ORIGINAL PAPER

Validation of reference genes for RT-qPCR normalizationin common bean during biotic and abiotic stresses

Aline Borges • Siu Mui Tsai •

Danielle Gregorio Gomes Caldas

Received: 17 September 2011 / Revised: 25 November 2011 / Accepted: 2 December 2011 / Published online: 23 December 2011

� Springer-Verlag 2011

Abstract Selection of reference genes is an essential

consideration to increase the precision and quality of rel-

ative expression analysis by the quantitative RT-PCR

method. The stability of eight expressed sequence tags was

evaluated to define potential reference genes to study the

differential expression of common bean target genes under

biotic (incompatible interaction between common bean

and fungus Colletotrichum lindemuthianum) and abiotic

(drought; salinity; cold temperature) stresses. The effi-

ciency of amplification curves and quantification cycle (Cq)

were determined using LinRegPCR software. The stability

of the candidate reference genes was obtained using geN-

orm and NormFinder software, whereas the normalization

of differential expression of target genes [beta-1,3-glu-

canase 1 (BG1) gene for biotic stress and dehydration

responsive element binding (DREB) gene for abiotic stress]

was defined by REST software. High stability was obtained

for insulin degrading enzyme (IDE), actin-11 (Act11),

unknown 1 (Ukn1) and unknown 2 (Ukn2) genes during

biotic stress, and for SKP1/ASK-interacting protein 16

(Skip16), Act11, Tubulin beta-8 (b-Tub8) and Unk1 genes

under abiotic stresses. However, IDE and Act11 were

indicated as the best combination of reference genes for

biotic stress analysis, whereas the Skip16 and Act11 genes

were the best combination to study abiotic stress. These

genes should be useful in the normalization of gene

expression by RT-PCR analysis in common bean, the most

important edible legume.

Keywords Phaseolus vulgaris � RT-qPCR � Normalizer

genes � Stress conditions

Introduction

Common bean is the most important legume for direct

human consumption, providing an important source of

minerals and proteins (Gepts et al. 2008). Due to their

importance and the consequent achievement of several

projects involving transcriptional analysis, an increasing

number of expressed sequence tags (ESTs) have become

publicly available in the GenBank database, National Center

for Biotechnology Information (NCBI) (116,716 ESTs

record in 2011) (http://www.ncbi.nlm.nih.gov/nucest). This

remarkable genomic resource accumulated over the last few

years enables quantitative measurements of transcripts of

interest through real-time PCR technique associated with the

reverse transcriptase enzyme (RT-qPCR) and notably

increases the importance of the availability of internal

controls.

The RT-qPCR has allowed for the calculation of dif-

ferential gene expression in different organs or tissues

under several treatments using cDNA molecules synthe-

sized from mRNA (Heid et al. 1996; Bustin 2002). The

quantitative PCR method has shown important attributes

such as accuracy, precision and relative ease of use due to

its speed, sensitivity and specificity (Bustin 2002; Reece

2004). Given these features, this method has been high-

lighted in several research areas, including life sciences,

medicine and forensic study (Bustin et al. 2009). However,

despite these advantages, the qPCR technique is subject to

Communicated by Q. Zhao.

A. Borges � S. M. Tsai � D. G. G. Caldas (&)

Center of Nuclear Energy for Agriculture,

Laboratory of Cellular and Molecular Biology,

University of Sao Paulo, P. O. Box 96 CEP,

Piracicaba, SP 13400-970, Brazil

e-mail: dcaldas@cena.usp.br

123

Plant Cell Rep (2012) 31:827–838

DOI 10.1007/s00299-011-1204-x

several sources of error. Particularly, in RT-qPCR, several

factors that should be considered are the efficiency of

cDNA synthesis, the use of different individuals, the proper

care in RNA isolation and storage, and variations in the

initial quantities of RNA in each essay (Andersen et al.

2004). Thus, the reliability of the technique depends on

carefully chosen experimental conditions, and especially

on the choice of internal controls to avoid obtaining biased

data and statistical analysis.

Genes related to basic and structural processes in the

cell (b-tubulin, b-actin, glyceraldehyde-3-phosphate

dehydrogenase, ribosomal subunits, ubiquitin, and others)

have been designated as housekeeping (uniform expression

genes), and have been used directly as normalizers in

quantitative assays (Tricarico et al. 2002). However, sev-

eral studies have reported variable expression of these

types of genes under different conditions and tissues

(Thellin et al. 1999; Cordoba et al. 2011; Czechowski et al.

