Evaluation and validation of the suitable control genes for quantitative PCR studies in plasma DNA for non‑invasive prenatal diagnosis

  • Authors:
    • Qiwei Yang
    • Hassan Abdellah Ahmed Ali
    • Shan Yu
    • Lin  Zhang
    • Xiuying Li
    • Zhenwu Du
    • Guizhen Zhang
  • View Affiliations

  • Published online on: September 23, 2014     https://doi.org/10.3892/ijmm.2014.1944
  • Pages: 1681-1687
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Abstract

Quantitative polymerase chain reaction (qPCR) is widely used in quantitation of plasma DNA for non‑invasive prenatal diagnosis (NIPD). Control genes are indispensable as standard normalizers in qPCR analysis, and there is increasing evidence indicating that the content levels of commonly used control genes vary significantly in different independent experiments. The commonly used control genes for DNA quantitation using qPCR in plasma DNA analysis are frequently chosen without any preliminary evaluation of their suitability. The present study aimed to examine a panel of six common control genes (HBB, TERT, GAPDH, ALB, ACTB and TRG) in order to evaluate and validate the most reliable control genes for qPCR studies in the quantitation of plasma DNA from pregnant and non‑pregnant females for NIPD. Plasma DNA was extracted from the peripheral blood of 18 pregnant females and 18 non‑pregnant females by the QIAamp DNA mini kit. qPCR followed by geNorm, NormFinder and BestKeeper based analysis was conducted to evaluate the DNA content stabilities of the six candidate control genes. DSCR3 was used to validate the result. The study recommended TERT and the combination of ACTB and TERT as the optimal control genes for qPCR studies on pregnant/non‑pregnant plasma DNA quantitation. Thus, the study reveals that the DNA content stability of widely used control genes varies significantly in pregnant and non‑pregnant plasma DNA.

Introduction

Quantitative polymerase chain reaction (qPCR) is the most fundamental, sensitive and common method used for quantitative analysis of DNA. However, its accuracy is influenced by a number of external and internal factors, including the amount of starting samples, the quality of templates and PCR efficiency (1). At present, using control genes as a standard normalizer is the most common method to minimize the effects (2). Control genes are commonly defined as genes that ubiquitously exist at stable levels in various biological contexts and are used to confirm the presence and content of DNA, as well as quantitatively measure the total DNA in each sample (3,4). However, accumulating evidence indicates that content levels of widely used control genes vary significantly in different independent studies (5,6).

Since the presence of cell-free fetal DNA in maternal plasma and serum was confirmed by the Lo et al (7) study in 1997, there are increasing studies focusing on the utilization of plasma DNA for non-invasive prenatal diagnosis (NIPD). Thus far, plasma DNA analysis is widely studied in numerous NIPD, including fetal gender detection, Rhesus blood group, D antigen (RhD) status determination, monogenic diseases and chromosomal aneuploidies prenatal diagnosis. To the best of our knowledge, the commonly used control genes for plasma DNA analysis are frequently chosen empirically and without any preliminary evaluation of their suitability. Thus, it is essential to compare and evaluate the content stability of each control gene prior to use for normalization in quantitative analysis of plasma DNA.

The focus of the present study is on the content stability of six commonly used control genes (HBB, TERT, GAPDH, ALB, ACTB and TRG) in pregnant and non-pregnant plasma DNA using qPCR. The candidate control genes were selected from previous studies on pregnant plasma DNA (811). Three common programs, geNorm (12), NormFinder (13) and BestKeeper (14), were used for data analysis. In order to confirm the analysis results, each of the candidate control genes was used as a normalizer to quantitatively measure the DSCR3 gene. The DSCR3 region only exists in chromosome 21 and it is supposed to have the same relative quantity in pregnant and non-pregnant groups of normal females (15,16). The result may reveal the optimal control gene selections for further quantitative studies on plasma DNA from pregnant females.

