Validation of internal control genes for quantitative gene expression studies in chickpea (Cicer arietinum L.)

Biochemical and Biophysical Research Communications 396 (2010) 283–288

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Biochemical and Biophysical Research Communications journal homepage: www.elsevier.com/locate/ybbrc

Validation of internal control genes for quantitative gene expression studies in chickpea (Cicer arietinum L.) Rohini Garg, Annapurna Sahoo, Akhilesh K. Tyagi, Mukesh Jain * National Institute of Plant Genome Research (NIPGR), Aruna Asaf Ali Marg, New Delhi 110 067, India

a r t i c l e

i n f o

Article history: Received 23 March 2010 Available online 23 April 2010 Keywords: Chickpea (Cicer arietinum) Gene expression Housekeeping genes Internal control Normalization Real-time PCR

a b s t r a c t The real-time polymerase chain reaction (PCR) data requires normalization with an internal control gene expressed at constant levels under all the experimental conditions being analyzed for accurate and reliable gene expression results. In this study, the expression of 12 candidate internal control genes, including ACT1, EF1a, GAPDH, IF4a, TUB6, UBC, UBQ5, UBQ10, 18SrRNA, 25SrRNA, GRX and HSP90, in a diverse set of 18 tissue samples representing different organs/developmental stages and stress conditions in chickpea (Cicer arietinum L.) has been validated. Their expression levels vary considerably in various tissue samples analyzed. The expression levels of EF1a and HSP90 are most constant across various organs/ developmental stages analyzed. Similarly, the expression levels of IF4a and GAPDH are most constant across various stress conditions. A set of two most stable genes is found sufficient for accurate and reliable normalization of real-time PCR data in the given set of tissue samples of chickpea. The genes with most constant expression identified in this study should be useful for normalization of gene expression data in a wide variety of tissue samples in chickpea. Ó 2010 Elsevier Inc. All rights reserved.

1. Introduction Gene expression analysis is very important to gain insight into the function of genes. Among the various techniques employed, real-time PCR offers a robust means of quantifying gene expression due to its higher sensitivity, specificity and broad dynamic range [1–3]. Therefore, real-time PCR has become the most common technique for analyzing expression of a set of genes, molecular diagnostics and validating results of microarray [4–9]. The normalization of real-time PCR data with a suitable internal control gene is extremely important for accurate and reliable results [10,11]. The internal control gene used for normalization in real-time PCR data analysis should exhibit highly constant expression throughout the experimental conditions being analyzed. The traditional housekeeping genes such as actin, ubiquitin, tubulin, glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and 18SrRNA are used most commonly as internal controls for gene expression studies, as they are assumed to be expressed at constant levels regardless of experimental conditions. However, several reports have shown that the expression of these genes can also vary considerably under different environmental conditions [10,12–14]. Therefore, to avoid erroneous results, there is a need for evaluation of candidate genes for normalization of real-time PCR data under given experimental conditions. Recognizing the * Corresponding author. Fax: +91 11 26741658. E-mail addresses: [email protected], [email protected] (M. Jain). 0006-291X/$ - see front matter Ó 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.bbrc.2010.04.079

importance, several studies have reported the evaluation of various housekeeping genes to identify the best suited internal control genes for normalization of real-time PCR data under specific conditions in various organisms, including plants [12–16]. Genome-wide analyses have also been performed to identify novel internal control genes for normalization [17–19]. Chickpea (Cicer arietinum L.) is a very important legume crop plant, but very limited genomic information is available about it [20]. However, high-throughput whole-genome and transcriptome sequencing of chickpea are in progress. Considering the importance of real-time PCR analysis in functional genomic studies, it will be important to identify the most suitable internal control genes for gene expression data analysis in chickpea. To our knowledge, no such study has been performed so far in legume plants except for soybean [21]. In this study, we sought to validate the 12 candidate genes to identify the most suitable internal control gene(s) for normalization of real-time PCR data in a diverse set of tissue samples in chickpea. 2. Materials and methods 2.1. Plant material Chickpea (C. arietinum L. genotype ICC 4958) seeds procured from ICRISAT, Hyderabad, India, after thorough washing were soaked overnight in reverse osmosis (RO) purified water. The seedlings were grown in autoclaved mixture (1:1) of agropeat and vermiculite in

