- Research article
- Open Access
Selection of reference genes for qPCR in hairy root cultures of peanut
BMC Research Notes volume 4, Article number: 392 (2011)
Hairy root cultures produced via Agrobacterium rhizogenes-mediated transformation have emerged as practical biological models to elucidate the biosynthesis of specialized metabolites. To effectively understand the expression patterns of the genes involved in the metabolic pathways of these compounds, reference genes need to be systematically validated under specific experimental conditions as established by the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines. In the present report we describe the first validation of reference genes for RT-qPCR in hairy root cultures of peanut which produce stilbenoids upon elicitor treatments.
A total of 21 candidate reference genes were evaluated. Nineteen genes were selected based on previous qPCR studies in plants and two were from the T-DNAs transferred from A. rhizogenes. Nucleotide sequences of peanut candidate genes were obtained using their homologous sequences in Arabidopsis. To identify the suitable primers, calibration curves were obtained for each candidate reference gene. After data analysis, 12 candidate genes meeting standard efficiency criteria were selected. The expression stability of these genes was analyzed using geNorm and NormFinder algorithms and a ranking was established based on expression stability of the genes. Candidate reference gene expression was shown to have less variation in methyl jasmonate (MeJA) treated root cultures than those treated with sodium acetate (NaOAc).
This work constitutes the first effort to validate reference genes for RT-qPCR in hairy roots. While these genes were selected under conditions of NaOAc and MeJA treatment, we anticipate these genes to provide good targets for reference genes for hairy roots under a variety of stress conditions. The lead reference genes were a gene encoding for a TATA box binding protein (TBP2) and a gene encoding a ribosomal protein (RPL8C). A commonly used reference gene GAPDH showed low stability of expression suggesting that its use may lead to inaccurate gene expression profiles when used for data normalization in stress-stimulated hairy roots. Likewise the A. rhizogenes transgene rolC showed less expression stability than GAPDH. This study proposes that a minimum of two reference genes should be used for a normalization procedure in gene expression profiling using elicited hairy roots.
Analysis of mRNA transcript levels is used to study gene expression profiling in organisms. Different techniques are employed to achieve this goal. Among them northern blot and in situ hybridization  and RNAse protection assays  have been the most used. Even though these techniques have some disadvantages, they are all still in some use. Polymerase chain reaction (PCR) technology emerged in the 1980s and from that period different techniques were developed using this powerful and highly sensitive platform. Several of these PCR-based techniques were developed to address gene expression analysis. One of the most widely adopted procedure involved reverse transcription PCR (RT-PCR) [3, 4], where a reverse transcriptase reaction (generation of cDNA from RNA) is performed followed by a PCR and then the amplicons are visualized by gel electrophoresis . The main drawback of conventional RT-PCR is the analysis of the amplicons at the end-point (plateau level) of the PCR amplification and thus gene expression analysis is only semi-quantitative. In the early 1990s, real-time PCR technology using fluorescent dyes allowed the processes of amplification and detection of a target to be monitored in real-time. The MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines  refers to real time reverse transcription quantitative PCR as RT-qPCR, which is currently the most sensitive and widely used method for accurate determination of gene expression profiling. The advantage of this technique is that the kinetics of the reaction is measured in the early (exponential) phase of PCR, thereby providing higher sensitivity, reproducibility and a broader quantification range than previous molecular techniques [5, 7, 8]. Detection of amplification in qPCR is achieved when the fluorescence of a sample crosses the threshold (baseline above fluorescence background). The cycle at which the fluorescence from the sample crosses the threshold is called the quantification cycle (Cq). The concentration of the amplified gene will determine early or late Cq values for high or low expression of the gene, respectively.
Regardless of the technique used for gene expression analysis, data normalization is crucial to get accurate and reliable gene expression measurements. Normalization is used to correct variation associated with variability along the multistep experimental procedure (e.g. sample to sample variation, errors in sample quantification, RT-PCR efficiency). Different normalization strategies have been proposed  and, among them, the use of endogenous unregulated reference gene transcripts is the most common method [6, 10, 11]. Reference genes (previously known as housekeeping genes) are internal controls which are exposed to all sources of variations throughout the assay in the same way as the gene of interest. In an ideal scenario, the gene expression profile of a reference gene should not be influenced by the experimental conditions. However, many of the genes used as conventional reference genes for quantification of mRNA expression including glyceraldehyde-3-phosphate (GAPDH), α-tubulin (TUBA), β-actin (ACTB) and 18S ribosomal RNA (18S rRNA) have been shown to vary in expression levels under different experimental conditions [12–16] which leads to misrepresentation of target gene expression. Therefore, the normalization strategy using an internal control is greatly dependent on the reference gene employed and consequently the use of a reference gene without previous validation under the specific experimental conditions can lead to inaccurate data interpretation and to erroneous expression levels of target genes [6, 17].
To date there is not a well-defined and validated set of reference genes described for peanut (Arachis hypogaea) that show stable expression across a range of experimental elicited conditions. In the present study, we tested 21 candidate reference genes for qPCR. They were evaluated in a time course experiment under two abiotic stress (elicitor) conditions (NaOAc and MeJA) in peanut hairy root cultures. A schematic summary of this procedure is summarized in Figure 1. Validation of reference genes was done following the MIQE guidelines. To our knowledge, this is the first study done to validate reference genes in hairy root cultures.
Results and discussion
In recent years, concerns about validation of reference genes and reproducibility of qPCR experiments have increased. To this end, the MIQE guidelines [6, 18] recommend which information should be provided in a publication in order to make the qPCR experiments reproducible. Some key issues in the qPCR process are sample quality, PCR efficiency, number of reference genes used for normalization and validation of these reference genes. Quality, purity and quantification of RNA are important because they affect the entire RT-qPCR process [19–22]. PCR efficiency is calculated using different RNA concentrations and fluorescence values generated at specific RNA concentration. The fluorescence is due to a fluorescent dye (SYBR Green), which is in complex with the double strand cDNA. In an ideal scenario the efficiency should be 100%, it means that the amount of product is doubled with each PCR cycle. PCR efficiency determines the performance of the PCR assay which involves purity of the template and optimum PCR amplification conditions. In more detail, PCR efficiency is affected by the quality of the template (cDNA, DNA), template concentration, low expression of the gene, primer design (unspecific PCR products), cycle conditions [23–25]. Therefore, reporting detailed procedures across qPCR can guarantee that it can be reproduced by others.
In the last years a number of studies on the validation of reference genes have been done for different plant species [26–34]. Most of them used a list of reference genes from other plant species and tested them under their experimental conditions. Then software-based applications such as geNorm , NormFinder , BestKeeper  or qBase  were used to perform statistical identification of the best reference gene from a group of candidate genes in a defined set of biological samples.
Plants produce a wide range of phenolic compounds which are derived from the phenylpropanoid/acetate pathway . Many of these metabolites are produced by the plant under pathogen attack and appear to function as phytoalexins [40, 41]. Among this group of inducible phenolics are the stilbenes (also referred as stilbenoids), which recently have caught the attention of scientists because of their numerous health benefit properties [42–45]. Interestingly, stilbenes are found in non-related taxonomically plant species such as peanut and grapevine . Previously, our laboratory showed that peanut hairy root cultures are a good model system to study the biosynthesis of stilbenoids [47, 48]. To our knowledge only a single study was done with grape where reference genes were validated to study the expression profile of the stilbenoid metabolic pathway . In the case of peanut, few studies have been conducted using qPCR. In two of them, ubiquitin  and actin  were employed as reference genes. However, none of these studies validated their use. Sequencing of the genome of model plant species such as Arabidopsis has provided a starting point to identify homologous reference genes in other species where no genome sequence is available. Also, microarray data for Arabidopsis has been used to identify new reference genes. This study showed that conventional reference genes are often not a good choice .