Materials and methods

Plasma sample collection and DNA extraction

A total of 2 ml peripheral blood donated from 18 pregnant females (gestational age, 12.87±1.25 weeks) and 18 non-pregnant volunteers was collected. A form of consent was obtained from each volunteer and the experiment was approved by the Ethical Committee of Second Hospital, Jilin University (Jilin, China). The blood samples were anti-coagulated by EDTA. The plasma supernatant was separated from the entire blood by centrifugation at 2,000 × g for 20 min at room temperature, followed by further centrifugation at 12,000 × g for 5 min to remove the residual intact cells. The supernatant was collected carefully. DNA was extracted from 350 μl plasma from each sample by the QIAamp DNA mini kit (Qiagen, Hilden, Germany) following the manufacturer’s protocol. The whole process was performed within 4 h of the withdrawal time.

qPCR analysis

qPCR was carried out using Roche LightCycler 480 [Roche Diagnostics (Schweiz) AG, Risch, Switzerland]. The primers of the control (HBB, TERT, GAPDH, ALB, ACTB and TRG) and two target genes (DSCR3 and SRY) were synthesized by Sangon Biotech Shanghai Co., Ltd., (Shanghai, China) (Table I).

Table I

Primer sequences, product sizes and PCR efficiency.

Table I

Primer sequences, product sizes and PCR efficiency.

SymbolGene namePrimers sequences (5′→3′)Amplicon size (bp)PCR efficiency
HBBβ-globin F-GTGCACCTGACTCCTGAGGAGA
R-CCTTGATACCAACCTGCCCAG
1012.58
TERTTelomerase F-GGTGAACCTCGTAAGTTTATGCAA
R-GGCACACGTGGCTTTTCG
972.00
GAPDH Glyceraldehyde-3-phosphate dehydrogenase F-GGACTGAGGCTCCCACCTTT
R-GCATGGACTGTGGTCTGCAA
1571.72
ALBAlbumin F-TGAAACATACGTTCCCAAAGAGTTT
R-CTCTCCTTCTCAGAAAGTGTGCATAT
801.79
ACTBβ-actin F-CCTGTACGCCAACACAGTGC
R-ATACTCCTGCTTGCTGATCC
2112.08
TRGT cell receptor γ F-AGGGTTGTGTTGGAATCAGG
R-CGTCGACAACAAGTGTTGTTCCAC
1601.82
DSCR3Down syndrome critical region-3 F-CAGTGCAATGACAGCAGTAT
R-TGGGATCACATCAAGCTAA
1412.11
SRYGender-determining region Y F-AAAGGCAACGTCCAGGATAGAG
R-CCACTGGTATCCCAGCTGCT
1372.19

[i] bp, basepair; PCR, polymerase chain reaction.

The reactions were performed in a 20 μl volume containing 8 ng DNA using the All-in-One qPCR Mix kit (GeneCopodia, Inc., Rockville, MD, USA) following the protocol. The amplification was as follows: An initial step of 95°C for 10 min, 50 cycles of 95°C for 15 sec, 58°C for 15 sec and 72°C for 30 sec. Each assay was performed in triplicate. qPCR results were subjected to 1% agarose gel electrophoresis. To estimate the efficiencies of amplification, a standard curve was generated for each primer pair based on four points of serial 2-fold DNA dilution. The efficiencies were calculated using the slope of the calibration curve following the equation: E=2−1/slope.

Data analysis

Microsoft Excel was used to calculate the mean and standard deviation (SD) values. The content stabilities of the six candidate-control genes were assessed by three commonly used programs: geNorm, NormFinder and BestKeeper, as described in their manuals. geNorm calculates a gene content stability measure (M) and pairwise variation (V) parameter. Lower M values represent higher content stability. V is calculated to determine the minimal number of control genes required. When V<0.15, the number of control genes is enough for valid normalization. NormFinder computes inter- and intra-group content stability values by an analysis of variance-based model. Lower value indicates higher content stabilities. BestKeeper analyses content stability based on SD and coefficient of correlation (r) of all the candidate control genes. The genes with SD >1.00 are suggested to be considered unreliable as a stable control gene and the remaining genes are ranked according to their r values, with a higher r value indicating higher stability. All the analyses were performed separately for the following three groups: Pregnant, non-pregnant and total sample (pregnant and non-pregnant) groups.

Control gene validation

DSCR3 was used as the target gene in order to validate the control genes for normalization of relative quantity in the pregnant and non-pregnant groups (17). The relative quantity in each sample was normalized by each of the six control genes and the most stable combination recommended by geNorm and NormFinder independently, using the 2−ΔΔCt method (18). SRY is only presented in pregnant females carrying male fetuses (19), and was used to detect whether or not the extracted DNA was contaminated with exogenous DNA.