2005). With this, researchers have agreed on the need to

perform basic procedures, such as the selection of genes

characterized by low variation in expression levels, or

typical housekeeping genes to allow for the determination

of the best set of internal controls for certain treatments

(Andersen et al. 2004; Tunbridge et al. 2011). Furthermore,

the use of a normalization factor based on the geometric

mean of multiple reference genes has been recommended

for the normalization of target gene expression levels by

RT-qPCR (Vandesompele et al. 2002). Therefore, it must

be ensured that internal controls have high expression

stability levels in different tissues and developmental

stages, and this expression should be independent of

experimental parameters (Bustin 2000).

Reference gene analysis has focused on studies in the

medical sciences (Bustin 2002) while few works have been

reported for plant species, among them Arabidopsis thali-

ana (Czechowski et al. 2005; Gutierrez et al. 2008), rice

(Jain et al. 2006), tomato (Exposito-Rodrıguez et al. 2008;

Lovdal and Lillo 2009), peach (Tong et al. 2009), wheat

(Paolacci et al. 2009), soybean (Libault et al. 2008; Hu

et al. 2009; Kulcheski et al. 2010), chicory (Maroufi et al.

2010) and litchi (Zhong et al. 2011). Furthermore, the large

majority of works have been using geNorm (Vandesompele

et al. 2002) and NormFinder (Andersen et al. 2004) anal-

ysis tools to identify the most stable genes in an experi-

ment. To our knowledge, a detailed study on suitable

normalizers for Phaseolus vulgaris L. has not yet been

reported, and is restricted to a record of a small evaluation

of three candidates to an internal control found in an EST

library obtained from P. vulgaris in interaction with the

fungus Uromyces appendiculatus (Thibivilliers et al.

2009).

In this study, we aimed at defining reference genes

suited for quantitative analysis of common bean genes

under different experimental conditions. In this way, the

stability of eight candidate reference genes were evaluated

by RT-qPCR for the quantification of relative expression

levels of transcripts in P. vulgaris subjected to biotic stress

treatment (interaction with an avirulent race of Colletotri-

chum lindemuthianum (Sacc. & Magnus) Briosi & Cavara,

considering different plant organs and periods after inoc-

ulation) and to abiotic stress treatments (water deficit,

salinity and low temperature). In addition, the best nor-

malizer gene combination was determined for each

condition.

Materials and methods

Incompatible interaction between common bean

and C. lindemuthianum treatment

For biotic treatment, we used the breeding line SEL 1308

(CIAT 1990) and the Black Magic variety, which was used as

positive control due its high susceptibility to the C. lindem-

uthianum race used in this study. Seed sterilization was

performed using a 10% sodium hypochlorite solution for

10 min, and then the seeds were settled on filter paper to

germinate over 4 days. Subsequently, seedlings were trans-

ferred to potting mix (Plantmax�) and separated for control

conditions (not inoculated) and inoculation assays.

In this work, the fungus used belongs to the race 73 of

C. lindemuthianum, which is avirulent in the interaction

with SEL 1308. Inoculum preparation, without the addition

of yeast extract, and inoculation methods were conducted

as described by Melotto and Kelly (2000). In the inocula-

tion treatment, a spore suspension (1 9 106 spores/mL)

was sprayed onto 10 day old seedlings (SEL 1308 and

positive control genotypes), which were maintained under

controlled conditions at 22�C, ±100% humidity, and a

16 h light:8 h dark photoperiod. Symptoms were evaluated

7 days after inoculation and, as expected, only the sus-

ceptible variety showed anthracnose reactions. Hypocotyls,

epicotyls and leaves of SEL 1308 from control and inoc-

ulated treatments were separately collected at three dif-

ferent periods: 1, 48 and 96 h post-inoculation (hpi). As

result, each sample represented a bulk from five individual

plants. In addition, the experiment was repeated three

times, and another total RNA bulk was obtained, resulting

in three experimental bulks (hypocotyls, epicotyls and leaf

bulk). Plant materials were immediately frozen in liquid

nitrogen and maintained at -80�C until RNA isolation.

Drought, salinity and cold treatments

Seeds of a P. vulgaris variety (BAT 477) described as

tolerant to drought stress (Guimaraes 1992), were sterilized

828 Plant Cell Rep (2012) 31:827–838

123

in a 10% sodium hypochlorite solution for 10 min and later

were germinated and grown in potting mix (Plantmax�) in

the greenhouse under a medium-day photoperiod (12 h

light:12 h dark) at 25�C and with regular irrigation before

treatments. In each drought, salinity and cold treatment, ten

plants were used at the V3 stage (emission of the first

trifoliate leaf). As a control condition, ten plants were kept

in the initial conditions until the sampling procedure. The

abiotic treatments were set as follows: (1) in drought stress,

the water supply was withheld for 4 days; (2) in salinity

stress, a shock with a solution of 250 mM NaCl was pro-

vided to the plants for 24 h; (3) in cold stress, the plants

were maintained at 4�C for 24 h; (4) in severe water deficit,

30 plants were kept without irrigation for 6, 8 and 10 days.