Candidate reference genes
Previously we showed that abiotic stressors (also referred to as elicitors) can induce the production of stilbenoids in peanut hairy roots [47, 48]. To identify reliable reference genes to be used in gene expression studies in elicited hairy root cultures, a time course experiment was designed using 2 well described elicitors NaOAc and MeJA, shown to stimulate stilbenoid production. Nine time points (0, 1, 3, 6, 12, 24, 48, 72 and 96 h) per treatment and two non-elicited control points (0 and 24 h) were considered. Due to the unavailability of plant databases for qPCR reference genes, we searched the literature for qPCR reference genes used in plants (additional file 1). Six genes were classified as conventional reference genes (actin, tubulin, elongation factor, 18S rRNA, GAPDH and ubiquitin) because they are the most frequently used genes. Based on microarray data from Arabidopsis, Czechowski et al.  proposed new candidate reference genes, which later were validated in other plant species (additional file 1). Comparison of plant and animal reference genes showed that in most cases, the same genes were used. From the summary done based on the literature review (additional file 1), 19 reference genes were selected. These genes showed different roles in the cell that include cell structure, membrane proteins, transcription, protein translation, protein folding, protein degradation (proteasomal) and metabolic pathways. This selection was done in order to reduce the chance that the genes could be co-regulated and therefore involved in similar functions in the cell (Table 1). Also the idea of using transgenes as reference genes was explored. The hairy root phenotype is due to the expression of bacterial genes that are integrated into the plant nuclear DNA. In the case of Agrobacterium rhizogenes strain ATCC 15834 two clusters of genes are mainly responsible for the hairy root phenotype: the rol [51, 52] and aux  genes. From the cluster of rol genes, rolC was targeted because it was found to be expressed at higher levels than rolA and rolB genes in root tissue . Knowing that the T-DNA integration into nuclear DNA starts from the right border (RB) [55, 56], the aux1 gene was targeted as a reference gene because it is closer to RB on the TR-DNA and therefore it should have a better chance for the integration than aux2. Furthermore, the presence of these genes in the peanut hairy root line 3 used in this study was previously confirmed by PCR . In total, 21 candidate reference genes were evaluated (Table 1).
Identification of intragenic regions in the peanut sequences was done with the aim of designing qPCR primers to flank those regions. This strategy was useful to detect DNA contaminants in RT-qPCR. In this case, primers span intron region(s) that results in little to no amplification of genomic DNA template under qPCR conditions. This concept has been also used in previous studies [30, 57]. The Arabidopsis sequence was used as a model since gene annotations are available for this species. All the analyzed genes presented introns in their sequences with the exception of H3 and CYP1. The number of introns present in Arabidopsis sequence for each reference gene tested ranged from one (RPL8C and UBQ11) to 33 (HEL) (Table 1). On the other hand, amino acid identity between Arabidopsis and peanut of the 19 sequences had a mean value of 87.11% (SD = 10.21%). A 100% amino acid identity was found for H3 and UBQ11, whereas the lowest identity (70%) was present in TIP41 and HEL. In the current study, a higher amino acid identity for our selected genes was obtained when compared to a previous study in which amino acid from tomato was compared to Arabidopsis. In that study, amino acid identity between tomato and Arabidopsis ranged from 61.4 to 95% . In the case of potato during biotic and abiotic stress , the highest similarity between potato EST and Arabidopsis was 83%, at the nucleotide level. In the present study, primer pairs targeted a single gene for members of gene family (GAPDH and UBQ). This approach was different to the one used for qPCR primers in grapes , where a primer pair was designed to target more than one gene family member.
Peanut sequences obtained for the 19 candidate reference genes (after TBLASTN) belonged to contig sequences from a Transcriptome Shotgun Assembly (TSA) for Arachis hypogaea (Institute for Plant Breeding at University of Georgia and submitted to the GenBank in 2010). These assemblies derived from SRA (sequence real archive) and ESTs (expressed sequence tag).
Selection of RNA extraction method
RNA extraction methods can produce different RNA yields depending on the characteristics of the plant and/or tissue employed. In efforts to establish an optimal protocol for peanut hairy roots, two different methods of RNA extraction were evaluated: Maxwell® (Promega) and TRIzol® (Invitrogen). Maxwell® uses guanidine thiocyanate to lyse samples, denature nucleoprotein complexes and inactivate ribonucleases. This broader and selective binding of RNA to the resin enriches the RNA for template. The Maxwell® instrument uses plungers designed to capture and release coated paramagnetic particles attached to biomolecules (RNA) into wells of prefilled reagent cartridges. On the other hand, the principle of TRIzol® method which also uses guanidine isothiocyanate for inactivation of RNases, relies on acidic phenol/chloroform for the partitioning of RNA into an aqueous supernatant for separation. It has been reported that isolation of RNA from woody plants/tissues with high levels of polyphenols and polysaccharides is challenging [58, 59]. In this study, peanut hairy root tissue was employed as material for RNA extraction. Based on our experience, peanut hairy roots contain high levels of phenolic compounds. Therefore, β-mercaptoethanol (BME), which has been recommended for use in plants with high levels of phenols/tannins during DNA extraction, was tested in the Maxwell® procedure. The manufacturer also recommends BME for mammalian tissue with high levels of nucleases. In addition, the amount of lyophilized material used for RNA extraction was evaluated in order to determine the capacity of the system. For this purpose three amounts of starting material were tested: 10, 20 and 40 mg dry weight (DW).
As shown in Figure 2, the highest yield of RNA was obtained with the TRIzol® method (more than 2-fold when compared to Maxwell®), independent of the amount of starting material. In the case of TRIzol®, the amount of recovered RNA decreased when the amount of starting material (DW) increased, indicating that saturation of the system could be reached using 20 mg DW. Based on these results, we decided to use TRIzol® as the RNA extraction method, since it provided the best RNA yield. Another advantage of TRIzol® is that the time to complete the extraction procedure is faster than Maxwell®; although the latter uses an instrument to automate the processing of up to 16 samples, a pre-process of samples is required for RNA extraction.
Establishing quality control parameters for RNA samples
Assessment of all RNA samples quality is required to verify that templates for RT-qPCR are in good condition and of equivalent quality. The MIQE guidelines  include a section for reporting integrity and quality of RNA. In our study, lyophilized tissue was used for RNA expression analysis. Lyophilization (freeze-drying) has been reported to have an effect on RNA integrity . The lyophilization process is thought to limit or reduce degradation of cellular components by inactivation of proteolytic enzymes and nucleases. To test if our lyophilization procedure  affected peanut hairy root RNA integrity, we compared frozen versus lyophilized tissue, as starting materials for RNA isolation. In addition, we tested if elicitation conditions (NaOAc) also affected RNA integrity. Integrity of the RNA isolated from lyophilized and frozen elicited and non-elicited hairy root tissue was compared in Figure 3A. Agarose-formaldehyde gel electrophoresis resolved the two ribosomal RNA subunits (28S and 18S), showing intact RNA. As reported by others, no effect of the lyophilization process was found in this study [61, 62]. Also, we showed that there was no effect of elicitation treatment on RNA integrity. Compared to lyophilized tissue a 2-fold increase in RNA yield was obtained from frozen tissue (Figure 3B). The difference of RNA yield could be because 0.31 g (SD = 0.03) FW of lyophilized tissue was used instead of 0.1 g FW as used for frozen tissue. Therefore, the use of 0.3 g FW was beyond the capacity of the TRIzol® method for RNA extraction. RNA yields were significantly (p < 0.05) different between frozen and lyophilized tissue, in control and elicited samples. On the other hand, there was not significant (p < 0.05) difference between control and elicited samples, in frozen and lyophilized tissues.
It is important that RNA is free of contaminants such as proteins, DNA, cellular material, and reagents associated with RNA extraction (phenol, ethanol and salts). The impurity of RNA can affect the efficiency of RT-PCR resulting in reduced amplification. RNA purity was determined using the A260/A280 ratio. As shown in Figure 4, our samples had an A260/A280 ratio between 1.76 and 2.15. Values for the A260/A280 ratio had a mean value of 1.98 (SD = 0.06). Most of the samples were at the A260/A280 ratio of 1.96-2.00. All of the A260/A280 values fell in the range of 1.7 - 2.1 as recommended for qPCR by the manufacturer (Ambion®) (TechNotes 11, Applied Biosystems). The A260/A230 ratio also is used as indicator of chemical contaminants in nucleic acids. Polyphenols have been indicated as inhibitors in plant material for RT, PCR and qPCR . Some phenols have absorbance near 230 nm, which decrease the expected A260/A230 value of 2.0-2.2 (NanoDrop, Technical support bulletin T009). In the present study, the mean A260/A230 value was 0.56 (SD = 0.17) (additional file 2) which is lower than the expected value (~2). These low A260/A230 values can be justified because all the RNA samples were treated with TURBO DNA-free™, which indicates the presence of significant absorbance at ~230 nm due to the enhancer in the TURBO DNase Buffer (TURBO DNA-free kit, Ambion). Inhibitors are present in all RNA samples independent of the RNA purification method used. Whether a specific compound is considered inhibitor depends of the concentration at which it disturbs the qPCR reaction. Inhibition is related to high concentration of specific compounds. In qPCR, it is important to be below the inhibition range in order to avoid their effect .
