showed the highest mRNA level while the mean Cp value (30.4) of eef1a1 expression level was lowest. Thus, it was not suitable as an internal reference gene.

Table 2: Assay performance characteristics showing PCR coefficient (R²), slope, and primer pair efficiency (E).

Gene name
Coefficient (R²)
slope (A)
primer pair efficiency (E)

actb
1
-3.482
0.937

gapdh
0.99
-3.402
0.968

eef1a1
0.997
-3.282
1.017

rplp0
0.999
-0.325
0.999

ppia
0.999
-0.452
0.948

ubc
0.999
-0.365
0.982

ywhaz
1
-3.37
0.906

hprt1
1
-3.625
0.887

hmbs
0.996
-3.272
1.021

b2m
0.998
-3.405
1.021

tfrc
1.0
-3.215
0.966

alas1
0.989
-3.502
1.047

gusb
0.992
-3.042
0.93

tbp
1
-3.37
1.131

Figure 2: Mean Ct values of the 14 housekeeping genes in EC cells. Bars represent the mean ± standard deviation.

3.2. Descriptive Statistic of Selected Reference Genes in Normal and Injured ECs

The RT-qPCR was used for the assessment of transcriptional expression of the 14 RGs in normal and injured ECs. The Cp values of all selected RGs using a threshold of 1.0 were detected except eef1a1 because of lowest expression level (missing Ct value in impaired endothelial cells). Evaluation of Cp values by statistical analysis revealed the variation and difference of RGs in normal and injured ECs. The 13 genes except eef1a1 were categorized into two groups based on the expression level. 7 genes (actb, gapdh, rplo, b₂m, ppia, ubc, and ywhaz), whose Cp values fall between 21 and 24 cycles, are considered as the highly expressed genes. Those with Cp values between 28 and 26 cycles (gusb, alas1, tfrc, hrt1, hmbs, and tbp) were moderately expressed genes. In addition, the coefficient of variation (CV%) is shown in Table 3, with ywhaz (CV=9.96%), actb (CV= 9.23%), and alasi (CV= 9.16%) indicating the highest variation in gene expression, while ubc (CV= 6.65%) and tbp (4.32%) had the lowest one indicating the lowest variation in expression level of gene.

Table 3: Mean Ct, STD, coefficient of variation, and Pearson coefficient of candidates’ RGs by BestKeeper.

Gene names
Geomean
Ar Mean
Min [CP]
Max [CP]
STD [±CP]
CV
Pearson coefficient [2]
p-value

ywhaz
24.33
24.51
22.17
32.58
2.37
9.66
0.996
0.001

actb
20.97
21.11
19.06
26.98
1.95
9.23
0.978
0.001

alas1
27.71
27.9
25.42
36.32
2.55
9.16
0.996
0.001

b2m
22.82
22.97
20.81
30.07
2.02
8.8
0.982
0.001

tfrc
27.15
27.32
25.2
35.58
2.29
8.4
0.99
0.001

ppia
23.15
23.27
20.73
28.96
1.92
8.26
0.962
0.001

hprt1
28.1
28.27
25.89
36.58
2.33
8.24
0.99
0.001

gapdh
21.13
21.23
19.62
26.18
1.7
8
0.958
0.001

rplpo
21.41
21.5
19.86
26.52
1.54
7.14
0.992
0.001

gusb
26.79
26.89
25.23
32.66
1.89
7.01
0.984
0.001

hmbs
28.83
28.94
26.98
35.4
2.03
7
0.994
0.001

ubc
23.13
23.24
21.04
29.42
1.59
6.85
0.835
0.002

tbp
28.15
28.2
26.93
32.47
1.22
4.32
0.96
0.001

3.3. Examination of Reference Stability

3.3.1. BestKeeper

The coefficient of variation (CV) expressed in percentage is used to determine the variation of the selected reference genes. The main factor in data analysis of CV: genes with the lowest value have lesser the variation [ubc (CV=6.65%); tbp (CV=4.32%)], while those with higher value [ywhaz (CV=9.66%); actb (CV=9.23%)] had more variation in gene expression. The Pearson coefficient of correlation (r²) is the index of the BestKeeper, which is used to determine the stability of the input reference gene and give the expression value. Genes with r² closer to 1 are considered as the most stable genes; thus ywhaz and alas1 with r² = 0.996 followed by actb (r² = 0.978) are ranked as the highest stable genes, while ubc (r² = 0.835) has the lowest value, indicating it had the lowest stability expression within these selected candidate reference genes as shown in Table 3.