Bulks for leaves, stem and roots in each treatment were

obtained by mixing ten plants, with the exception of severe

stress where just leaves were collected. Then, samples were

frozen in liquid nitrogen and stored at -80�C until RNA

extraction.

Candidate gene selection and primer design

The gene sequences used in this study were obtained through

bibliographic reviews of studies involving biotic and abiotic

stresses in common bean or related species (Libault et al.

2008; Hu et al. 2009; Thibivilliers et al. 2009), followed by

an in silico analysis using the BLAST tool (Altschul et al.

1990) of the NCBI database. For instance, a previously

selected EST was submitted to the BLASTN tool to obtain

EST homologues in the common bean (Table 1). Subse-

quently, we used NCBI non-redundant protein sequence

database (nr) to confirm the sequence function using

BLASTX tool (http://blast.ncbi.nlm.nih.gov/Blast.cgi).

To design primers, PRIMER 3 software (http://frodo.wi.

mit.edu/primer3/) was used considering the following

parameters: (a) product size range: 100–200 bp; (b) primer

size: 18–22 bp; (3) primer Tm: 57�–63�C. Primer quality

was evaluated in silico at the Oligo Analysis Tool, which is

in the public domain (www.operon.com).

Total RNA extraction

For each sample, 100 mg of plant material was added to

1 ml of TRIzol� (TRIzol� Reagent Kit, Invitrogen), fol-

lowing procedures recommended by the manufacturer. To

evaluate the quality and quantity of total isolated RNA, we

used a NanoDropTM 2000c spectrophotometer (Thermo

Scientific), considering the ideal absorbance ratio

(1.8 B A260/280 B 2.0), and visualized the band patterns

of total RNA by electrophoresis on a 1.5% agarose gel.

Purified total RNA was guaranteed by carrying out DNAse

treatment using a DNAse1 RNAse-free kit (Fermentas),

following the manufacturer’s instructions.

Quantitative cDNA amplification by RT-qPCR

Quantitative amplification reactions of cDNAs from candi-

date reference genes and target genes were carried out on

StepOnePlusTM Real Time PCR System (Applied Biosys-

tems) equipment in one step using 100 ng of total RNA, 2X

SensimixTM SYBR & ROX one-Step kit (Quantace), and

250 nM of each primer. The reaction conditions were set as

follows: 10 min at 42�C; 10 min at 95�C; 40 cycles of cDNA

amplification for 15 s at 95�C, 30 s at 60�C, 30 s at 72�C with

fluorescent signal recording. At the end, a final step of 15 s at

95�C, 1 min at 60�C and fluorescence measured at each 0.7�C

variation (from 60�C to 95�C) was included to obtain the

melting curve. Three technical replicates were performed.

Data analysis

Raw data (not baseline corrected) of fluorescence levels

were submitted to LinRegPCR software (Ramakers et al.

2003). This program performs baseline correction and

linear regression analysis on each amplification curve. In

addition, the optimal set of data points (Window-of-Line-

arity) was defined to allow the calculation of the threshold

and quantification cycle (Cq) values. Furthermore, the

efficiency was calculated based on slope of the line

(E = 10slope), considering an ideal value range

(1.8 B E B 2) and correlation (R C 0.995).

The expression stability of each candidate reference

gene and the best combination of normalizer genes for each

stress condition were obtained using a pairwise method by

geNorm (Vandesompele et al. 2002) and a model-based

method by NormFinder (Andersen et al. 2004) software,

using the comparative Cq method. To define the optimal

number of genes required for normalization, geNorm

platform estimates a normalization factor (NFn) by geo-

metric average of the n best reference genes and performs a

stepwise analysis (more stable to less stable genes) to

calculate the pairwise variation (Vn/Vn?1) between two

sequential normalization factors, NFn and NFn?1, including

more genes in each comparison (Tunbridge et al. 2011).