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3 in. plastic pots at 22 ± 1 °C in a culture room with a photoperiod of 14 h. All the mature organs/developmental stages were collected from plants grown in the field. For drought and salt stress treatments, the 10-day-old seedlings were transferred on folds of tissue paper and beaker containing 150 mM NaCl solution, respectively, at 22 ± 1 °C in the culture room. For cold treatment, the seedlings were kept in water at 4 ± 1 °C under light. The control seedlings were kept in water at 22 ± 1 °C. Root and shoot tissues were collected from stressed and control seedlings after 5 h of the treatment. At least three independent biological replicates of each tissue sample were harvested and immediately frozen in liquid nitrogen.

2.4. Genomic DNA PCR

2.2. RNA isolation and quality controls

The first strand cDNA synthesis was done with 6 lg of total RNA in a final reaction volume of 100 ll using High Capacity cDNA Archive Kit (Applied Biosystems) according to manufacturer’s instructions. The real-time PCR reactions were performed in 384well plates using 7900HT Sequence Detection System and software v2.3 (Applied Biosystems). SYBR green chemistry was used for all the real-time PCR reactions. The reaction mixture consisted of 1 ll of diluted cDNA (corresponding to 1.5 ng of starting amount of RNA for 18SrRNA and 25SrRNA, and 15 ng for other genes), 200 nM of each primer and 5 ll of Power SYBR Green PCR Master Mix (Applied Biosystems) in a final volume of 10 ll. All the reactions were performed under default conditions: 2 min at 50 °C, 10 min at 95 °C, and 40 cycles of 15 s at 95 °C, and 1 min at 60 °C. The specificity of reaction was verified by dissociation curve analysis by using cycle of 95 °C for 15 s followed by constant increase of temperature between 60 and 95 °C and agarose gel electrophoresis. Three biological replicates for each sample and three technical replicates of each biological replicate were analyzed for real-time PCR analysis. For a biological replicate of a tissue sample, same cDNA pool was used for real-time PCR analysis of all the genes analyzed using gene-specific primers.

Total RNA was extracted from all the tissue samples using TRI Reagent (Sigma Life Science, USA) according to manufacturer’s instructions. For total RNA isolation from young pods an additional step of chloroform extraction was done for complete removal of protein contamination. The quality and quantity of each RNA sample was checked twice using NanoVue (GE Healthcare, Hong Kong) and all the RNA samples were adjusted to same concentration. The quality of all the RNA samples was also checked using Agilent 2100 Bioanalyzer and RNA 6000 nanokit (Agilent Technologies, Singapore). Only the RNA samples with 260/280 ratio from 1.9 to 2.1, 260/230 ratio from 2.0 to 2.5 and RIN (RNA integrity number) more than 8.0, were used for the analysis. The integrity of RNA samples was also assessed by agarose gel electrophoresis. 2.3. Primer designing The primers for real-time PCR analysis were designed using Primer Express (v3.0) software (Applied Biosystems, Foster City, CA) under the default parameters; except for minimum primer length was set to 20. The specificity of primer pairs was confirmed by BLASTN with all the nucleotide sequences available for chickpea at National Centre for Biotechnology Information (NCBI). The primer sequences of all the candidate internal control genes tested in this study are listed in Table 1.

The genomic DNA from shoots of chickpea plants was isolated using cetyl trimethyl ammonium bromide method [22]. The PCRs for all the candidate genes were performed together using genomic DNA as template and gene-specific primers designed for real-time PCRs under following conditions: one cycle of 4 min at 95 °C, 35 cycles of 30 s at 95 °C, 30 s at 58–62 °C and 1 min 30 s at 70 °C,and one cycle of 10 min at 70 °C. 2.5. Real-time PCR analysis

2.6. Analysis of gene expression stability The gene expression stability of all the candidate genes tested across various tissues samples was analyzed using geNORM v3.5

Table 1 Candidate internal control genes and their primer sequences used for real-time PCR analysis.

a b c

Gene name

Accession No.a

Gene descriptionb

Primer sequencec

Amplicon length (bp)