3.3.2. geNorm Analysis

geNorm software grades the stability of the candidate genes based on the calculated M value. Genes with lower M value are considered as the most stable genes, while those with higher M value are the least stable genes. The three most stable genes include ywhaz and hprt1 both with an M value of 0.227 and alas1 with an M value of 0.284. The three reference genes with the highest M values were ubc (M = 0.962), tbp (M= 0.883), and rplo (M= 0.799), considered to be least stable expressed genes (Table 5 and Figure 3(a)). In addition to the geNorm software, pairwise variation was also used to determine the optimal number of candidate genes. All pairwise variation in this study was shown in Figure 3(b). According to [24], a cut-off value of 0.15 is recommended, where genes with a V ≤ 0.15 should be included because they indicate the lowest variation of reference gene normalization. Therefore, based on the cut-off value of 0.15, the two most stable reference genes (V₂/V₃₎ of this dataset would be adequate for accurate normalization as shown in Figure 3(a).

(a)

(b)

Figure 3: Graphical presentation of stability value by geNorm. (a) Showing the ranking of the 13 reference genes by geNorm software, with the most stable one toward the right and least stable one toward the left. (b) Determination of minimal number of reference genes by pairwise variation (Vn /n +1). The determination of optimal number of housekeeping genes by pairwise variation was shown. The effect of including more reference genes in a set of numbers of reference genes in all cases below the cut-off value of 0.15 is shown. The two most stable expressed reference genes may be accurate for qRT-PCR normalization. Including more reference genes for RT-qPCR normalization will not increase the stability of reference genes.

3.3.3. NormFinder

This software determined the stability value of the selected references and at the end it identified the best two combinations of genes. Genes with the lowest stability values are considered as the most stable (hmbs= 0.126, gusb=0.15, and actb=0.187), while those with the higher stability values are the least stable genes (ubc=0.599, tbc=0.494, and alas1= 0.456). The two best combined genes revealed by the software include hmbs and gusb with a stability value of 0.081 as shown in Figure 4.

Figure 4: Stability ranking values of reference genes by NormFinder. It shows the stability ranking values of reference genes by NormFinder with the most stable gene being hmbs (0.126) and least stable gene being ubc (=0.599). The software also identified the best two combination genes, hmbs and gusb with a stability value of 0.081.

3.3.4. The Comparative ΔΔ CT

The CT method determines the stability of housekeeping genes based on relative expression comparison within the samples. The lower ΔCp value between samples of gene tested is considered to remain constant. In order to obtain the most stable gene, the lower ΔCp value is deducted from other Cp values [43, 44]. The data got from this method resemble those results we obtained from the BestKeeper. It ranked the alas1 and hprt1 as the two most stable RGs as shown in Table 4. In contrast to the geNorm software analysis, it identified ywhaz and hprt 1 as the most stable genes, while the NormFinder recognized hmbs as the most stable gene.

Table 4: Expression stability detected by the comparative CT method (ΔΔCT method).

Gene names
Mean Ct value
SD
Variance
Ct±SD
Mean fold difference -CT
True fold difference log base-Ct

actb
21.105
2.672
7.139
-2.05±2.67
1.54±0.038
0.622

gapdh
21.348
2.085
4.97
-1.70±2.08
1.31±0.073
0.387

rplo
21.499
2.534
4.773
-1.65±2.53
1.45±0.055
0.540

ppia
23.373
2.534
6.423
-1.82±2.53
1.64±0.049
0.711

ubc
23.24
2.584
6.68
-2.21±2.58
1.31±0.036
0.379

ywhaz
24.512
3.462
11.986
-2.35±3.46
2.17±0.018
1.115

hprt1
28.266
3.519
12.383
-2.38±3.52
2.20±0.017
1.138

hmbs
28.939
2.842
8.077
-1.96±2.84
1.84±0.036
0.878

b2m
22.97
3.02
9.123
-2.16±3.02
1.82±0.028
0.86

tfrc
27.322
3.484
12.136
-2.13±3.48
1.33±0.020
0.408

Alas1
27.896
3.656
13.365
-2.48±3.66
2.26±0.014
1.175

gusb
26.894
2.587
6.693
-1.66±2.59
1.90±0.053
0.923

tbp
28.198
1.831
3.352
-1.27±1.83
1.47±0.012
0.558

Table 5: Ranking of thirteen RGs obtained using five different algorithms: ywhaz, alas1, and hmbs were ranked as the most stable housekeeping genes while ubc, tbp, and gapdh were ranked as the least stable ones.