Assessment of qPCR primer performance
A total of 21 genes were selected as candidates for reference genes in peanut hairy roots (Table 1). A pool of 6 samples from the time course experiment upon NaOAc treatment was used to run calibration curves with the 21 genes for qPCR using SYBR Green. After qPCR amplification, amplicons were analyzed on agarose gel to verify their product size (Figure 5). Most of the amplicons showed a single band of the expected size (Table 1). Only in the case of UBQ11 more than one band was observed. Consequently, melting curve identified two peaks (additional file 3), which correlated to the observations on the agarose gel. For the remaining genes, melting curve analysis was also performed and a single peak was observed (additional files 4, 5, 6, 7, 8 and 9). Figure 5 shows that intron prediction in peanut sequences for the 14 genes that contain introns was accurate, because a single expected band size by agarose gel electrophoresis was obtained. Amplification primers were tested using genomic DNA as template. Amplicons were longer than observed with a cDNA template, or no amplicon was observed for some genes (data not shown).
The melting temperature depends on base composition and length of the amplicon. In this study, average melting temperature (22 peaks, considering the second peak for UB11) was 80.39°C (SD = 2.579). Melting temperatures ranged from 77.50°C (SD = 0.000) for GAPDH to 88.03°C (SD = 0.129) for TUA3, indicating that amplicon sequences were different between the candidate reference genes. Melting curves with peaks lower than 78°C could indicate the presence of primer dimers in the reaction or alternatively smaller non-specific amplicon products or high AT-rich amplicons could produce lower melting temperatures. These analyses were performed using the data that was generated to determine PCR efficiencies. UBQ11 presented two peaks, one of them at 85.50°C (SD = 0.000), and the other at 78.63°C (SD = 0.226). For UBQ11 the presence of two peaks could indicate the presence of primer dimer or also the amplification of other mRNA sequence. In the remaining 20 genes no contaminating products (contaminating DNA or primer dimers) were present in the reaction, because no additional peak separate from the desired amplicon peak was observed. The amplicon size for ACT7 was 75 bp (Figure 5), which corresponded to the expected size if no genomic DNA was present (Table 1). If a contaminant genomic DNA was present in the template, an amplicon of 162 bp should be observed, but this was not the case (Figure 5). This result confirmed that no contaminant genomic DNA was present in our samples and that the treatment with DNase was effective in removing genomic DNA.
Efficiency of reference genes
Efficiency for each of the 21 candidate reference genes was calculated. Efficiency was determined to test the performance of the PCR assays, and then used as our first criterion to screen our set of genes. In this case, only primers that fell in the range 90 - 110% of efficiency and that showed an r2 ≥ 0.95 were considered for further analysis. Efficiency analysis was performed using qbasePLUS software. Table 2 shows that only 11 of the 21 genes had these requirements. There was a wide range of efficiencies from 83.5% for AT4G33380 to 602.1% for aux1. Three genes (TIP41, HEL and AT1G31300) out of the seven that came from the transcript array in Arabidopsis  were considered for further analysis. The gene with the closest 100% PCR efficiency was AT1G31300 (99.9%). This gene was selected from the supplementary information for stressed roots from the transcript array study in Arabidopsis, which showed less variation under different conditions . The function of AT1G31300 is unknown even in Arabidopsis and this is the first time where this gene has been used as candidate for reference gene selection in plants. From the 5 traditional genes used as reference gene (additional file 1), 2 genes (ACT7 and EFα1) were selected. From the transgenes, rolC was considered as reference gene but not aux1. Amplification of NTC was observed for 2 genes (UBQ11 and aux1). However, these genes were also excluded as candidates for reference genes, because their efficiencies were not in the range of 90 - 110%. In summary, we considered 11 genes (ACT7, TIP41, H3, TBP2, EFα1, RPL8C, EIF4A1, CYP1, HEL, AT1G31300, rolC) that met the expected requirements as already explained. Interestingly, when a calibration curve for GAPDH was done with 5 points (from 8 to 0.0128 ng) instead of 6 (from 40 to 0.0128 ng), it showed an efficiency of 95.6% and r2 = 0.986. Even though the range of template concentration was below the concentration (10 ng) used in the time courses; we chose to target GAPDH along with the previous 11 selected genes to establish those reference genes that are not impacted by treatment over time course used in our elicitation studies.
Variability of quantification cycle (Cq) values between treatments
It is important to consider several aspects of a RT-qPCR experiment when establishing normalization parameters. Typically, variability in qPCR comes from two sources: the model system (biological variance) and the work flow (technical variability). High variability can be present in living organisms (e.g. tissue complexity, type of tissue, genetic variability, environmental impact, and species), which needs to be controlled when carrying out quantitative analyses like RT-qPCR. On the other hand, compared to biological variance the technical procedures (e.g. sampling techniques, tissue storage, RNA purification method, RT reaction, qPCR reactions, real-time instrument performance, and normalization procedure adopted) typically show less variability and are easier to control. Identification of which variable is the source of most of the variability is important. We focused on the technical procedure (technical variability), in which many of the qPCR studies have used technical replicates at the qPCR level, i.e. after the RT process where each cDNA is loaded in replicate into the plate to run real-time PCR (technical replicates). Even though there is a contribution of variability at this level, most of the variability in a RT-qPCR experiment comes from the RT step . Statistically, it has been shown that qPCR replicates are not necessary because only technical variation is measured . Considering these facts, we decided to use replicates at the RT level for the 12 genes in our time courses; for statistical purposes 3 RT replicates were employed.
Quantitative PCR (qPCR) was performed for the two time course experiments (9 time points per treatment) using the 12 candidate reference genes selected. Inter-plate calibrator, which corrects for variation between runs due to instrument settings, was used. Comparison of Cq values for each candidate reference genes was not significantly (p > 0.01) different between the two treatments (Figure 6). Analysis of variance of Cq values for each gene between treatments was done assuming normal distribution along the data. Variation of Cq values was higher in the NaOAc than in the MeJA treatment. This showed that gene expression under MeJA treatment is less variable than under NaOAc over the defined elicitation time used in the study. Independent of the treatment, the highest Cq value (31.42 for NaOAc and 27.16 for MeJA) was observed for GAPDH; while, the lowest Cq value (19.89 for NaOAc and 18.51 for MeJA) was for rolC. This suggests that rolC was the gene with the highest levels of expression and expression levels of GAPDH was the lowest, in this study.
Variation of Cq values among the 33 samples for the 12 candidate reference genes is shown in Figure 7. Cq values among the samples varied less for the MeJA than the NaOAc treatment (Figure 6). It suggests that NaOAc treatment produced more changes in the expression levels of the analyzed candidate reference genes than MeJA, indicating that reference genes are regulated due to the treatment. Gene expression (Cq values) of the 12 genes varied across the time course experiments and also variation was observed against biological replicates (Figure 7). Variation between biological samples indicated that each individual responds in a unique manner under specific conditions. For reference gene selection the goal is to find a set of genes which have the lowest variation of gene expression between biological samples and under different conditions.
Establishing gene expression stability of candidate reference genes
We used two algorithms, geNorm  and NormFinder  for selecting the best reference gene for our peanut hairy root cultures upon NaOAc and MeJA treatments. We used these algorithms to analyze our data under three modes: samples only treated with NaOAc; samples only treated with MeJA; and the complete set of samples/treatments that included treatment with NaOAc, MeJA and control conditions.
a) geNorm analysis
GeNorm algorithm ranks the analyzed reference genes based on their expression stability (M), selecting an optimal pair of reference genes out of a larger group of candidate genes. The algorithm calculates and compares the M-value of the set of candidate genes, then eliminates the candidate gene with the highest M-value (more variation in gene expression) and repeats this procedure until two genes are left. This last pair of genes is recommended as the optimal pair of reference genes. This algorithm assumes that candidate genes are not co-regulated. As shown on Figure 8, the order of the ranking of gene stability was different between NaOAc and MeJA. The ranking of NaOAc for stable expression levels gave to HEL > PRL8C > TBP2 > CYP1 > TIP41 as the best five reference genes (Figure 8A). HEL and RPL8C were the most stable pair of reference genes under NaOAc treatment over the time course with M-value of 0.43. On the other hand, the gene with the least stable expression was H3 (M = 1.07). In the case of MeJA treatment time course, the ranking of reference genes was TIP41 > HEL > PRL8C > TBP2 > CYP1 > EFα1 as the best five reference genes (Figure 8B). In this case, the best pair of reference genes was TIP41 and HEL with M-value of 0.32; while the least stable expression was associated with rolC (M = 0.70) gene. Comparison of M-values for best pair of reference gene under each elicitation treatment showed that under MeJA more expression stability (less M value) was obtained even for the same gene (HEL). This was also observed for the 11 remaining reference genes; which correlated the difference in variation between treatments (Figure 6). Potato exposed to different stress conditions also showed that expression stability (M) values were dependent of the stress condition .