ACT1

EU529707

Actin 1

850 0

62

EF1a

AJ004960

Elongation factor 1-alpha

GAPDH

AJ010224

Glyceraldehyde-3-phosphate dehydrogenase

IF4a

FL512356

Initiation factor 4a

TUB6

X98406

Tubulin 6

UBC

GR915526

Ubiquitin-conjugating enzyme E2

UBQ5

GR405803

Ubiquitin 5

UBQ10

GR398899

Ubiquitin 10

18SrRNA

AJ577394

18S ribosomal RNA

25SrRNA

FE671493

25S ribosomal RNA

HSP90

GR406804

Heat shock protein 90

GRX

GR406543

Glutaredoxin protein

5 -GCCTGATGGACAGGTGATCAC-30 911 0 5 -GGAACAGGACCTCTGGACATCT-30 857 0 5 -TCCACCACTTGGTCGTTTTG-30 920 0 5 -CTTAATGACACCGACAGCAACAG-30 209 0 5 -CCAAGGTCAAGATCGGAATCA-30 273 0 5 -CAAAGCCACTCTAGCAACCAAA-30 446 0 5 -TGGACCAGAACACTAGGGACATT-30 505 0 5 -AAACACGGGAAGACCCAGAA-30 439 0 5 -CGTAAAGAAGCCGAAAATTGTGA-30 499 0 5 -CTCCAAGCGAGTGGCATACTT-30 325 0 5 -TTGCTTTGATGGCTCATCCA-30 388 0 5 -CGCAGAAGATTACCTGAATCACA-30 69 0 5 -TCACCCTCGAGGTGGAGTCT-30 128 0 5 -TGTCTTGGATCTTTGCTTTGACA-30 551 0 5 -CCTCGCTGATTACAACATCCAG-30 638 0 5 -CAAGGTCTTCACACAAATCTGCATA-30 1620 0 5 -ACGTCCCTGCCCTTTGTACAC-30 1681 0 5 -CACTTCACCGGACCATTCAAT-30 57 0 5 -AAAACAAAGCATTGCGATGGT-30 116 0 5 -GCACTGGGCAGAAATCACATT-30 525 0 5 -GCAGCATGGCTGGTTACATGT-30 587 0 5 -TGATGGGATTCTCAGGGTTGA-30 88 0 5 -ACCAAAGGCAAAGGAGATCGT-30 9 0 5 -GGGCAATAGGATTTGCTGAAAA-30

64 65 60 61 64 60 88 61 60 63 63

cDNA/EST GenBank accession number. Gene description based on homology with Arabidopsis or rice proteins. Forward (upper line) and reverse (lower line) primer sequences along with their position (number given as superscript on the left) in cDNA/EST sequence.

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A

ACT1

B

GAPDH

C NTC

NTC

Temperature (°C)

Temperature (°C)

UBQ5

TUB6

NA NA H 0 0 C SrR SrR T1 1α PD a B6 C Q5 Q1 X P9 NT 18 25 AC EF GA IF4 TU UB UB UB GR HS

200 100 50

Rn

Rn

bp

bp

NA NA H 0 0 C SrR SrR T1 1α PD a B6 C Q5 Q1 X P9 NT 18 25 AC EF GA IF4 TU UB UB UB GR HS 1500 1031

1031

500

500 200

Rn

Rn

100 50

NTC

NTC

Temperature (°C)

Temperature (°C)

GAPDH

ACT1

ΔRn

ΔRn

D

CT

CT Cycle no.

Cycle no.

TUB6

ΔRn

ΔRn

UBQ5

CT Cycle no.

CT Cycle no.