Gene names
Bestkeeper
GeNorm
NormFinder
-CT
Comprehensive Ranking

Pearson coefficient
ranking
Stability Values
ranking
Stability values
ranking
Mean fold difference
ranking
Geometric mean
Final ranking

ywhaz
0.996
1
0.228
1
0.386
9
2.166
3
2.28
1

alas1
0.996
1
0.284
3
0.456
11
2.26
1
2.40
2

hmbs
0.994
3
0.561
6
0.126
1
1.838
5
3.08
3

hprt1
0.99
5
0.228
1
0.444
10
2.20
2
3.16
4

gusb
0.984
7
0.683
8
0.15
2
1.896
4
4.60
5

b2m
0.982
8
0.482
5
0.316
5
1.815
6
5.89
6

actb
0.978
9
0.650
7
0.187
3
1.540
8
6.24
7

tfrc
0.99
5
0.372
4
0.333
7
1.327
11
6.26
8

rplo
0.992
4
0.799
11
0.378
8
1.454
10
7.70
9

ppia
0.962
10
0.719
9
0.329
6
1.637
7
7.84
10

gapdh
0.958
12
0.766
10
0.301
4
1.308
12
8.71
11

tbp
0.96
11
0.883
12
0.494
12
1.472
9
10.93
12

ubc
0.835
13
0.963
13
0.599
13
1.30
13
13.00
13

It illustrated the calculations of standard deviation (SD), variance, and mean fold difference using the delta-delta method and true fold difference determined by using log base 2 power of delta-delta value to determine the actual fold difference between the selected reference genes.

3.3.5. Cumulative Ranking of All the Methods

We coranked the four methods to determine the most stable RGs in our study. In order to achieve the overall ranking, we allocate a weight to all the genes, which is then used to calculate the geometric mean [25]. The geometric mean value we obtained ranked ywhaz, alas1, and hmbs as the three most stable ones and gapdh, tbp, and ubc were considered as the least stable housekeeping genes as shown in Table 5.

3.3.6. Illustration of the Impact of the Selected Housekeeping Genes on Normalized Fold Change of the Target Genes

To evaluate the performance of the selected reference genes, we detect three target genes from three different signalling pathways and normalized the gene expression level or the fold change values using different selected references genes. We selected the best two candidate genes (ywhaz and alas1) and the two most often used RGs (actb and gapdh). The results are shown in Figure 5, which suggested the strong variation when different reference genes were selected. When using ywhaz and alas1 as reference genes, which were selected as the best two candidate housekeeping genes, the fold change values did not greatly increase over time. When using actb and gapdh as reference genes, which were commonly used, very higher values of fold change were obtained, with strong variation during cell injury, which suggested actb and gapdh were not suitable as reference genes during gene expression analysis in Busulfan-impaired ECs.

Figure 5: The impact of the selected different housekeeping genes on the target gene expression. Fold change values of three genes (ctnnb1, robo4, and notch1) were calculated based on actb, gapdh, ywhaz, and alas1. When using ywhaz and alas1 as reference genes, which were selected as the best two candidate housekeeping genes, the fold change values did not greatly increase over time. When using actb and gapdh as reference genes, which were commonly used, very higher values of fold change were obtained, with strong variation during cell injury.

3.3.7. Effect of Busulfan on robo4 Expression in ECs

When ywhaz was used as reference gene, we also found that robo4 expression level was significantly increased from 12h (0.7±0.3) to 48h (8.0±1.0) as shown in Figure 6.

Figure 6: Expression levels of the robo4 gene. The normal and Busulfan-injured endothelial cells were used, using wyhaz, the best validated reference gene for normalization. Data are mean ±SEM, n =3; P 0.01; P 0.001 vs. 0h calculated from the 3 independent replicates.