When all the treatments (controls and elicitors) were analyzed their combined expression stability ranking showed that the best five reference genes (TBP2 > PRL8C > HEL > TIP41 > CYP1) (Figure 8C) were the same genes observed under NaOAc and MeJA treatment. This order of gene ranking was more similar to the MeJA treatment. The best pair of reference genes was TBP2 and RPL8C (Figure 8C) with M-value of 0.45, and the least stable reference gene was rolC (M = 0.95). The M-value of 0.45 was the highest observed for the best pair of reference genes, compared to NaOAc (0.43) and MeJA (0.32). GeNorm considers high reference target stability for M-values ≤ 0.5 (for homogeneous samples, e.g. untreated cell culture). In all samples/treatments analysis the first three genes (TBP2, PRL8C and HEL) with low M-values fell into this category.
b) NormFinder analysis
NormFinder also calculates an expression stability value (standard deviation) for the candidate reference genes. This method is similar to geNorm in that a low value of SD represents a more stable expression of the gene. This algorithm attempts to identify the optimum reference gene in a group of candidate genes. In contrast to geNorm, NormFinder produces a ranking of the genes taking information from their estimated intra- and intergroup variations. Analysis using this algorithm was performed for NaOAc (Figure 9A), MeJA (Figure 9B) and for all treatments (controls and elicitors) (Figure 9C). The ranking of reference genes for NaOAc gave as the best five genes to EFα1 > ACT7 > TBP2 > CYP1 > AT1G31300 (Figure 9A). The best reference gene was EFα1 with a SD-value of 0.32 and the gene with the least stable expression was H3 (SD = 1.33). For MeJA treatment the best five reference genes were TBP2 > HEL > ACT7 > EFα1 > AT1G31300 (Figure 9B). In this case, TBP2 was the best reference gene with a SD-value of 0.25 and the gene with the least stable expression was rolC (SD = 1.07). The ranking for reference genes between NaOAc and MeJA was different. Similar to the finding when geNorm was applied, SD values for MeJA were lower than NaOAc suggesting that less variability was observed under MeJA treatment. When the analysis was performed for all the treatments (controls and elicitors), the best five reference genes were EFα1 > ACT7 > TBP2 > AT1G31300 > PRL8C (Figure 9C). The best reference gene was EFα1 with a SD-value of 0.35 and the genes with the least stable expression were H3 and rolC (SD = 1.12). No optimal SD-values for NormFinder have been specified. This order of the ranking of reference genes (all treatments) was similar to the NaOAc treatment.
c) Identifying most optimal reference genes based on cumulative geNorm and NormFinder analyses
The difference on selection of reference gene between these two algorithms is that when geNorm eliminates the gene with the largest variation, after selection procedure, also it does not consider the Cq values from this gene for further analysis. The implications of this are that different sizes of groups are analyzed across the procedure thus recalculating the standard deviation at each step. This results in the standard deviation being different after each elimination process of the gene with the largest variation. In contrast, NormFinder is based on ANOVA (analysis of variance); which considers all the Cq values, from the genes analyzed, through the entire process of selection of reference gene.
The best reference genes considering all treatments (controls and elicitors) were TBP2 and RPL8C, and EFα1 for geNorm (Figure 8C) and NormFinder (Figure 9C), respectively. TBP2 and RPL8C were placed in the middle top on NormFinder ranking (third and fifth, respectively). In the case of EFα1, it placed sixth on the geNorm ranking. It is interesting to note that TBP2, RPL8C and EFα1 placed in the top or middle of the ranking in previous studies in which different plant tissue (leaf and root) , developmental stages [28–30, 33] or stress conditions (biotic and abiotic)  were employed. EFα1 has been shown to be one of the most stable reference genes in qPCR [27–29, 33] and our study confirmed that. Although RPL8C is not a conventional reference gene, it has been used as reference gene in humans . This gene was placed in the middle of the ranking of reference genes in prior studies [27, 30]. Interestingly, when stress conditions (late blight exposure, cold stress and salt stress) were evaluated in potato for validation of reference genes, RPL8C was ranked on the top middle . This suggests that RPL8C may prove to be a good target for other plant stressors on other plant species. On the other hand, TBP2 was used in a previous study, in which it placed the fourth position in the ranking of reference genes .
GAPDH is commonly used as reference gene in animals [67, 68] and plants . In peanut hairy roots, GAPDH was shown to be one of the least stable expression reference genes tested (Figures 8 and 9) and this was independent of the algorithm used. This has been shown to be the case as well for other plant RT-qPCR studies [13, 15, 30, 33]. Together these results reinforce the concept that some conventional reference genes are not good choices for normalization of gene expression and the importance of always confirming your reference genes for the specific system you are studying. Of the 21 genes screened, H3 and rolC showed greater variation in expression levels using either algorithm.
Optimal number of reference genes for normalization
Normalization procedure for quantification of gene expression is crucial for accuracy of expression levels of target genes. The most common method for normalizing mRNA data is the use of reference genes. However, the choice of reference gene and how many are needed for normalization is a concern. In order to determine the optimal number of reference genes for our hairy root system, two algorithms were analyzed. The first one was calculated by geNorm (pairwise variation: V) (Figure 10A) and the second one was calculated by NormFinder (accumulated SD) (Figure 10B).
Pairwise variation (V) is calculated based on normalization factor values (NFn and NFn+1) after the inclusion of a least stable reference gene and indicates if the extra reference gene adds to the stability of the normalization factor. A threshold V-value of 0.15 is recommended by qbasePLUS software as optimal to determine the minimum number of reference genes. In the present study, the analyses used suggest a minimum of two reference genes (TBP2 and RPL8C) were needed to be below the V-value of 0.15. The lowest V-value (0.073) was obtained when the addition of the 8th most stable gene (ACT7) was done (Figure 10A). Addition of more genes increased the V-values, indicating that those least stable reference genes will negatively impact the normalization process. Our results have shown that only two reference genes are needed to be below the threshold value. Previous studies in which determination of optimal number of reference genes was done showed that even using four  or seven  of their best reference genes, the V-values were not below the threshold V-value.
Accumulated SD is an indicator of the optimal number of reference genes. The optimal number of reference genes described by NormFinder was 8 (Figure 10B), taking into account those genes up to the 8th most stable reference gene (HEL) (Figure 9C). Similar to pairwise variation, addition of the least stable reference genes will increase the variability, which must be avoided. Often, using 8 reference genes for normalization procedures is not experimentally practical (use of more reagents and time consuming). In this case consideration must be taken to determine how much accumulated (acc.) SD decreases when an additional reference gene is added to the normalization procedure. For example, in Figure 10B when one reference gene is used the acc. SD was 0.345. This value dropped to 0.258 when the second reference gene was added, a difference of 0.087 of acc. SD. When the third reference gene is considered, the acc. SD decreased to 0.221 (a difference of 0.037). However, when the fourth reference gene is added acc. SD only decreased in 0.015. The "rate of change" in SD between each gene when no longer increasing or maintain is generally a good indicator that the number of reference genes required has been achieved. Therefore, the first three most stable reference genes could be used for normalization instead of 8 genes, because the most acc. SD is eliminated with the first 3 reference genes.
It is also important to consider the other sources of variation in the qPCR process. One possible contributor is the real-time PCR instrument. In the case of this block cycler, the well-to-well variation (SD) has been calculated to be ± 0.20 by the manufacturer (CFX384™ Real-time detection system, Bio-Rad). Considering that a variation (SD) of 0.20 is present when the qPCR instrument is used, there is no reason in adding more than 4 reference genes (in this study, Figure 10B) for normalization if there is a "default" variation during the qPCR process due to the instrument.