Reference genes validation

The coding sequences for a beta-1,3-glucanase 1 (BG1) and

for a dehydration-responsive element binding (DREB)

(Table 1) were used as target genes in order to validate the

best combination of reference genes for normalization of

relative expression in biotic and abiotic treatments, respec-

tively. To analyze the effect of different amounts of refer-

ence genes in the analysis, relative expression obtained for

target genes were compared when eight different candidate

normalizer genes were used individually or in combination.

Then, the best combination was obtained either by geNorm

Plant Cell Rep (2012) 31:827–838 829

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as by NormFinder softwares for all samples. In this stage,

samples of leaves from control and from 48 and 96 hpi

treatments (SEL 1308) were considered for biotic stress

analysis, whereas stem samples from control, drought, high

salinity and cold temperature treatments (BAT 477) were

used for abiotic stress analysis. The relative expression of

BG1 and DREB genes was obtained by REST software

(Pfaffl et al. 2002) using average values of efficiency and Cq

of target and reference genes. This software compares con-

trol and treatment Cq values to obtain the concentration of

expression (C), where C ¼ EmeanCqðcontrolÞ�meanCqðtreatmentÞ;then, it calculates the relative expression (RE) ratio, where

RE = Ctarget gene/geometric average Creference gene; and per-

forms a pairwise fixed reallocation randomization test

(bootstrap = 2,000 permutation) to obtain p values.

Results and discussion

Quantification cycles (Cq)

The first evaluation that can be made regarding RT-qPCR

data when studying reference genes is the calculation of the

coefficient of variation (CV) among Cqs obtained for each

gene when multiple treatments are considered. The lower a

Cq variation, the higher its stability. So that, in this work,

we analyzed the Cq values of each gene in all samples

submitted to biotic and abiotic stresses and found a varia-

tion from 19.1 for the Ubq gene (biotic stress) to 27.0 for

the Ukn2 gene (abiotic stress), which is within the range

recommended for gene expression analysis by RT-qPCR

(Fig. 1). This is suggested owing to the establishment of Cq

values lower than 30, to avoid interfering in the reaction,

and Cqs greater than 15 to better determine the baseline

(Karlen et al. 2007). In addition, the choice of reference

genes must consider a range of Cq values that includes Cq

values of target genes in order to guarantee similar effi-

ciency estimation among genes and, consequently, accurate

results. Therefore, based on these preliminary results, all

candidate reference genes chosen for this study have the

potential to perform normalization of relative quantifica-

tion of gene expression by RT-qPCR after a more detailed

analysis.

In general, our candidate genes showed a low coefficient

of variation in Cq values (CV \ 5% in biotic stress treat-

ment and CV \ 9% in abiotic stress treatments), showing

Table 1 Common bean sequences used as template for primer design for RT-qPCR analysis

NCBI GI Gene

abbreviation

Functional annotation Sequence: 50 ? 30 (Forward/

Reverse)

Amplicon

size (bp)

Tma

(�C)

Biotic PCR

efficiencybAbiotic PCR

efficiencyc

170752836 Ubq Ubiquitin-conjugatingenzyme

AGAAAAGCCCCCAAGTGTTC 161 84.6 1.963 ± 0.354 1.882 ± 0.054

CTGCCATCTCCTTCTTCAGC

171656467 b-Tub9 Tubulin beta-9 TTTCCTTCCCCCAAGGTATC 164 79.4 1.917 ± 0.024 1.951 ± 0.069

TCCCCAAAAGATGGTGTAGC

171614244 IDE Insulin degradingenzyme

GCAACCAACCTTTCATCAGC 156 81.1 1.928 ± 0.012 1.967 ± 0.043

AGAAATGCCTCAACCCTTTG

187434529 Skip16 SKP1/ASK-interactingprotein 16

CACCAGGATGCAAAAGTGG 163 81.5 1.922 ± 0.012 1.953 ± 0.065

ATCCGCTTGTCCCTTGAAC

187435357 Ukn1 Unknown ATTCCCATCATGCAGCAAAG 192 79.4 1.922 ± 0.021 1.945 ± 0.058

AGATCCCTCCAGGTCAATCC

62707797 Ukn2 Unknown CCAATTCAACCATCCCTCAC 153 79.4 1.938 ± 0.017 1.935 ± 0.047

AAACTCCTCTGCACCCTCAG

62703083 Act11 Actin-11 TGCATACGTTGGTGATGAGG 190 79.4 1.944 ± 0.012 1.917 ± 0.069

AGCCTTGGGGTTAAGAGGAG

171656465 b-Tub8 Tubulin beta-8 AATGTGAAGTCCAGCGTGTG 163 80.4 1.954 ± 0.016 1.913 ± 0.094