4. Discussion

The ECs have been suggested recently to play an active role in normal hematopoiesis and HSC trafficking [6, 8]. Meanwhile, these cells are vulnerable to Busulfan, one bifunctional DNA alkylating agent widely used in preparative regimens in conditioning therapy for HSCT [1, 2]. Up to now, how the biomolecular expression changed in the injured ECs and how these injured ECs affected HSC are still unknown. Therefore, it is really important for screening of differential expression gene firstly, which will provide important information on the gene expression change in normal and Busulfan-injured ECs, especially those genes associated with HSC survival, homing, and trafficking. Gene expression changes caused by this drug can be detected accurately and sensitively by RT-qPCR. Several reports have ascribed that there is no specific housekeeping gene that can remain unchanged in all experiments and it is recommended that multiple genes can be used for normalization, in order to determine the most stable gene among the group of genes tested [45–47]. The concepts to verify and identify the housekeeping gene for normalization in RT-qPCR data analysis and to determine their stability under different experimental condition have been demonstrated [48–51]. The method seems to be easy, but normalization of data before use still remains an issue and needs to be validated. At present, there are still various studies regarding the identification of suitable HKGs by RT-qPCR. But, based on our knowledge, this is the first study of stable RGs selection in normal and Busulfan-injured ECs. In all, 14 RGs (actb, hmbs, hprt1, alas1, gapdh, gusb, ywhaz, rplo, ppia, tfrc, b2m, tbp, eef1a1, and ubc) were investigated in this study. The Ct value is usually used to determine the mRNA level by RT-qPCR data analysis. The same amount of RNA is used to obtain the gene expression levels. Generally, Cp value greater than 30 or lower than 15 is not suitable for RT-qPCR [52, 53]. In this research, the Ct values of selected RGs showed moderate variation in our samples. The Ct values of actb, gapdh, rplo, ppia, b2m, and ubc ranged from 21 to 23, while gusb and ywhaz ranged from 24 to 26 and the remaining reference genes alas1, tfrc, and tbp ranged from 27 to 28, respectively. Eef1a1 had the lowest expression level greater than 30, not suitable as RGs. Five different statistical methods previously reported [15] were used to compare the expression stability of these 13 candidate RGs. Based on the BestKeeper software analysis, the coefficient of variation, expressed in percentage, showed the variation in gene expression; thus genes with the highest CV values (ywhaz, actb, and alas 1) in sequential order had the highest variation in gene expression, in contrast to those with the lowest CV values (tbp, ubc, and hmbs), respectively. In addition, the Pearson correlation coefficient (r) closer to 1 represented the most stable gene. Based on the Pearson coefficient (r), ywhaz and alas1 were identified as the highest stability genes followed by hmbs, whereas ubc displayed the lowest stability. Our result revealed that actb was relatively stable and expressed in both normal and injured ECs in contrast to gapdh which was unstable. However, ywhaz, alasi, and hmbs were the three reference genes reported to be more stable in contrast to the gapdh, tbp, and ubc, which were the three least stable RGs. In accordance with previous studies, they identified HPRT1 and YWHAZ as the two most suitable genes in HUVEC-stain treated cells stimulated with TNF-α in relation to gene expression [25]. However, there was complete discrepancy in two recent studies, which identified TFRC, RPLO, GAPDH, and ACTB as the most stable genes [12] and the other studies found that hmbs, ywhaz, and tbp were considered as three most stable RGs in MSCs before and after differentiation [54]. Interestingly, in our study, ywhaz was ranked 1st, hmbs 3rd, actb 7th, tfrc 8th, gapdh11th, and rplo 9th, respectively. These findings suggested that it is very important to assess the expression stability of the selected RGs in any experimental research for ECs.

In our study we employed five methods (geNorm, NormFinder, BestKeeper, 2-ΔΔCt, and Comprehensive Ranking methods). The pairwise comparison approach includes the BestKeeper and geNorm, and this software identifies the most stable RG based on the expression of variation ratios among the RGs in our sample collected. The ratio variation (pairwise variation) for two candidate RGs across the sample measures the stability of the gene. However, the stability measurements of all the methods were combined to obtain the overall ranking of selected genes. The grading of RGs stability may be different due to the discrepancies of the program software. For example, the geNorm software ranked ywhaz and hprt 1 as the two most stable genes, while the NormFinder ranked the hmbs and gusb as the most stable genes. On the other hand, the BestKeeper ranked ywhaz and actb as the two most stable genes, while 2-ΔΔCt method ranked alasi and hprt1 as the two most stable genes. So, finally, an overall ranking is established by calculating the geometric mean in order to minimize the differences between the statistical programs (geNorm, NormFinder and BestKeeper) and delta-delta method and have a final consensus of the most stable RGs. Based on the overall ranking, ywhaz was graded as the most stable among the 13 HKGs, together with alas1 and hmbs, respectively. These three genes were considered as the most appropriate HKGs because they displayed minimal fluctuations under our experimental conditions.