Fig. 1. Specificity of real-time PCR amplification. (A) Dissociation curves for four representative candidate internal control genes from three technical replicates of 18 cDNA pools along with no template control (NTC). Dissociation curves for all the genes are shown in Supplemental Fig. S1. (B) Agarose gel (2%) showing amplification of a specific PCR product of expected size in real-time PCR reactions for each candidate internal control gene tested in the study. (C) Agarose gel (1.5%) showing amplification of a specific PCR product with genomic DNA as template using gene-specific primers for each candidate internal control gene tested in the study. The primers for GAPDH, TUB6 and GRX amplified a larger size PCR product (as compared to B) indicating the position of primer pairs spanning at least one intron. (D) Amplification plots for four representative candidate internal control genes from three technical replicates of 18 cDNA pools. The horizontal green line represents the threshold florescence (0.18) at which CT (vertical arrow) was determined. Amplification plots for all the genes are shown in Supplemental Fig. S2.

software as proposed [15]. The geometric means of CT values of the 12 candidate genes in all the tissue samples analyzed were calculated and raw quantities obtained via delta CT method [15] were taken as input in geNORM. 3. Results 3.1. Expression profiling of candidate internal control genes We identified putative orthologs of 10 candidate traditional housekeeping genes (ACT1, EF1a, GAPDH, IF4a, TUB6, UBC, UBQ5, UBQ10, 18SrRNA and 25SrRNA) most commonly used as internal

controls from chickpea by TBLASTN and BLASTN searches in the non-redundant and expressed sequence tag (EST) databases at NCBI. In addition, we also identified orthologs of two novel reference genes, HSP90 and GRX, in chickpea, which were found to have highly constant expression in a genome-wide analysis across various developmental stages in rice [18]. The expression levels of these 12 candidate internal control genes were determined and assessed for expression stability in a set of 18 different tissue samples comprising of 10 samples from development series representing different tissues/organs/developmental stages and eight samples from stress series representing various abiotic stress treatments to root and shoot tissues. The gene name, accession

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number, gene description, primer sequences with their position and amplicon length used in the study are given in Table 1. The description of various tissue samples used in this study is given in Supplemental Table S1. Real-time PCRs were performed in triplicates for each biological replicate of 18 samples with a no template control in parallel for each gene. The dissociation curve analysis and agarose gel electrophoresis showed that all the 12 primer pairs amplified a specific PCR product of desired size from various cDNA pools (Fig. 1A and B, Supplemental Fig. S1). Since the genomic sequence of the candidate genes was not

A

available, additional PCRs were also performed using chickpea genomic DNA as template and gene-specific primers (which were used for real-time PCRs) to find the presence of intron(s) within the amplicon region. We found that primer pairs for GAPDH, TUB6 and GRX amplified a specific larger-sized PCR product as compared to that amplified from cDNA as template in real-time PCRs (Fig. 1C). This indicates that primer pairs for these genes span at least one intron. Since we got the specific amplification of desired size PCR product in real-time PCR reactions for these genes, the presence of any genomic DNA contamination in the RNA samples is ruled out.

Average expression stability (M)

0.8 0.7

0.72 0.66 0.62

0.6

0.59 0.55 0.51

0.5

0.47 0.43

0.4

0.37 0.33

0.3

0.28

0.2

1 ACT

B

GRX 8SrRNAUBQ10 UBQ5 5SrRNA UBC GAPDH IF4a 1 2

6 TUB

EF1 0 HSP9

Average expression stability (M)

0.9 0.8

0.82 0.72

0.7

0.68 0.64 0.60

0.6

0.55 0.51

0.5

0.42

0.4

0.37 0.30

0.3

1 ACT

C

0.48

X GR

6 TUB

5 UBQ

10 NA NA UBC APDH IF4a UBQ 18SrR 25SrR G

EF1 90 HS P

0.55

Average expression stability (M)

0.52 0.50 0.45 0.40 0.35

0.44 0.38 0.35 0.33 0.30

0.30

0.25

0.25

0.21 0.20 0.16

0.15 0.10

NA TUB6 SrRNA UBC UBQ10 GRX 18SrR 25

Least stable genes

ACT1 HSP90 UBQ5

0.15

EF1

0.13

GA P D IF4a

H

Most stable genes

Fig. 2. Expression stability and ranking of candidate internal control genes as calculated by geNORM in all the 18 tissue samples (A), development series (B) and stress series (C). A lower value of average expression stability (M) indicates more stable expression.

R. Garg et al. / Biochemical and Biophysical Research Communications 396 (2010) 283–288

0.14

V2/3 V3/4

V4/5 V5/6

V6/7 V7/8

V8/9 V9/10

V10/11 V11/12

287

and HSP90 were lower in stress series tissue samples than that of when all the tissue samples or development series tissue samples were analyzed, indicating their high expression stability (Fig. 2).