CTTCCCCAGTGTACCAATGC

59937438 BG1 Beta-1,3-glucanase 1 AGCAGCTCTGCAAGCACTCA 100 81.1 1.923 ± 0.011 –

ACGAGCAGTGTCGGCATTG

157429777 DREB Dehydrationresponsive elementbinding

GATGAGGAAGTGGGGGAAGT 177 85.3 – 1.964 ± 0.041

TCGTCTTGGGAGAGCAGTTC

a Melting temperatureb Amplicon efficiency average for all samples in biotic stressc Amplicon efficiency average for all samples in abiotic stress

830 Plant Cell Rep (2012) 31:827–838

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higher stability in expression levels under biotic than abi-

otic stress conditions, where more treatments were con-

sidered. In samples submitted to the incompatible reaction

with C. lindemuthianum, the lowest variation was observed

for Skip16 (CV = 2.43%) and IDE (CV = 2.79%); con-

versely, Ubq and b-Tub9 had the greater variations

(CV = 4.24 and 4.41%, respectively). In samples sub-

jected to different abiotic stresses, Ubq expression was less

variable (CV = 4.14%), along with Skip16 (CV =

5.26%)¸which proved be highly stable, while Ukn1

(CV = 6.61%) and b-Tub8 (CV = 8.57%) were the most

variable. From these initial findings, the Skip16 gene would

be suitable for the analysis of changes in expression levels

in common beans during biotic and abiotic stress; however,

the calculation of the coefficient of variation of Cq values

can be considered as a preliminary step in the selection of

internal controls for the quantitative RT-PCR method of

analysis, since to confirm the stability of genes, it is also

necessary to linearize the data considering not only Cq

values, but also the efficiencies of the amplification curves.

Efficiency of amplification curves

PCR efficiency of amplicons has been shown to be a

decisive aspect in the calculation of changes in gene

expression levels. Early works assumed the same PCR

efficiency for all amplicons in a study (e.g. E = 2 or 100%

efficiency); later, standard curves constructed through

serial dilutions of cDNA samples (Pfaffl 2001) were

adopted to overcome the introduced bias. Moreover, the

estimation of PCR efficiency via the increase in absolute

fluorescence developed by Ramakers et al. (2003) has also

been used, mainly because it allows for the calculation of

PCR efficiency for individual samples/reactions and pre-

vents some problems, which arise from standard curves.

Here, the program LinRegPCR (Ramakers et al. 2003) was

applied to the raw fluorescence data to obtain individual

efficiency values, i.e. for each amplicon in each sample.

In general, the amplification curves obtained in this

work had high average values of PCR efficiency (Table 1).

Except for the Ubq amplicon, which showed an average

efficiency of 1.882 in abiotic stress treatments, all candi-

date reference genes and target genes showed mean values

of efficiency greater than 1.90. These represent specific

transcripts being amplified at least at 90% efficiency per

cycle in the qPCR reactions. In this study, the calculation

of expression levels for target and candidate reference

genes was based on the mean efficiencies of amplicons

from the technical triplicate rather than individual effi-

ciencies. The use of individual efficiency values, generally

more variable, can result in biased data, and since this is

inserted during the processing of data to a linear scale, it

can strongly affect the results of gene expression analysis

(Ruijter et al. 2009).

Stability values by geNorm

Despite the importance of primer specificity and efficiency

during the selection of a reference gene, an essential aspect

to be considered in this choice is its stability over different

samples, treatments, etc., which is crucial for generating

reliable results. To evaluate the potential of eight putative

normalizers, we used two methods, one of them the geN-

orm (Vandesompele et al. 2002). We performed the tests

separately for each stress treatment and then analyzed each

set of samples included in our conditions.

The gene IDE was verified to have the lowest stability

values (M) when samples submitted to biotic stress were

analyzed (Fig. 2), which in geNorm analysis indicates high

stability. However, different combinations between genes

showed low M values when samples were grouped according

to treatment or tissue (Fig. 2). Therefore, the gene pairs

Fig. 1 Average Cq values of

candidate reference genes used

in the study. a All samples of

the incompatibility interaction

(biotic stress); b all samples of

the drought, salinity and cold

temperature treatments (abiotic

stress)

Plant Cell Rep (2012) 31:827–838 831

123

indicated for expression normalization were IDE/Ukn2 for a

general biotic stress study; IDE/b-Tub8 for different tissues

and Skip16/Act11, Skip16/b-Tub8 and b-Tub9/IDE for

leaves, epicotyl and hypocotyl, respectively, under incom-

patible interaction with C. Lindemuthianum (Fig. 2).