Some previous reports have suggested that accurate measurement of expression levels of normalization required multiple HKGs [24]. Here we performed RT-qPCR to test the mRNA expression level of three target genes based on the best two candidate genes (ywhaz and alas1) and the two most often used RGs (actb and gapdh) for normalization. Interestingly, we got very different results, which suggest that it is really an important thing to select suitable RGs before RT-qPCR was performed. The improper use of RGs would lead to a misinterpretation of gene expression data. But unfortunately, there were still a lot of researchers who did not provide a clear evidence for RGs selection. To our best knowledge, this is the first time to report the RG selection in Busulfan-injured ECs. Henceforth, our results will be very useful in guiding us/other researchers for further gene expression studies of ECs and encourage more investigators to perform RGs selection before gene expression analysis.

5. Conclusion

Our study demonstrated that Busulfan can strongly influence the stability of RGs. ywhaz and alas1 but not actb or gapdh were recognized as the idlest RGs for normal and injured ECs. This will be helpful for detection of gene expression in relation to both normal and Busulfan-injured ECs. Moreover, we also found that robo4 was significantly upregulated in Busulfan-injured ECs with time prolonged by optimized gene expression analysis. In conclusion, the results showed strong difference in gene expression level of three target genes using four different RGs which means that RGs selection is necessary for RT-qPCR normalization.

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

All authors have no conflicts of interest to disclose.

Authors’ Contributions

Wen Ju, Alhaji Osman Smith, Kailin Xu, and Lingyu Zeng designed research; Pingping Zhao, Yan Jiang, Tiantian Sun, Ting Zhang, and Kunming Qi performed experiments; Wen Ju, Pingping Zhao, and Jianlin Qiao analyzed the data; Wen Ju, Alhaji Osman Smith, and Tiantian Sun wrote and revised the manuscript. Kailin Xu and Lingyu Zeng corrected the final version of this paper. All authors approved the submission of the manuscript. Wen Ju, Alhaji Osman Smith, and Tiantian Sun contributed equally to this work.

Acknowledgments

This research was supported by National Natural Science Foundation of China under Grant numbers 81700178, 81400082, 81641151, and 81570096; Natural Science Foundation of Jiangsu Province under Grant number BK20170259; Jiangsu University Excellent Science and Technology Innovation Team, Jiangsu Province’s Key Provincial Talents Program under Grant number ZDRCA2016054; Science and Technology Program of Xuzhou under Grant number KC15H0077; China Postdoctoral Science Foundation Grant number 2018M632380; Jiangsu Planned Projects for Postdoctoral Research Funds under Grant number 1701064B; and the Fundamental Research Funds of Xuzhou Medical University.

References

A. Rambaldi, A. Grassi, A. Masciulli et al., “Busulfan plus cyclophosphamide versus busulfan plus fludarabine as a preparative regimen for allogeneic haemopoietic stem-cell transplantation in patients with acute myeloid leukaemia: an open-label, multicentre, randomised, phase 3 trial,” The Lancet Oncology, vol. 16, no. 15, pp. 1525–1536, 2015.

L. Zeng, L. Jia, S. Xu, Z. Yan, S. Ding, and K. Xu, “Vascular endothelium changes after conditioning in hematopoietic stem cell transplantation: Role of cyclophosphamide and busulfan,” Transplantation Proceedings, vol. 42, no. 7, pp. 2720–2724, 2010.

Y. Kong, Y. Hu, X.-H. Zhang et al., “Association between an Impaired Bone Marrow Vascular Microenvironment and Prolonged Isolated Thrombocytopenia after Allogeneic Hematopoietic Stem Cell Transplantation,” Biology of Blood and Marrow Transplantation, vol. 20, no. 8, pp. 1190–1197, 2014.

Y. Kong, Y. Wang, Y. Hu et al., “The bone marrow microenvironment is similarly impaired in allogeneic hematopoietic stem cell transplantation patients with early and late poor graft function,” Bone Marrow Transplantation, vol. 51, no. 2, pp. 249–255, 2016.

X. Cao, Y. Kong, Y. Song et al., “Impairment of bone marrow endothelial progenitor cells in acute graft-versus-host disease patients after allotransplant,” British Journal of Haematology, vol. 182, no. 6, pp. 870–886, 2018.