Results

Amplification performance of primers

The qPCR amplification product was detected in 1% agarose gel electrophoresis. The results showed clear bands with expected size and no primer dimers (Fig. 1A). One single peak was obtained in each amplification reaction during the analysis of the melting curves; this confirmed the specific amplification of primers (Fig. 1B). The efficiencies of the primers are listed in Table I. SRY was only amplified in the pregnant group, indicating that there was no exogenous DNA contamination.

Amplification profile of candidate control genes

The amplification profile of the candidate control genes was estimated as Ct values. Fig. 2 shows the mean Ct values of each gene in the pregnant and non-pregnant groups. The Ct values of the groups varied between 25.99 to 32.66 for the pregnant group (Fig. 2A) and 28.02 to 34.09 for the non-pregnant group (Fig. 2B). ACTB exhibited the lowest Ct value (mean ± SD is 25.99±0.99 and 28.02±1.86) and GAPDH exhibited the highest Ct value (mean ± SD is 32.66±3.21 and 34.09±2.92) in the two groups respectively. In the pregnant group, GAPDH is the most variable with a high SD value (3.21), whereas ACTB had the lowest SD values (0.99). In the non-pregnant group, TRG was the most variable with a high SD value (4.13), whereas ALB had the lowest SD values (1.19). There was no significant difference of the Ct values between maternal- and fetal-derived DNA in each gene.

geNorm analysis

The geNorm analysis result revealed that ACTB and TERT were the most stable genes and GAPDH was the least stable among the total samples (Fig. 3A). Similar results were obtained in the pregnant group (Fig. 3B). In the non-pregnant group, HBB and ALB were the most stable genes and TRG was the least stable (Fig. 3C). None of the V values were below the cut-off value (Fig. 3D) indicating that there was no optimal combination number of control genes for normalization.

NormFinder analysis

The results of the NormFinder analysis showed that ACTB and TERT were the top two content stable genes in the total and pregnant groups, whereas HBB and ALB were the top two genes in the non-pregnant group (Table II). GAPDH and TRG were the least stable genes in the pregnant and non-pregnant groups, respectively, and TRG was also considered as the least stable in the total group. The best combination of two genes for total sample analysis was ACTB and TERT, and the stability value (0.224) was lower than TERT (0.340). This indicated that the combination of these two genes provide higher stability than using TERT alone.

Table II

Rank of six candidate control genes in order of their quantity stability calculated by NormFinder.

Table II

Rank of six candidate control genes in order of their quantity stability calculated by NormFinder.

Total samplePregnant groupNon-pregnant group



RankGeneStability valueGeneStability valueGeneStability value
1TERT0.340ACTB0.115HBB0.318
2ACTB0.418TERT0.299ALB0.419
3HBB0.462TRG0.439TERT0.782
4GAPDH0.552ALB0.928ACTB0.820
5ALB0.577HBB1.218GAPDH0.930
6TRG0.771GAPDH2.042TRG2.360
BestKeeper analysis

According to BestKeeper analysis, when considering the total samples, TERT was found to be the optimal control gene with SD<1.00 and the highest r value (0.870). In the pregnant group, ACTB and TERT were acceptable with SD<1.00, whereas ACTB had a higher r value (0.954) and was considered to be the most optimal control gene. In the non-pregnant group, TERT was the only gene with SD<1.00, but ALB had the highest r value (0.951). Although the SD of ALB was higher than 1.00 (SD=1.07), it was still considered to be a reliable control gene, similar to TERT (Table III). GAPDH and TRG were the least stable genes as shown by the results of geNorm and NormFinder in the pregnant and non-pregnant groups.

Table III

Descriptive statistics of six candidate control genes based on their cycle threshold values as calculated by BestKeeper.

Table III

Descriptive statistics of six candidate control genes based on their cycle threshold values as calculated by BestKeeper.