The homologue gene to IDE (cons7) was defined as a

reference gene in a microarray study on soybean under

several conditions (Libault et al. 2008). In this work, the

authors analyzed soybean in a range of different tissues

under biotic and abiotic stresses, and cons7 was considered

one of the most stable genes in all treatments. In another

study, this same gene was also determined as the most

stable and suitable for validation of subtractive libraries of

common bean during compatible and incompatible inter-

actions with the fungus U. appendiculatus (Thibivilliers

et al. 2009). This gene encodes for a peptidase, homolo-

gous to insulin-degrading peptidase in animals, which in

plant cells would be related to the cleavage control of

proline-rich signaling proteins (Cunningham and O’Connor

1997). Thus, its performance in other studies and its

important role in basic cell processes indicate that this gene

does have potential as a normalizer gene.

The best combination of reference genes in our abiotic

stress treatments (tissues and stresses without grouping)

was Ukn1/Act11 (Fig. 3a). As for biotic treatments, dif-

ferent sample grouping led to different combinations of

best genes, but both the Ukn1 and Act11 were present in

many situations and had the overall lowest M values

(Fig. 3). Actin genes are generally chosen as reference

genes since they participate in basic and essential processes

in the cell; e.g.: Actin has been considered highly stable

when validating reference gene for gene expression studies

in chicory (Maroufi et al. 2010) and lichi (Zhong et al.

2011). On the other hand, its partner gene indicated here is

an unknown EST, which in a microarray study on soybean

presented no significant changes in its expression (Libault

et al. 2008). This finding shows that a reference gene does

not necessarily need to be among those usually described,

and that many transcriptomics studies could be essential

sources to select candidate reference genes for expression

normalization. Figure 3 shows that, together with other

highly stable genes (Skip16, b-Tub9, IDE and Ubq), these

genes can be used as normalizers in expression analysis

according to the goal of the study, namely, the influence of

Fig. 2 Average expression stability values (M) of candidate refer-

ence genes by geNorm analysis. a All samples (leaf, epicotyl and

hypocotyl under 1, 48 and 96 h post-inoculation); b leaf, epicotyl and

hypocotyl in the control treatment; c leaf in control and inoculated

treatment; d epicotyl in control and inoculated treatment; e hypocotyl

in control and inoculated treatment; f pairwise variation analysis to

select the optimal number of reference genes

832 Plant Cell Rep (2012) 31:827–838

123

different abiotic stresses, distinct tissues or only stress

severity. Thus, the variable performance of various candi-

date genes in certain treatments corroborates the impor-

tance of validating normalizing genes for conditions of

interest.

The pairwise method applied by geNorm may be

sensitive to genes coding for proteins of the same

functional class (Andersen et al. 2004), tending to group

them in the analysis. Nevertheless, in our work, the

occurrence of this type of biased result was not observed,

Fig. 3 Average expression stability values (M) of candidate refer-

ence genes by geNorm analysis. a All samples (leaf, stem and root

under drought, salinity and cold stress); b leaf, stem and root under

drought stress; c leaf, stem and root under salinity stress; d leaf, stem

and root under cold stress; e leaf under drought, salinity and cold

stress; f stem under drought, salinity and cold stresses; g root under

drought, salinity and cold stresses; h leaves after 6, 8 and 10 days of

drought; i pairwise variation analysis to select the optimal number of

reference genes

Plant Cell Rep (2012) 31:827–838 833

123

since the best combination presented only unrelated

genes (Figs. 2, 3).

Another important analysis carried out by geNorm was the

determination of the optimal number of reference genes, or

the effect of adding an extra gene in the analysis, through the

calculation of pairwise variation (Vn/Vn?1) between two

sequential candidate genes. High values indicate the need for

the inclusion of a gene to obtain a reliable normalization fac-

tor, which should contain at least two internal controls. Thus,

extra reference genes can be included until the Vn/Vn?1 value is

smaller than a threshold, which here was 0.15 as recom-

mended by Vandesompele et al. (2002). Based on this

parameter, two reference genes were recommended for all

conditions under biotic stress, as well as control tissues, in the

expression analysis of target genes by quantitative RT-PCR,

since the pairwise variation values were less than 0.15

(Fig. 2f). Moreover, although the majority of abiotic stress

treatments demonstrated the need for only two reference

genes, an exception occurred in the analysis grouping samples

from different tissues (leaves, stems and roots) under different

types of abiotic stress (drought, salinity and cold) (Fig. 3h). In

this set of samples, at least three reference genes would be

recommended to maintain the average pairwise variation at the

threshold. However, the threshold-based choice of the number

of normalizers does not need be an exclusive criterion, since

the researcher may consider other factors such as cost analysis

and more accurate data, and can therefore assume larger

numbers of genes in order to increase data accuracy, or even

fewer genes when many treatments and tissues are analyzed

(Hu et al. 2009). In our study, where three reference genes

were recommended, we could assume two internal controls,

considering the large number of treatments and tissues.