S. Smith-Berdan, A. Nguyen, M. A. Hong, and E. C. Forsberg, “ROBO4-mediated vascular integrity regulates the directionality of hematopoietic stem cell trafficking,” Stem Cell Reports, vol. 4, no. 2, pp. 255–268, 2015.

Z. Yan, L. Zeng, Z. Li et al., “Bone marrow-derived endothelial progenitor cells promote hematopoietic reconstitution after hematopoietic stem cell transplantation,” Transplantation Proceedings, vol. 45, no. 1, pp. 427–433, 2013.

L. Ding, T. L. Saunders, G. Enikolopov, and S. J. Morrison, “Endothelial and perivascular cells maintain haematopoietic stem cells,” Nature, vol. 481, no. 7382, pp. 457–462, 2012.

L. Zeng, C. Chen, G. Song et al., “Infusion of Endothelial Progenitor Cells Accelerates Hematopoietic and Immune Reconstitution, and Ameliorates the Graft-Versus-Host Disease After Hematopoietic Stem Cell Transplantation,” Cell Biochemistry and Biophysics, vol. 64, no. 3, pp. 213–222, 2012.

J. M. Butler, D. J. Nolan, E. L. Vertes et al., “Endothelial cells are essential for the self-renewal and repopulation of Notch-dependent hematopoietic stem cells,” Cell Stem Cell, vol. 6, no. 3, pp. 251–264, 2010.

M. Hassan and B. S. Andersson, “Role of pharmacogenetics in busulfan/cyclophosphamide conditioning therapy prior to hematopoietic stem cell transplantation,” Pharmacogenomics, vol. 14, no. 1, pp. 75–87, 2013.

T. Li, H. Diao, L. Zhao et al., “Identification of suitable reference genes for real-time quantitative PCR analysis of hydrogen peroxide-treated human umbilical vein endothelial cells,” BMC Molecular Biology, vol. 18, no. 1, article no. 10, 2017.

Y. Li, K. Wang, L. Chen, X. Zhu, and J. Zhou, “Quantification of mRNA Levels Using Real-Time Polymerase Chain Reaction (PCR),” in Breast Cancer, vol. 1406 of Methods in Molecular Biology, pp. 73–79, Springer New York, New York, NY, 2016.

M. Shakeel, A. Rodriguez, U. B. Tahir, and F. Jin, “Gene expression studies of reference genes for quantitative real-time PCR: an overview in insects,” Biotechnology Letters, vol. 40, no. 2, pp. 227–236, 2018.

Y. Panina, A. Germond, S. Masui, and T. M. Watanabe, “Validation of Common Housekeeping Genes as Reference for qPCR Gene Expression Analysis During iPS Reprogramming Process,” Scientific Reports, vol. 8, no. 1, 2018.

X. Li, Q. Yang, J. Bai, Y. Xuan, and Y. Wang, “Identification of appropriate reference genes for human mesenchymal stem cell analysis by quantitative real-time PCR,” Biotechnology Letters, vol. 37, no. 1, pp. 67–73, 2015.

B. Appukuttan, L. M. Ashander, Y. Ma, and J. R. Smith, “Selection of reference genes for studies of human retinal endothelial cell gene expression by reverse transcription-quantitative real-time polymerase chain reaction,” Gene Reports, vol. 10, pp. 123–134, 2018.

X. Wang, X. Liu, C. Liu et al., “Validation of reference genes for the normalization of RT-qPCR expression studies in human tongue carcinoma cell lines and tissue,” Oncology Letters, vol. 13, no. 5, pp. 3951–3957, 2017.

C. E. Molina, E. Jacquet, P. Ponien et al., “Identification of optimal reference genes for transcriptomic analyses in normal and diseased human heart,” Cardiovascular Research, vol. 114, no. 2, pp. 247–258, 2018.

J. Rauh, A. Jacobi, and M. Stiehler, “Identification of stable reference genes for gene expression analysis of three-dimensional cultivated human bone marrow-derived mesenchymal stromal cells for bone tissue engineering,” Tissue Engineering Part C: Methods, vol. 21, no. 2, pp. 192–206, 2015.

X. Rao, D. Lai, and X. Huang, “A new method for quantitative real-time polymerase chain reaction data analysis,” Journal of Computational Biology, vol. 20, no. 9, pp. 703–711, 2013.