This study provides the first validation of reference genes for RT-qPCR in hairy root cultures. Selection of an appropriate RNA extraction method to yield adequate amounts of RNA for RT-PCR was critical. Twenty-one candidate reference genes were measured in peanut hairy root cultures treated with two elicitors (NaOAc and MeJA). Due to poor PCR efficiencies, nine of the 21 genes were discarded. Analysis of the relative expression stability of reference genes using geNorm and NormFinder resulted in different reference genes being designated as lead targets. However, overall the TBP2 was the most stable across both elicitation NaOAc and MeJA treatments, followed by RPL8C. TBP2 is a non-traditional reference gene and we recommend testing its utility for not only normalization of gene expression measurements in peanut hairy roots under stress conditions as well as possibly other plant stress conditions. Interestingly, TBP2 is a TATA binding protein required for basal transcription in the cell. It functions as a transcription factor that binds to DNA sequence known as TATA box during the transcription process, thus having a steady state level of expression in the cell under different conditions. In addition, data analysis showed that the evaluated genes had more variation after NaOAc than MeJA treatment. The transgene (rolC) was also evaluated as reference gene and found to be one of the genes with low expression stability. The minimum number of reference genes for normalization was calculated to be two genes (TBP2 and RPL8C) using geNorm and three genes (EFα1, ACT7 and TBP2) using NormFinder. It is possible that the other genes that were eliminated from the analysis based on PCR efficiency may be good candidates as reference genes if qPCR primers were redesigned in other regions of the target gene sequence. Such assessment will be facilitated with the full sequence of the peanut genes analyzed which currently is not available. In future studies using peanut hairy root elicited with NaOAc or MeJA, TBP2 and RPL8C are recommended to be used as reference genes.
Plant material and elicitor treatments
All experiments were conducted with hairy root cultures of peanut (Arachis hypogaea) cv. Hull line 3 . Briefly, the cultures were established by direct inoculation of stem explants with Agrobacterium rhizogenes strain ATCC 15834. Hairy roots developed at the inoculation site after 2 weeks. Line 3 used in this study derived from one initial hairy root that developed at the Agrobacterium inoculation site and was clonally propagated using root tips. The hairy root line was maintained by subculturing 10 root tips into 250 mL flasks containing 50 mL of a modified MS medium (MSV) as previously described . At day 9 of culture (mid-exponential growth stage), the spent medium was replaced with fresh MSV medium containing as elicitors 10.2 mM NaOAc  or 100 μM MeJA. As controls, the medium of 9-day cultures was replaced with MSV medium without elicitor. The tissue was collected at 0, 1, 3, 6, 12, 24, 48, 72 and 96 h after elicitor treatment (NaOAc and MeJA), then frozen at -80°C and lyophilized as previously described .
Total RNA was extracted from lyophilized tissue with TRIzol® (ratio of 20 mg DW tissue to 1 mL solvent) (Invitrogen) according to manufacturer's procedure. After extraction, the RNA was dissolved in 50 μL of DNase/RNase-free distilled water (ultraPURE™, Gibco). Genomic DNA contamination was eliminated by treating the RNA with TURBO DNA-free™ (Applied Biosystems).
RNA concentration was determined using Quant-iT™ RiboGreen® RNA kit (Invitrogen) using the following modified method for a 96-well microplate format. A standard curve was generated using seven serial dilutions (1:2). The concentrations ranged from 1 μg/mL to 15.63 ng/mL. One hundred μL of standard or sample and 100 μL of diluted Quant-iT™ RiboGreen® reagent were used in 200 μL of assay volume. Standards were run in triplicate on each plate. Samples were analyzed at two concentrations (dilution 1:5). Detection was done by fluorescence (excitation at 485 nm) and emission at 520 nm) using a POLARstar OPTIMA microplate reader (BMG Labtech).
Purity of the total RNA extracted was estimated from the ratio of absorbance readings at 260 and 280 nm using a NanoDrop™ 800 spectrophotometer (Thermo Scientific).
Comparison of RNA extraction methods
Total RNA extracted with Maxwell® 16 Total RNA Purification kit (Promega) was done according to the manufacturer's procedure described for plant tissue samples with the following modifications for lyophilized tissue. Samples (10, 20 or 40 mg DW) were treated with 500 μL of Lysis buffer. After lysis step, the lysate volumes obtained were 500, 400 and 300 μL for 10, 20 and 40 mg, respectively. The amount of Blue RNA Dilution Buffer added to the sample lysate was according to manufacturer's procedure. The same amount of Clearing Agent (125 μL) was added independent of the amount of DW used. Alternatively, BME was added into the Lysis Buffer in the Maxwell® procedure. Then, samples were loaded into Maxwell® 16 Total RNA Purification cartridge (Promega) and processed using the Maxwell® 16 MDx instrument (Promega) following the default protocol for RNA extraction. Contaminating genomic DNA was eliminated through the Maxwell® 16 Total RNA Purification kit.
Total RNA quantification was done using Quant-iT™ RiboGreen® RNA kit, as previously described. Final volumes of pure RNA were 50 and 220 μL for TRIzol® and Maxwell®, respectively.
Nine-day peanut hairy root cultures were elicited with NaOAc 10.2 mM . Control cultures did not include NaOAc. After 3 hours of treatment the roots were frozen with liquid nitrogen and either stored at -80°C or lyophilized as previously described . Three biological replicates per treatment/storage were considered. Total RNA extraction was done with TRIzol® as previously described, except that RNA was treated with RQ1 RNase-Free DNase (Promega). As starting material, 40 mg and 100 mg were used of lyophilized and frozen tissue, respectively. Two technical replicates of RNA extraction were done. RNA was quantified by absorbance at 260 nm using a spectrophotometer (ND-1000, Nanodrop®). RNA was run on 1.2% agarose gel electrophoresis containing 2.2 M formaldehyde as previously described . Technical replicates of RNA extractions were pooled and an average of their concentrations was considered for further analysis. Two μg of RNA per sample were loaded on agarose gel. SYBR® Gold NucleicAcid Gel Stain (Invitrogen) was used to visualize RNA following the manufacturer's procedure.
Selection of reference genes
Twenty-one candidate reference genes were selected as shown in Table 1. These genes were involved in different functional classes in the cell. This group of genes comprised five commonly used reference genes: TUA3 (α-tubulin), ACT7 (actin 7), EFα1 (elongation factor α1), GAPDH (glyceraldehyde-3-phosphate dehydrogenase) and UBQ11 (ubiquitin 11). Less common reference genes such as H3 (histone H3), TBP2 (TATA binding protein 2), RPB1 (RNA polymerase II large subunit), RPL8C (60S ribosomal protein L8), EIF4A1 (eukaryotic translation initiation factor 4A1), CYP1 (cyclophilin) and APT1 (adenine phosphoribosyltransferase) were also included.
The set of candidate reference genes also included less conventional reference genes which expression levels showed to be more stable in an analysis of microarray data-sets from Arabidopsis . These genes included: TIP41 (Tip41-like family protein), SAND (SAND family protein), AP47 (clathrin-associated protein), AT4G33380 (expressed sequence), PP2AA3 (protein phosphatase 2A subunit A3), HEL (helicase domain-containing protein) and AT1G31300 (expressed sequence). Most of these genes also showed stable expression in other plant species [29, 30].
To identify peanut homologous sequence to the candidate reference genes, TBLASTN analysis from the National Center for Biotechnology Information (NCBI) was used. A query for Arachis hypogaea (taxid3818 [ORGN]) nucleotide sequences [nucleotide collection (nr/nt)] with Arabidopsis protein sequences was performed. In the case of H3, first a homologous gene in Arabidopsis was obtained using Ziziphus jujuba protein sequence [GenBank: ACG70966] . Although a similar approach was followed for the RPB1 sequence, a human (Homo sapiens) protein sequence [GenBank: NP_000928] was used. Due to lack of a peanut sequence that matched the Arabidopsis homologous locus AT4G38710  (additional file 1); another homologous gene to Arabidopsis was obtained using Oryza sativa protein sequence for EIF4A1 [GenBank: BAG93556.1] .
Transgenes that harbor the T-DNA from Agrobacterium rhizogenes were also included in the set of candidate reference genes: rolC (root loci C gene) and aux1 (tryptophan oxygenase gene). Sequences for rolC and aux1 genes were obtained from the GenBank (NCBI) (Table 1).