Stabilities values by NormFinder

In NormFinder program, stability analysis can be carried

out with all samples or in each treatment with or without

group determination. However, sample grouping is con-

sidered more informative as it takes into account inter- and

intra-group variations, from which the best combination of

genes can be defined for each set of samples analyzed.

According to this, the best genes are determined due to low

values of differential expression between and within groups

(Andersen et al. 2004).

Like the results obtained from the pairwise method,

NormFinder analysis showed that the IDE gene was the

most stable gene in sample groupings from the incompat-

ibility reaction (common bean/avirulent pathogen) and

control condition (not inoculated), followed by Act11

(Table 2). IDE showed again high stability as an internal

control under biotic stress treatments analyzed individually

(Table 2). In addition, the best combination obtained for

gene expression analysis of different control tissues was

Ubq/Act11, while IDE/Act11 was elected to better nor-

malize genes for samples under different time points of

biotic stress (Table 2).

When abiotic stresses were analyzed, results very sim-

ilar to those of geNorm were obtained. According to

NormFinder, the most stable gene to be used as an internal

control when all kinds of stress and tissue are being studied

was Act11 (Table 3). To study different abiotic treatments

grouped by the type of plant tissue, the best combination of

two genes was Act11/b-Tub8; to work with distinct tissues

experiencing a series of abiotic stress, Act11/Skip16 was

the best pair of normalizers; and when drought severity is

considered, the pair Ukn2/Ukn1 was indicated (Table 3).

Hu et al. (2009) reported the homologues, Ukn1 and Ukn2,

as reliable reference genes for soybean under various

treatments; and in special, Ukn2 showed also stable

expression across tomato development and tissues (Expo-

sito-Rodrıguez et al. 2008).

For each set of samples submitted to NormFinder

analysis, a ranking of genes was given according to the

calculated values of stability, allowing one to choose the

Table 2 Ranking of candidate

reference genes based on

stability values calculated by

NormFinder software for biotic

stress treatments, including a

non-inoculated group and

inoculated group analysis of leaf

(L), epicotyl (E), hypocotyl

(H) tissues and control (C) in 1,

48, 96 h post-inoculation

periods under incompatible

interaction

Ranking Control (L:E:H) Leaf Epicotyl Hypocotyl (C:1:48:96)

1 IDE IDE Act11 IDE IDE

2 Skip16 Ukn1 IDE Ukn2 Act11

3 b-Tub8 Act11 Ukn1 b-Tub8 Ukn2

4 Ukn2 Skip16 b-Tub8 Skip16 Ukn1

5 Act11 Ukn2 Ukn2 b-Tub9 Skip16

6 Ukn1 Ubq b-Tub9 Ukn1 b-Tub9

7 Ubq b-Tub9 Skip16 Ubq b-Tub8

8 b-Tub9 b-Tub8 Ubq Act11 Ubq

Best stable gene IDE IDE Act11 IDE IDE

Stability values 0.062 0.037 0.028 0.070 0.042

Best combination of two genes Ubq/Act11 IDE/Act11

Stability values 0.056 0.062

834 Plant Cell Rep (2012) 31:827–838

123

better normalizer for an experiment, either for individual or

for combined treatments (Tables 2, 3).

Interestingly, despite belonging to important classes for

cellular functioning, Ubq and b-Tub9 genes performed as

less stable in both geNorm and NormFinder in terms of

biotic and abiotic stress. These genes were also evaluated

by Thibivilliers et al. (2009), showing low stability during

validation of a subtractive library of common bean. Fur-

thermore, relative expression of Tubulin and Ubiquitin

genes were reported as highly variable during various

developmental stages in Arabidopsis (Gutierrez et al.

2008); while Tubulin gene showed low stability for tomato

treatments (Exposito-Rodrıguez et al. 2008); and Ubiquitin

gene presented poor performance as reference gene in

soybean during different conditions (various developmen-

tal stages, photoperiods and cultivars) (Jian et al. 2008).

These variations of genes normally used as internal con-

trols confirm the need for validation of reference genes

under specific conditions of interest.