GroupCP dataACTBHBBTERTALBTRGGAPDH
Total samplesgeo Mean (CP)26.7499428.6791828.9439530.4278430.7450533.09142
Min (CP)24.6331125.2156927.1645925.0027827.4723226.64072
Max (CP)30.5861432.0680631.5733934.286939.4469838.16773
SD (± CP)1.3105031.6998490.9995351.3696841.9555112.533179
coeff. of corr. (r)0.8530.7540.8700.7760.7840.774
Pregnantgeo Mean (CP)25.9693928.467728.6177730.4084729.7907132.51344
Min (CP)24.6331125.2156927.1645925.0027827.4723226.64072
Max (CP)27.9123232.0680630.9721834.2869031.9253636.89494
SD (± CP)0.763511.8044390.8778341.5750631.1603672.65516
coeff. of corr. (r)0.9540.7110.8870.8880.8520.644
Non-pregnantgeo Mean (CP)27.9649628.9993629.4402130.4569132.2341933.97771
Min (CP)25.9806826.7366427.909328.9551529.1365629.93401
Max (CP)30.5861430.7080731.5733931.5124239.4469838.16773
SD (± CP)1.5457311.4190530.9834661.0664813.2798692.360286
coeff of corr (r)0.8140.9200.8150.9510.7850.948

[i] CP, cycle threshold; geo Mean (CP), geometric mean (CP); min, max (CP), extreme values of (CP); SD (± CP), standard deviation (CP); coeff of corr (r), pairwise correlation coefficient.

Validation of control genes

In order to verify the results of the control genes from geNorm, NormFinder and BestKeeper, the relative quantities of DSCR3 were determined using six candidate control genes and the combinations recommended by geNorm (ACTB + TERT) and NormFinder (HBB + ALB) were the normalizers (Fig. 4). GAPDH had the minimum difference between the two groups, followed by TERT. ACTB had the largest difference, although it was ranked as one of the top two in all three algorithms. TERT combined with ACTB provided a smaller difference between the two groups compared to ALB + HBB.

Discussion

The discovery of cell-free fetal DNA in maternal plasma has become a primary target for NIPD (7). In healthy gravidae, fetal DNA can be detected in maternal plasma as early as the seventh week after conception (20), and it increases with the pregnancy progresses (10), reaches the plateau in the ensuing three months, is promptly cleared from maternal plasma and disappears within 2 h of delivery (21). These properties caused plasma DNA to be the optimal material for NIPD. Thus far, qPCR is the most fundamental, sensitive and accurate method, widely used in studies of maternal plasma DNA. Due to its low cost and ease of use, a number of diseases, including gender determination (2224), β-thalassemia (2527), rhesus fetal blood group genotyping (2830) and aneuploidies diseases (31), have been successfully diagnosed by qPCR. Although it is an extremely useful technique, there are challenges concerned with its use. The most important is normalization with an accurate, reliable control gene. To avoid the incorrect analysis results caused by pipetting errors, inhibitory compounds, quality of starting material or other systematic errors in qPCR (32), control genes should be stably contained in pregnant and non-pregnant female plasma. Ideally, control genes in plasma should not be influenced or regulated by pregnancy conditions, stress response, stimulation or any other physiological or pathological states between different individuals (4,33). However, there is accumulating evidence indicating that content levels of widely used control genes varies significantly in different independent studies, for example, B2M, ACTB and SDHA showed significant variation in expression levels in human epileptogenic brain tissues (34), and the single-copy DNA control gene HBB, which is used for representing the cell number, has been proved to not be the most reliable control gene (3). Therefore, it has become indispensable to normalize the control gene quantity levels and determine reliable control genes prior to any qPCR relative quantitative analysis. To the best of our knowledge, this is the first study to evaluate the content stability of control genes commonly used in the plasma DNA from pregnant and non-pregnant females. In the present study, the samples in the second trimester of the gestational age were selected, as in this stage the content of plasma DNA is stable. Six commonly used control genes (HBB, TERT, GAPDH, ALB, ACTB and TRG) were analyzed by qPCR of the plasma DNA from pregnant and non-pregnant females. Three common statistical algorithms (geNorm, NormFinder and BestKeeper) were used for data analysis and DSCR3 was used to confirm the data analysis results.

On the basis of the results obtained from the three algorithms, the rank of the candidate genes stability was slightly different. These variations were possibly caused by different calculation algorithms (35,36) and indicated different features of the correlations between these control genes. Among the six candidate control genes, ACTB and TERT in the total samples and pregnant group, and HBB and ALB in the non-pregnant group showed the highest stability. This conclusion is consistent with the Ct values. ACTB, TERT and TRG had the lowest SD (0.99, 1.16 and 1.43) of the Ct values in the pregnant group; ALB, TERT and HBB had the lowest SD (1.19, 1.25 and 1.64) in the non-pregnant group. By contrast, GAPDH was unanimously affirmed as the least stable gene by the three algorithms in the pregnant group, as was TRG in the non-pregnant group. This corresponded to their high SD (3.21 and 4.13, respectively) for the Ct values, which means that they clearly vary.