Protein coding gene models for all the candidate reference genes, except rolC and aux1, were obtained for Arabidopsis thaliana from the TAIR (The Arabidopsis Information Resource) web site http://www.arabidopsis.org. In each case the latest version or the most conserved gene model was used. Arabidopsis nucleotide sequences for mRNA, genomic DNA (gDNA) and amino acid sequence were downloaded from the GenBank (NCBI) (Table 1). An alignment between the gDNA and mRNA (for the same gene) for Arabidopsis sequences was done using AlignX (Vector NTI®) with the purpose of localizing intron regions. Intron regions were double-checked manually to confirm that they follow the GT-AG, CC-AG or AT-AC intron rules . Based on the protein coding gene model for Arabidopsis, two genes did not present introns in their sequences: H3 and CYP1. The peanut sequence obtained for each gene from TBLASTN was aligned against the previous alignment between mRNA - gDNA (Arabidopsis sequence) to determine exon position in the peanut sequences.
SYBR® green primers for qPCR were designed using AlleleID® 7 software (Premier Biosoft). Primers flanking intron regions were designed, when gene sequence contained introns. Secondary DNA structures have been shown to affect PCR efficiency , therefore peanut sequences were analyzed for secondary structure to avoid these regions for primers design. For sequences longer than 1200 bp the "mfold" website http://mfold.rna.albany.edu/?q=mfold/DNA-Folding-Form was used. The structures were downloaded in "rnaml" format and loaded into AlleleID® 7. Gene sequences were blasted against nr database (GenBank, NCBI) to determine cross homology with other sequences (target specificity). After these analyses were done for each of the 21 candidate reference gene, primers were designed for each gene.
Two-step RT-qPCR was performed using SYBR Green detection chemistry. cDNA was synthesized from 1 μg of total RNA and oligo(dT) primers, using iScript™ Select cDNA Synthesis kit (Bio-Rad), following the manufacture's procedure. Volume of RNA treated with TURBO DNA-free™ was less than 25% of the final RT-PCR volume; because no more than 40% of the final volume can be used, otherwise some inhibition of the qPCR reactions might occur according to manufacturer's recommendation. Quantitative real-time PCR reactions were prepared in a total volume of 10 μL containing: 4 μL of template (10 ng) and 6 μL of master mix [0.4 μL of each primer (0.8 μM, final concentration), 5 μL of iQ™ SYBR® Green Supermix (Bio-Rad) (1X, final concentration) and 0.2 μL of ultraPURE™ water]. Primers were synthesized by Invitrogen. Pipetting was performed using the epMotion 5075 (Eppendorf) on Hard-Shell® Thin-Wall 384-Well Skirted PCR plates (Bio-Rad) sealed with Microseal® 'B' adhesive seal (Bio-Rad). Technical samples were run in triplicate at the RT level. Non-template controls (NTC) were run in three technical replicates. All qPCRs were performed using the CFX384™ Real-time PCR Detection System (Bio-Rad). The following amplification program was used: denaturation at 95°C for 3 min, 40 cycles of amplification (95°C for 10 s, 60°C for 30 s) and a melting curve program (from 65°C to 95°C, with an increment of 0.5°C for 5 s). Data were collected using the CFX Manager 2.0 (Bio-Rad). Minus reverse transcription (-RT) control, which assesses the presence of genomic contamination in the sample, was not used in this study. Genomic DNA was eliminated with DNase treatment. To confirm that no contaminant genomic DNA was present after DNase treatment, analysis of ACT7 was done. Primers for ACT7 were designed flanking an intron of 87 bp. As shown in Figure 5 the size of the amplicon corresponded to the predicted cDNA size (Table 1), indicating no presence of contaminating DNA.
For PCR efficiencies a pool of 6 samples from the time course experiment were used (3 elicited and 3 control samples). RNA extraction and their treatment with DNase were made separately for each sample as already detailed. RNA was quantified for each sample with Quant-iT™ RiboGreen® RNA kit, and RNA samples were pooled, quantified and used for cDNA synthesis as previously described. The cDNA used as PCR template was in a range of 40, 8, 1.6, 0.32, 0.064 and 0.0128 ng (1:5 dilution series). Quantitative PCR reactions and conditions were as described above, except that 45 cycles were run instead of 40 cycles. Quantitative PCR amplifications were run using three technical replicates. NTC was run in three technical replicates per gene. Efficiency (E) for each gene was determined with the slope of a linear regression model  using the Cq values and the following equation was used:
The sample used for assessing PCR efficiency (1.6 ng) with the RPL8C gene was employed as an inter-plate calibrator when the 12 sets of primers were run. The Cq value of the inter-plate calibrator was ~ 25 and it was run in three technical replicates per plate. This sample was selected from data generated for PCR efficiency.
PCR product sizes were checked on 3% agarose gel and ethidium bromide staining. Melting curves were analyzed for each gene using CFX Manager Version 2.0.; qPCR efficiency between 90 and 110%, r2 ≥ 0.95, a single peak in the melting curve were requirements for considering a gene as a good candidate.
Analysis of gene expression stability
To analyze gene expression stability and rank, geNorm  and NormFinder  algorithms were used. They were included on GenEx (multid) software. GeNorm V (pairwise variation) was performed using qbasePLUS Version 1.5.
Cq value comparison of the genes between treatments was calculated with ANOVA using a Bonferroni correction and a significance cut-off of 0.01. Comparison of RNA yields was calculated with ANOVA using the Tukey's test with a significance cut-off of 0.05. These analyses were performed using GraphPad Prisma® software.
expressed sequence tag
minimum information for publication of quantitative real-time PCR experiments
no template control
polymerase chain reaction
quantitative real-time PCR
reverse transcription PCR
reverse transcription quantitative real-time PCR
sequence real archive
Parker RM, Barnes NM: mRNA: detection by in Situ and northern hybridization. Methods Mol Biol. 1999, 106: 247-283.
Hod Y: A simplified ribonuclease protection assay. Biotechniques. 1992, 13: 852-854.
Tan SS, Weis JH: Development of a sensitive reverse transcriptase PCR assay, RT-RPCR, utilizing rapid cycle times. PCR Methods Appl. 1992, 2: 137-143.
Weis JH, Tan SS, Martin BK, Wittwer CT: Detection of rare mRNAs via quantitative RT-PCR. Trends Genet. 1992, 8: 263-264.
Bustin SA: Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol. 2000, 25: 169-193. 10.1677/jme.0.0250169.
Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL, et al: The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009, 55: 611-622. 10.1373/clinchem.2008.112797.
Pfaffl MW, Hageleit M: Validities of mRNA quantification using recombinant RNA and recombinant DNA external calibration curves in real-time RT-PCR. Biotechnology Letters. 2001, 23: 275-282. 10.1023/A:1005658330108.
Ding C, Cantor CR: Quantitative analysis of nucleic acids--the last few years of progress. J Biochem Mol Biol. 2004, 37: 1-10. 10.5483/BMBRep.2004.37.1.001.
Huggett J, Dheda K, Bustin S, Zumla A: Real-time RT-PCR normalisation; strategies and considerations. Genes and immunity. 2005, 6: 279-284. 10.1038/sj.gene.6364190.
VanGuilder HD, Vrana KE, Freeman WM: Twenty-five years of quantitative PCR for gene expression analysis. Biotechniques. 2008, 44: 619-626. 10.2144/000112776.
Vandesompele J, Kubista M, Pfaffl MW: Reference gene validation software for improved normalization. Real-time PCR: Current Technology and Applications. 2009, 47-64.
Radonic A, Thulke S, Mackay IM, Landt O, Siegert W, Nitsche A: Guideline to reference gene selection for quantitative real-time PCR. Biochem Biophys Res Commun. 2004, 313: 856-862. 10.1016/j.bbrc.2003.11.177.
Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible WR: Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol. 2005, 139: 5-17. 10.1104/pp.105.063743.
Ferguson BS, Nam H, Hopkins RG, Morrison RF: Impact of reference gene selection for target gene normalization on experimental outcome using real-time qRT-PCR in adipocytes. PloS one. 2010, 5: e15208-10.1371/journal.pone.0015208.
Maroufi A, Van Bockstaele E, De Loose M: Validation of reference genes for gene expression analysis in chicory (Cichorium intybus) using quantitative real-time PCR. BMC Mol Biol. 2010, 11: 15-10.1186/1471-2199-11-15.
Dang W, Sun L: Determination of internal controls for quantitative real time RT-PCR analysis of the effect of Edwardsiella tarda infection on gene expression in turbot (Scophthalmus maximus). Fish Shellfish Immunol. 2011, 30: 720-728. 10.1016/j.fsi.2010.12.028.
Dheda K, Huggett JF, Chang JS, Kim LU, Bustin SA, Johnson MA, Rook GA, Zumla A: The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Anal Biochem. 2005, 344: 141-143. 10.1016/j.ab.2005.05.022.