Pairwise variation (V)

0.12

3.3. Optimal number of internal control genes for normalization 0.10 0.08 0.06 0.04 0.02 0

All

Development

Stress

Fig. 3. Pairwise variation (V) to determine the optimal number of control genes for accurate normalization in all the 18 tissue samples, development series samples and stress series samples. Arrow heads indicate the optimal number of genes for normalization.

The amplification plots for each gene were generated and grouped across all the tissue samples and cycle threshold (CT) was determined at florescence value of 0.18 (Fig. 1D, Supplemental Fig. S2). The transcript levels of 18SrRNA and 25SrRNA were higher several orders of magnitude as indicated by lower average CT values of 10.1 and 7.2, respectively, than that of other 10 genes, which have average CT values ranging from 16.9 to 22.9 (Supplemental Table S2). Among these 10 genes, EF1a was expressed at relatively higher level (average CT value 16.9) followed by UBQ10 (average CT value 17.7) and GAPDH (average CT value 18.1). UBC exhibited lowest expression with average CT value of 22.9. A low value of standard deviation (Supplemental Table S2) confirmed a high precision among the three biological replicates of all the samples analyzed. We found the variations in the relative expression of 12 candidate genes across all the tissue samples analyzed, specifically in the samples of development series (Supplemental Fig. S3). These results suggest the expression levels of these genes is not constant, but varies in unique temporal and/or spatial manner. 3.2. Statistical analysis of expression stability The gene expression stability of all the candidate genes in various tissue samples was assessed statistically using geNORM software. geNORM calculates the mean pairwise variation for a gene in comparison to all other genes being tested and reports the average expression stability (M) of all the genes in a given set of samples [15]. Genes with lower expression stability will have a higher M value, whereas the genes with lowest M value are considered to show most constant expression. We analyzed our data in three sets, first including all the 18 tissue samples, second including 10 samples of development series and third including eight samples of stress series. All the candidate genes displayed high expression stability with low (<0.9) M values, which were below the default limit of 1.5 in geNORM. In case of all the samples analyzed together, the M value was least for EF1a (0.28) and HSP90 (0.28) followed by IF4a (0.33) and GAPDH (0.37). However, M value was highest for ACT1 (0.72) (Fig. 2A). This indicates that EF1a and HSP90 exhibit most constant expression, whereas the expression of ACT1 is most variable. Consistently, EF1a and HSP90 exhibited highest gene expression stability, whereas ACT1 exhibited lowest expression stability, when only the samples of development series were analyzed (Fig. 2B). However, for stress series, GAPDH and IF4a ranked first with highest gene expression stability followed by EF1a, UBQ5 and HSP90 (Fig. 2C). Notably, the M values for EF1a

Although single gene with high expression stability may be appropriate for normalization of gene expression data, the use of two or more genes as internal controls is better for accurate and reliable results. geNORM provides a method to determine the optimal number of internal control genes for normalization of gene expression data in a given set of samples [15]. geNORM calculates the normalization factor (NF) for the two genes with highest expression stability and then for other genes by stepwise addition of them in order of decreasing expression stability. Subsequently, pairwise variation (V) of NFn and NFn+1 are calculated, which measures the effect of adding additional internal control gene on NF. For all the three sets (all samples, development series samples and stress series samples), V2/3 was below 0.15 cut-off value (Fig. 3). It has been suggested that below this cut-off value there is no need of inclusion of an additional internal control gene [15]. This suggests that only two genes with most stable expression are optimal for reliable normalization in the analyzed tissue samples of chickpea.