M. W. Pfaffl, A. Tichopad, C. Prgomet, and T. P. Neuvians, “Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: bestKeeper—excel-based tool using pair-wise correlations,” Biotechnology Letters, vol. 26, no. 6, pp. 509–515, 2004.

C. L. Andersen, J. L. Jensen, and T. F. Ørntoft, “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 Research, vol. 64, no. 15, pp. 5245–5250, 2004.

J. Vandesompele, K. De Preter, F. Pattyn et al., “Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes,” Genome Biology, vol. 3, no. 7, Article ID RESEARCH0034, 2002.

B. Zyzyńska-Granica and K. Koziak, “Identification of Suitable Reference Genes for Real-Time PCR Analysis of Statin-Treated Human Umbilical Vein Endothelial Cells,” PLoS ONE, vol. 7, no. 12, 2012.

X. Li, J. Qiao, N. Yang et al., “Identification of Suitable Reference Genes for Normalization of Real-Time Quantitative Polymerase Chain Reaction in an Intestinal Graft-Versus-Host Disease Mouse Model,” Transplantation Proceedings, vol. 47, no. 6, pp. 2017–2025, 2015.

X. Feng, Y. Xiong, H. Qian, M. Lei, D. Xu, and Z. Ren, “Selection of reference genes for gene expression studies in porcine skeletal muscle using SYBR green qPCR,” Journal of Biotechnology, vol. 150, no. 3, pp. 288–293, 2010.

D. Laukens, L. Devisscher, L. Van den Bossche et al., “Tauroursodeoxycholic acid inhibits experimental colitis by preventing early intestinal epithelial cell death,” Laboratory Investigation, vol. 94, no. 12, pp. 1419–1430, 2014.

T. Mazumdar, F. M. Gorgun, Y. Sha et al., “Regulation of NF- B activity and inducible nitric oxide synthase by regulatory particle non-ATPase subunit 13 (Rpn13),” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 107, no. 31, pp. 13854–13859, 2010.

Y. Wu, Y. Li, K. Matsushima, T. Baba, and N. Mukaida, “CCL3-CCR5 axis regulates intratumoral accumulation of leukocytes and fibroblasts and promotes angiogenesis in murine lung metastasis process,” The Journal of Immunology, vol. 181, no. 9, pp. 6384–6393, 2008.

L. Liu, J. Chen, X. Zhang et al., “Chemokine receptor 7 overexpression promotes mesenchymal stem cell migration and proliferation via secreting Chemokine ligand 12,” Scientific Reports, vol. 8, no. 1, 2018.

S. Rajan, H. C. P. Dang, H. Djambazian et al., “Analysis of early C2C12 myogenesis identifies stably and differentially expressed transcriptional regulators whose knock-down inhibits myoblast differentiation,” Physiological Genomics, vol. 44, no. 2, pp. 183–197, 2012.

R. Patel, J. D. Moffatt, E. Mourmoura et al., “Effect of reproductive ageing on pregnant mouse uterus and cervix,” The Journal of Physiology, vol. 595, no. 6, pp. 2065–2084, 2017.

P. Lv, S. Tian, C. Feng et al., “Maternal High Estradiol Exposure is Associated with Elevated Thyroxine and Pax8 in Mouse Offspring,” Scientific Reports, vol. 6, no. 1, 2016.

X. Tong, M. Muchnik, Z. Chen et al., “Transcriptional repressor E4-binding protein 4 (E4BP4) regulates metabolic hormone Fibroblast Growth Factor 21 (FGF21) during circadian cycles and feeding,” The Journal of Biological Chemistry, vol. 285, no. 47, pp. 36401–36409, 2010.

L. Wang, E. Koutelou, C. Hirsch et al., “GCN5 Regulates FGF Signaling and Activates Selective MYC Target Genes during Early Embryoid Body Differentiation,” Stem Cell Reports, vol. 10, no. 1, pp. 287–299, 2018.

J. Grisouard, T. Shimizu, A. Duek et al., “Deletion of Stat3 in hematopoietic cells enhances thrombocytosis and shortens survival in a JAK2-V617F mouse model of MPN,” Blood, vol. 125, no. 13, pp. 2131–2140, 2015.

M. S. Collado, B. R. Thiede, W. Baker, C. Askew, L. M. Igbani, and J. T. Corwin, “The postnatal accumulation of junctional e-cadherin is inversely correlated with the capacity for supporting cells to convert directly into sensory hair cells in mammalian balance organs,” The Journal of Neuroscience, vol. 31, no. 33, pp. 11855–11866, 2011.