Bustin SA, Beaulieu JF, Huggett J, Jaggi R, Kibenge FS, Olsvik PA, Penning LC, Toegel S: MIQE precis: Practical implementation of minimum standard guidelines for fluorescence-based quantitative real-time PCR experiments. BMC Mol Biol. 2010, 11: 74-10.1186/1471-2199-11-74.
Perez-Novo CA, Claeys C, Speleman F, Van Cauwenberge P, Bachert C, Vandesompele J: Impact of RNA quality on reference gene expression stability. Biotechniques. 2005, 39: 52, 54, 56-
Fleige S, Pfaffl MW: RNA integrity and the effect on the real-time qRT-PCR performance. Mol Aspects Med. 2006, 27: 126-139. 10.1016/j.mam.2005.12.003.
Fleige S, Walf V, Huch S, Prgomet C, Sehm J, Pfaffl MW: Comparison of relative mRNA quantification models and the impact of RNA integrity in quantitative real-time RT-PCR. Biotechnol Lett. 2006, 28: 1601-1613. 10.1007/s10529-006-9127-2.
Becker C, Hammerle-Fickinger A, Riedmaier I, Pfaffl MW: mRNA and microRNA quality control for RT-qPCR analysis. Methods. 2010, 50: 237-243. 10.1016/j.ymeth.2010.01.010.
Platts AE, Johnson GD, Linnemann AK, Krawetz SA: Real-time PCR quantification using a variable reaction efficiency model. Anal Biochem. 2008, 380: 315-322. 10.1016/j.ab.2008.05.048.
Figueiredo MD, Salter CE, Andrietti AL, Vandenplas ML, Hurley DJ, Moore JN: Validation of a reliable set of primer pairs for measuring gene expression by real-time quantitative RT-PCR in equine leukocytes. Vet Immunol Immunopathol. 2009, 131: 65-72. 10.1016/j.vetimm.2009.03.013.
Sieber MW, Recknagel P, Glaser F, Witte OW, Bauer M, Claus RA, Frahm C: Substantial performance discrepancies among commercially available kits for reverse transcription quantitative polymerase chain reaction: a systematic comparative investigator-driven approach. Anal Biochem. 2010, 401: 303-311. 10.1016/j.ab.2010.03.007.
Brunner AM, Yakovlev IA, Strauss SH: Validating internal controls for quantitative plant gene expression studies. BMC Plant Biol. 2004, 4: 14-10.1186/1471-2229-4-14.
Nicot N, Hausman JF, Hoffmann L, Evers D: Housekeeping gene selection for real-time RT-PCR normalization in potato during biotic and abiotic stress. J Exp Bot. 2005, 56: 2907-2914. 10.1093/jxb/eri285.
Jain M, Nijhawan A, Tyagi AK, Khurana JP: Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative real-time PCR. Biochem Biophys Res Commun. 2006, 345: 646-651. 10.1016/j.bbrc.2006.04.140.
Reid KE, Olsson N, Schlosser J, Peng F, Lund ST: An optimized grapevine RNA isolation procedure and statistical determination of reference genes for real-time RT-PCR during berry development. BMC Plant Biol. 2006, 6: 27-10.1186/1471-2229-6-27.
Exposito-Rodriguez M, Borges AA, Borges-Perez A, Perez JA: Selection of internal control genes for quantitative real-time RT-PCR studies during tomato development process. BMC Plant Biol. 2008, 8: 131-10.1186/1471-2229-8-131.
Silveira ED, Alves-Ferreira M, Guimaraes LA, da Silva FR, Carneiro VT: Selection of reference genes for quantitative real-time PCR expression studies in the apomictic and sexual grass Brachiaria brizantha. BMC Plant Biol. 2009, 9: 84-10.1186/1471-2229-9-84.
Sun HF, Meng YP, Cui GM, Cao QF, Li J, Liang AH: Selection of housekeeping genes for gene expression studies on the development of fruit bearing shoots in Chinese jujube (Ziziphus jujube Mill.). Mol Biol Rep. 2009, 36: 2183-2190. 10.1007/s11033-008-9433-y.
Mallona I, Lischewski S, Weiss J, Hause B, Egea-Cortines M: Validation of reference genes for quantitative real-time PCR during leaf and flower development in Petunia hybrida. BMC Plant Biol. 2010, 10: 4-10.1186/1471-2229-10-4.
Gamm M, Heloir MC, Kelloniemi J, Poinssot B, Wendehenne D, Adrian M: Identification of reference genes suitable for qRT-PCR in grapevine and application for the study of the expression of genes involved in pterostilbene synthesis. Mol Genet Genomics. 2011, 285: 273-285. 10.1007/s00438-011-0607-2.
Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3: RESEARCH0034-
Andersen CL, Jensen JL, Orntoft TF: Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004, 64: 5245-5250. 10.1158/0008-5472.CAN-04-0496.
Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP: Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper--Excel-based tool using pair-wise correlations. Biotechnol Lett. 2004, 26: 509-515.
Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J: qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol. 2007, 8: R19-10.1186/gb-2007-8-2-r19.
Chong J, Poutaraud A, Hugueney P: Metabolism and roles of stilbenes in plants. Plant Science. 2009, 177: 143-155. 10.1016/j.plantsci.2009.05.012.
Dixon RA, Paiva NL: Stress-Induced Phenylpropanoid Metabolism. Plant Cell. 1995, 7: 1085-1097.
Michalak A: Phenolic compounds and their antioxidant activity in plants growing under heavy metal stress. Polish Journal of Environmental Studies. 2006, 15: 523-530.
Sobolev VS, Khan SI, Tabanca N, Wedge DE, Manly SP, Cutler SJ, Coy MR, Becnel JJ, Neff SA, Gloer JB: Biological activity of peanut (Arachis hypogaea) phytoalexins and selected natural and synthetic stilbenoids. J Agric Food Chem. 2011, 59: 1673-1682. 10.1021/jf104742n.
Das S, Das DK: Anti-inflammatory responses of resveratrol. Inflamm Allergy Drug Targets. 2007, 6: 168-173. 10.2174/187152807781696464.
Velioglu-Ogunc A, Sehirli O, Toklu HZ, Ozyurt H, Mayadagli A, Eksioglu-Demiralp E, Erzik C, Cetinel S, Yegen BC, Sener G: Resveratrol protects against irradiation-induced hepatic and ileal damage via its anti-oxidative activity. Free Radic Res. 2009, 43: 1060-1071. 10.1080/10715760903171100.
Mikstacka R, Rimando AM, Ignatowicz E: Antioxidant effect of trans-resveratrol, pterostilbene, quercetin and their combinations in human erythrocytes in vitro. Plant Foods Hum Nutr. 2010, 65: 57-63. 10.1007/s11130-010-0154-8.
Aggarwal BB, Bhardwaj A, Aggarwal RS, Seeram NP, Shishodia S, Takada Y: Role of resveratrol in prevention and therapy of cancer: preclinical and clinical studies. Anticancer Res. 2004, 24: 2783-2840.
Medina-Bolivar F, Condori J, Rimando AM, Hubstenberger J, Shelton K, O'Keefe SF, Bennett S, Dolan MC: Production and secretion of resveratrol in hairy root cultures of peanut. Phytochemistry. 2007, 68: 1992-2003. 10.1016/j.phytochem.2007.04.039.
Condori J, Sivakumar G, Hubstenberger J, Dolan MC, Sobolev VS, Medina-Bolivar F: Induced biosynthesis of resveratrol and the prenylated stilbenoids arachidin-1 and arachidin-3 in hairy root cultures of peanut: Effects of culture medium and growth stage. Plant Physiol Biochem. 2010, 48: 310-318. 10.1016/j.plaphy.2010.01.008.
Luo M, Liang XQ, Dang P, Holbrook CC, Bausher MG, Lee RD, Guo BZ: Microarray-based screening of differentially expressed genes in peanut in response to Aspergillus parasiticus infection and drought stress. Plant Science. 2005, 169: 695-703. 10.1016/j.plantsci.2005.05.020.
Tsitsigiannis DI, Kunze S, Willis DK, Feussner I, Keller NP: Aspergillus infection inhibits the expression of peanut 13S-HPODE-forming seed lipoxygenases. Mol Plant Microbe Interact. 2005, 18: 1081-1089. 10.1094/MPMI-18-1081.
Slightom JL, Durand-Tardif M, Jouanin L, Tepfer D: Nucleotide sequence analysis of TL-DNA of Agrobacterium rhizogenes agropine type plasmid. Identification of open reading frames. J Biol Chem. 1986, 261: 108-121.