4. Discussion Real-time PCR is extremely powerful technique for quantifying gene expression. However, besides being highly reliable, it suffers from certain pitfalls, most important being the choice of most suitable internal control gene(s) for normalization [10,23]. The normalization takes into account variations introduced by RNA sample quality, RNA input quantity and enzymatic efficiency of reverse transcription. An ideal internal control gene should have a constant expression level throughout the experimental conditions being analyzed. However, no gene is supposed to have highly constant expression throughout all the experimental conditions. Therefore, normalization with multiple internal control genes validated for their expression stability is required for reliable gene expression results. Several statistical algorithms are available to validate the expression stability of candidate genes [15,24,25]. Numerous studies have reported the identification of most stable internal control genes at whole-genome level or from a set of candidate genes in a wide range to specific tissue samples and/or experimental conditions [13,14,17,18,21]. In the present study, we have validated the expression stability of 12 candidate genes, including 10 traditional most commonly used housekeeping genes and two novel genes in a diverse set of 18 tissue samples after various quality controls. The results show that many of the tested housekeeping genes exhibit differential expression in the tissue samples analyzed and thus are not good candidate internal control genes for normalization. Several other studies have also shown that expression of housekeeping genes vary with the experimental conditions because they are not only involved in basal cell metabolism, but are also implicated in specific cellular functions [10,13,17,26]. The statistical analysis revealed that the expression of EF1a and HSP90 followed by IF4a and GAPDH was most constant in all the tissue samples and development series samples representing various organs/developmental stages. However, ACT1 and TUB6 displayed highest expression variability limiting their use as internal control genes. The higher expression stability of HSP90 as compared to most of housekeeping genes is not surprising, as other studies have also reported that many novel genes outperform the traditional housekeeping genes in terms of their expression stability [17,18]. At whole-genome level, HSP90 was among the top 25 genes, which displayed most

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constant expression across various developmental stages in rice [18]. Recently, using traditional reverse transcription-PCR, it has been reported that ACT1 is expressed ubiquitously in various organs of chickpea and has been proposed as a potential reference gene based on its similar expression pattern with Arabidopsis ACT2 and ACT8 genes without any statistical validation [27]. However, our results clearly showed that the expression of ACT1 is least stable in all the tissue samples analyzed in this study. Different housekeeping genes have been proposed as the most suitable internal control genes depending on the experimental conditions and plant species. For example, in rice, we identified UBQ5 and EF1a as the most stable genes over a wide range of tissue samples and experimental conditions among a set of 10 housekeeping genes [13]. However, a UBQ and a TUA gene were found to be most stably expressed in Populus [28] and GAPDH in sugarcane [29]. 18SrRNA and 25SrRNA are also commonly used internal control genes. In this study, we found the expression of 18SrRNA and 25SrRNA was quite variable as revealed by their expression stabilities in all the datasets and thus are not suitable for normalization. Further, their expression levels were also very high, excluding the possibility of their use as internal control genes for weakly or moderately expressed genes of interest. Four genes, encoding ATP-binding cassette transporter, F-box protein family, metalloprotease and CDPK-related protein kinase, have been reported most suitable for gene expression normalization in soybean [21]. Their orthologs are not represented in the sequences available for chickpea at NCBI databases so far. Although GAPDH and IF4a exhibited the most stable expression in tissue samples of stress series, the expression stabilities of EF1a and HSP90 was also similar to that in all the tissue samples and development series samples. Therefore, we recommend the use of EF1a and HSP90 as most suitable internal control genes in a diverse set of tissue samples of chickpea. Although these two genes should be sufficient to give reliable results, the addition of a third gene, IF4a, as internal control may produce even better results. However, it is likely that the genes other than those identified here may act as better candidate internal control genes in specific cell-/tissue-type or experimental conditions. In conclusion, we have identified EF1a and HSP90 as the most suitable internal control genes for normalization of real-time PCR results in chickpea. This is the first study to identify most suitable internal control genes in legume crop plant chickpea. With the growing genomic resources, these genes should enable accurate and reliable gene expression data analysis over a wide range of tissue samples/experimental conditions in chickpea for functional genomic studies. Acknowledgments This study was supported financially by the Department of Biotechnology, Government of India, New Delhi, under the Next Generation Challenge Programme on Chickpea Genomics and core grant from the NIPGR. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.bbrc.2010.04.079. References [1] W.M. Freeman, S.J. Walker, K.E. Vrana, Quantitative RT-PCR: pitfalls and potential, Biotechniques 26 (1999) 112–122. [2] S.A. Bustin, Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems, J. Mol. Endocrinol. 29 (2002) 23–39. [3] C. Gachon, A. Mingam, B. Charrier, Real-time PCR: what relevance to plant studies? J. Exp. Bot. 55 (2004) 1445–1454.

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