In order to evaluate the exactitude of the control genes recommended by the three algorithms, the candidate control genes were used as a normalizer to detect the relative levels of the DSCR3 gene. The DSCR3 region only exists in chromosome 21, which is supposed to have the same relative quantity in the plasma DNA from pregnant and non-pregnant females. The content variance between the pregnant and non-pregnant groups was performed at maximum when using ACTB as the control gene, but minimum when using GAPDH. There is a slight discrepancy between the DSCR3 evaluation and algorithm results. When using TERT as the normalizer, the content variance is within the tolerable range. When combining more than one control gene as the normalizer, the best combination chosen was ACTB + TERT, suggested by geNorm, and HBB + ALB from NormFinder. The result reveals that ACTB + TERT obtain an improved result compared to using TERT alone.

The optimal number of control genes for normalization was indicated by the V value below the cut-off of 0.15, as shown in geNorm (12). However, as the result from geNorm showed, there was no optimal combination of the selected control genes below the cut-off value. It has been suggested that when conditions permit, three of the most stable control genes could be used instead of a single gene (37,38).

Furthermore, it is of note that the concentration of plasma DNA in plasma is extremely low (21) and highly originates from the apoptosis process (39,40). These characteristics influence the amplification efficiency of plasma DNA. For example, firstly, the amplificon sizes should be short enough, as the longer the template of the target gene is, the more opportunities there are to be digested in the apoptosis process, which reduces the effective templates. Secondly, the succesful amplification of every single target gene from plasma DNA cannot be guaranteed. There are increasing studies focusing further on the clinical application of plasma DNA, which is required for NIPD. However, to the best of our knowledge, all the control genes used in plasma DNA analysis are chosen by experience, and no evaluation has been performed to confirm the content stability of these control genes in the plasma DNA from pregnant and non-pregnant females. The present study validated the most content stable control genes at the second trimester of gestational age, which can be used as a criterion in subsequent studies.

In conclusion, the present study indicated that the content stability of control genes used for DNA showed significant variation in pregnant and non-pregnant plasma DNA. Every qPCR DNA study should commence with the selection of an appropriate control gene individually. According to the study, TERT and the combination of ACTB and TERT permit an efficient normalization for DNA quantitation using qPCR in pregnant and non-pregnant plasma, whereas GAPDH and TRG were proved to be the least reliable control genes.

Acknowledgements

The present study was supported by the Project supported by the Key Foundation of Jilin Provincial Science and Technology Department, China (nos. 20130727038YY and 20100942) and the Jilin Provincial Development and Reform Commission, China (no. 20101928).

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Yang Q, Ali HA, Yu S, Zhang L, Li X, Du Z and Zhang G: Evaluation and validation of the suitable control genes for quantitative PCR studies in plasma DNA for non‑invasive prenatal diagnosis. Int J Mol Med 34: 1681-1687, 2014
APA
Yang, Q., Ali, H.A., Yu, S., Zhang, L., Li, X., Du, Z., & Zhang, G. (2014). Evaluation and validation of the suitable control genes for quantitative PCR studies in plasma DNA for non‑invasive prenatal diagnosis. International Journal of Molecular Medicine, 34, 1681-1687. https://doi.org/10.3892/ijmm.2014.1944
MLA
Yang, Q., Ali, H. A., Yu, S., Zhang, L., Li, X., Du, Z., Zhang, G."Evaluation and validation of the suitable control genes for quantitative PCR studies in plasma DNA for non‑invasive prenatal diagnosis". International Journal of Molecular Medicine 34.6 (2014): 1681-1687.
Chicago
Yang, Q., Ali, H. A., Yu, S., Zhang, L., Li, X., Du, Z., Zhang, G."Evaluation and validation of the suitable control genes for quantitative PCR studies in plasma DNA for non‑invasive prenatal diagnosis". International Journal of Molecular Medicine 34, no. 6 (2014): 1681-1687. https://doi.org/10.3892/ijmm.2014.1944