Schmulling T, Schell J, Spena A: Single genes from Agrobacterium rhizogenes influence plant development. EMBO J. 1988, 7: 2621-2629.
Camilleri C, Jouanin L: The TR-DNA region carrying the auxin synthesis genes of the Agrobacterium rhizogenes agropine-type plasmid pRiA4: nucleotide sequence analysis and introduction into tobacco plants. Mol Plant Microbe Interact. 1991, 4: 155-162. 10.1094/MPMI-4-155.
Schmulling T, Schell J, Spena A: Promoters of the rolA, B, and C genes of Agrobacterium rhizogenesare differentially regulated in transgenic plants. Plant Cell. 1989, 1: 665-670.
Wang K, Herrera-Estrella L, Van Montagu M, Zambryski P: Right 25 bp terminus sequence of the nopaline T-DNA is essential for and determines direction of DNA transfer from Agrobacterium to the plant genome. Cell. 1984, 38: 455-462. 10.1016/0092-8674(84)90500-2.
Gelvin SB: Crown gall disease and hairy root disease: a sledgehammer and a tackhammer. Plant Physiol. 1990, 92: 281-285. 10.1104/pp.92.2.281.
Overbergh L, Valckx D, Waer M, Mathieu C: Quantification of murine cytokine mRNAs using real time quantitative reverse transcriptase PCR. Cytokine. 1999, 11: 305-312. 10.1006/cyto.1998.0426.
Moser C, Gatto P, Moser M, Pindo M, Velasco R: Isolation of functional RNA from small amounts of different grape and apple tissues. Mol Biotechnol. 2004, 26: 95-100. 10.1385/MB:26:2:95.
Tattersall EAR, Ergul A, AlKayal F, DeLuc L, Cushman JC, Cramer GR: Comparison of methods for isolating high-quality RNA from leaves of grapevine. Am J Enol Vitic. 2005, 56: 400-406.
Saha S, Callahan FE, Dollar DA, Creech JB: Effect of lyophilization of cotton tissue on quality of extractable DNA. RNA and Protein J Cotton Sci. 1997, 1: 11-14.
Jaiprakash MR, Pillai B, Venkatesh P, Subramanian N, Sinkar VP, Sadhale PP: RNA isolation from high-phenolic freeze-dried tea (Camellia sinensis) leaves. Plant Molecular Biology Reporter. 2003, 21: 465-466. 10.1007/BF02772599.
Kumar GN, Iyer S, Knowles NR: Extraction of RNA from fresh, frozen, and lyophilized tuber and root tissues. J Agric Food Chem. 2007, 55: 1674-1678. 10.1021/jf062941m.
Gallup JM: qPCR inhibition and qmplification of difficult templates. 2011, Norfolk, UK: Caister Academic Press
Opel KL, Chung D, McCord BR: A study of PCR inhibition mechanisms using real time PCR. J Forensic Sci. 2010, 55: 25-33. 10.1111/j.1556-4029.2009.01245.x.
Pfaffl MW: Quantification strategies in real-time PCR. AZ of quantitative PCR. 2004, 1: 89-113.
Derveaux S, Vandesompele J, Hellemans J: How to do successful gene expression analysis using real-time PCR. Methods. 2010, 50: 227-230. 10.1016/j.ymeth.2009.11.001.
Barber RD, Harmer DW, Coleman RA, Clark BJ: GAPDH as a housekeeping gene: analysis of GAPDH mRNA expression in a panel of 72 human tissues. Physiol Genomics. 2005, 21: 389-395. 10.1152/physiolgenomics.00025.2005.
Zainuddin A, Makpol S, Chua KH, Abdul Rahim N, Yusof YA, Ngah WZ: GAPDH as housekeeping gene for human skin fibroblast senescent model. Med J Malaysia. 2008, 63 (Suppl A): 73-74.
Barsalobres-Cavallari CF, Severino FE, Maluf MP, Maia IG: Identification of suitable internal control genes for expression studies in Coffea arabica under different experimental conditions. BMC Mol Biol. 2009, 10: 1-10.1186/1471-2199-10-1.
Sambrook J, Russell DW: Molecular cloning: a laboratory manual. 2001, CSHL Press
Wu Q, Krainer AR: AT-AC pre-mRNA splicing mechanisms and conservation of minor introns in voltage-gated ion channel genes. Mol Cell Biol. 1999, 19: 3225-3236.
D'Haene B, Vandesompele J, Hellemans J: Accurate and objective copy number profiling using real-time quantitative PCR. Methods. 2010, 50: 262-270. 10.1016/j.ymeth.2009.12.007.
Pfaffl MW: A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 2001, 29: e45-10.1093/nar/29.9.e45.
This work was supported by the National Science Foundation-EPSCoR (grant # EPS-0701890; Center for Plant-Powered Production-P3), Arkansas ASSET Initiative and the Arkansas Science & Technology Authority and the Arkansas Biosciences Institute. Special thanks to the Arkansas Biosciences Institute, the Molecular Biosciences Program and the College of Sciences and Mathematics at Arkansas State University for covering the expenses of JC to present aspects of this work at the qPCR Symposium USA in November 2010 (Millbrae, CA). The authors thank Dr. Maureen Dolan (Arkansas Biosciences Institute at Arkansas State University) for her suggestions during the preparation of the manuscript.
The authors declare that they have no competing interests.
JC performed data analysis, carried out the statistical analysis and drafted the manuscript; JC and CNO carried out the entire experimental procedure; JC and FMB conceived of the study, designed and evaluated the experiments. GM supervised experimental design and evaluated the experiments. All authors read, revised and approved the final manuscript.
Electronic supplementary material
Additional file 1: Summary of reference genes in literature used in plants and humans1. Select literature used in identifying potential validation of reference genes candidates are listed by first author and year of the publication. Gene targets used for each study are indicated by accession number (GenBank or TAIR database). One reference1 provides reference genes used for humans. The gene(s) identified as best reference gene(s) in each of the cited studies is showed in red. In green: Genes selected from Transcript arrays in Arabidopsis . In yellow: Most frequently reference genes used. (XLS 38 KB)
Additional file 2: RNA chemical contaminants. Indicator of chemical contaminants in RNA was measured using the ratio of: absorbance at 260 nm/absorbance at 230 nm. X-axis shows the intervals of A260/A230 for the 60 biological samples from the time course experiments. (PNG 13 KB)
Additional file 3: Primer specificity of reference genes for APT1, UBQ11 and AP47. Melting curves generated for 40 (red), 8 (green), 1.6 (light blue), 0.32 (yellow) and 0.064 (black) ng of cDNA. (PNG 56 KB)
Additional file 4: Primer specificity of reference genes for ACT7, TIP41 and H3. Melting curves generated for 40 (red), 8 (green), 1.6 (light blue), 0.32 (yellow) and 0.064 (black) ng of cDNA. (PNG 55 KB)
Additional file 5: Primer specificity of reference genes for TBP2, EFa1 and RPL8C. Melting curves generated for 40 (red), 8 (green), 1.6 (light blue), 0.32 (yellow) and 0.064 (black) ng of cDNA. (PNG 51 KB)
Additional file 6: Primer specificity of reference genes for EIF4A1, CYP1 and rolC. Melting curves generated for 40 (red), 8 (green), 1.6 (light blue), 0.32 (yellow) and 0.064 (black) ng of cDNA. (PNG 56 KB)
Additional file 7: Primer specificity of reference genes for AT1G31300, HEL and GAPDH. Melting curves generated for 40 (red), 8 (green), 1.6 (light blue), 0.32 (yellow) and 0.064 (black) ng of cDNA. (PNG 56 KB)
Additional file 8: Primer specificity of reference genes for TUA3, SAND and RPB1. Melting curves generated for 40 (red), 8 (green), 1.6 (light blue), 0.32 (yellow) and 0.064 (black) ng of cDNA. (PNG 51 KB)
Additional file 9: Primer specificity of reference genes for AT4G33380, PP2AA3 and aux1. Melting curves generated for 40 (red), 8 (green), 1.6 (light blue), 0.32 (yellow) and 0.064 (black) ng of cDNA. (PNG 52 KB)
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Condori, J., Nopo-Olazabal, C., Medrano, G. et al. Selection of reference genes for qPCR in hairy root cultures of peanut. BMC Res Notes 4, 392 (2011). https://doi.org/10.1186/1756-0500-4-392
- Reference Gene
- Hairy Root
- Hairy Root Culture
- Candidate Reference Gene