V. K. Pulakazhi Venu, M. Saifeddine, K. Mihara et al., “Heat shock protein-27 and sex-selective regulation of muscarinic and proteinase-activated receptor 2-mediated vasodilatation: differential sensitivity to endothelial NOS inhibition,” British Journal of Pharmacology, vol. 175, no. 11, pp. 2063–2076, 2018.

G. Siddappa, S. Kulsum, D. R. Ravindra et al., “Curcumin and metformin-mediated chemoprevention of oral cancer is associated with inhibition of cancer stem cells,” Molecular Carcinogenesis, vol. 56, no. 11, pp. 2446–2460, 2017.

F. Mastroiacovo, S. Moyanova, M. Cannella et al., “Genetic deletion of mGlu2 metabotropic glutamate receptors improves the short-term outcome of cerebral transient focal ischemia,” Molecular Brain, vol. 10, no. 1, 2017.

S. Smith-Berdan, A. Nguyen, D. Hassanein et al., “Robo4 cooperates with Cxcr4 to specify hematopoietic stem cell localization to bone marrow niches,” Cell Stem Cell, vol. 8, no. 1, pp. 72–83, 2011.

N. Silver, S. Best, J. Jiang, and S. L. Thein, “Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR,” BMC Molecular Biology, vol. 7, article 33, 2006.

K. J. Livak and T. D. Schmittgen, “Analysis of relative gene expression data using real-time quantitative PCR and the method,” Methods, vol. 25, no. 4, pp. 402–408, 2001.

J. Xie, X. Liu, Y. Li, Y. Liu, and G. Su, “Validation of RT-qPCR reference genes and determination of Robo4 expression levels in human retinal endothelial cells under hypoxia and/or hyperglycemia,” Gene, vol. 585, no. 1, pp. 135–142, 2016.

J. Yang, Q. Yang, S. Yu, and X. Zhang, “Evaluation and validation of suitable reference genes for reverse transcription-quantitative polymerase chain reaction studies in cholangiocarcinoma patients and cell lines,” Oncology Letters, vol. 11, no. 4, pp. 2673–2681, 2016.

C. S. Thiel, Swantje Hauschild, Svantje Tauber et al., “Identification of Reference Genes in Human Myelomonocytic Cells for Gene Expression Studies in Altered Gravity,” BioMed Research International, vol. 2015, Article ID 363575, 20 pages, 2015.

A. Balogh, G. Paragh Jr., A. Juhász et al., “Reference genes for quantitative real time PCR in UVB irradiated keratinocytes,” Journal of Photochemistry and Photobiology B: Biology, vol. 93, no. 3, pp. 133–139, 2008.

F. Wang, J. Wang, D. Liu, and Y. Su, “Normalizing genes for real-time polymerase chain reaction in epithelial and nonepithelial cells of mouse small intestine,” Analytical Biochemistry, vol. 399, no. 2, pp. 211–217, 2010.

Q. Wu, X. Ma, K. Zhang, and X. Feng, “Identification of reference genes for tissue-specific gene expression in Panax notoginseng using quantitative real-time PCR,” Biotechnology Letters, vol. 37, no. 1, pp. 197–204, 2015.

X. Tang, H. Wang, C. Shao, and H. Shao, “Reference Gene Selection for qPCR Normalization of Kosteletzkya virginica under Salt Stress,” BioMed Research International, vol. 2015, 2015.

M. A. Ribeiro, M. B. dos Reis, L. N. de Moraes, C. Briton-Jones, C. A. Rainho, and W. R. Scarano, “Defining suitable reference genes for RT-qPCR analysis on human sertoli cells after 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) exposure,” Molecular Biology Reports, vol. 41, no. 11, pp. 7063–7066, 2014.

X. Xu, X. Liu, S. Chen et al., “Selection of relatively exact reference genes for gene expression studies in flixweed (Descurainia sophia) by quantitative real-time polymerase chain reaction,” Pesticide Biochemistry and Physiology, vol. 127, pp. 59–66, 2016.

Y.-X. He, Y. Zhang, Q. Yang, C. Wang, and G. Su, “Selection of suitable reference genes for reverse transcription-quantitative polymerase chain reaction analysis of neuronal cells differentiated from bone mesenchymal stem cells,” Molecular Medicine Reports, vol. 12, no. 2, pp. 2291–2300, 2015.