Validation of references genes

After the determination of internal controls for an RT-

qPCR experiment, it is important to validate them through

the expression analysis of a target gene for which the

pattern is already known or previously described for the

treatment to be analyzed. Thus, to validate the results

obtained for reference genes in biotic stress, we conducted

a relative expression analysis of a BG1 gene comparing

control samples at 48 and 96 hpi in SEL 1308 leaves

during incompatible interaction with race 73 of

C. lindemuthianum.

Under these conditions, BG1 was approximately up-

regulated by twofold in 48 hpi samples and five to sixfold

in 96 hpi samples after expression normalization, showing

significance at least at 95% for six genes, with the excep-

tion of b-Tub9 and Ubq (Table 4) which had already been

observed as poor reference genes for our conditions. In this

study, it was expected that suitable internal controls could

reveal positive and significant relative quantification (RQ)

values in the biotic stress treatment, since the gene

encoding beta-1,3-glucanase 1 was found in an EST library

of common bean (Melotto et al. 2005), which showed a

larger number of this transcript in the treatment inoculated

with the fungus C. lindemuthianum. In addition, the prod-

uct of this gene acts directly on the pathogen cell wall,

attempting to interact with glucans, resulting in instability

of the pathogenic agent’s cell wall and thereby preventing

its growth (Ferreira et al. 2007). Therefore, this gene, used

to validate our work, is suggested to participate directly in

plant defense mechanisms against the pathogen.

Using the gene combinations determined by both geN-

orm and NormFinder, the same results were observed, withTa

ble

3R

ank

ing

of

can

did

ate

refe

ren

ceg

enes

bas

edo

nst

abil

ity

val

ues

calc

ula

ted

by

No

rmF

ind

erso

ftw

are

for

abio

tic

trea

tmen

ts,in

clu

din

ga

no

n-s

tres

sed

gro

up

and

stre

ssed

gro

up

anal

ysi

so

f

leaf

(L),

stem

(S),

roo

t(R

)ti

ssu

es,

con

tro

l(C

),d

rou

gh

t(D

),sa

lin

ity

(S)

and

cold

tem

per

atu

re(C

o)

con

dit

ion

s,an

dte

mp

ora

lan

aly

sis

of

dro

ug

ht

stre

ss(6

,8

and

10

day

s)

Ran

kin

gT

issu

e/st

ress

es

(no

gro

up

ing

)

Lea

fS

tem

Ro

ot

L:S

:RC

:D:S

:Co

C:D

C:S

C:C

oC

:6:8

:10

1A

ct1

1A

ct1

1ID

EID

EA

ct1

1A

ct1

1S

kip

16

Ski

p1

6U

kn1

Ukn

2

2S

kip

16

b-T

ub

8U

bq

Ski

p1

6S

kip

16

Ski

p1

6U

kn2

Act

11

Ski

p1

6U

kn1

3U

kn2

Ukn

1S

kip

16

Ukn

2b

-Tu

b8

Ukn

1U

kn1

Ukn

2ID

EID

E

4U

kn1

b-T

ub

9A

ct1

1A

ct1

1ID

EU

kn2

Act

11

b-T

ub

8A

ct1

1S

kip

16

5b-

Tu

b8

Ski

p1

6U

kn1

b-T

ub

8U

kn1

b-T

ub

8b-

Tu

b8

Ukn

1U

kn2

Act

11

6ID

EID

Eb-

Tu

b9

Ukn

1b

-Tu

b9

b-T

ub

9U

bq

IDE

b-T

ub

8b

-Tu

b9

7b-

Tu

b9

Ukn

2b-

Tu

b8

b-T

ub

9U

kn2

IDE

IDE

b-T

ub

9b-

Tu

b9

b-T

ub

8

8U

bq

Ub

qU

kn2

Ub

qU

bq

Ub

qb-

Tu

b9

Ub

qU

bq

Ub

q

Bes

tst

able

gen

eA

ct1

1A

ct1

1ID

EID

EA

ct1

1A

ct1

1S

kip

16

Ski

p1

6U

kn1

Ukn

2

Sta

bil

ity

val

ues

0.1

31

0.0

68

0.0

80

0.1

15

0.1

19

0.0

95

0.0

70

0.0

37

0.1

06

0.0

13

Bes

tco

mb

inat

ion

of

two

gen

esA

ct1

1/b

-Tu

b8

Ski

p1

6/A

ct1

1S

kip

16/U

kn2

Ski

p1

6/A

ct1

1S

kip

16/U

kn1

Ukn

1/U

kn2

Sta

bil

ity

val

ues

0.0

98

0.0

89

0.0

68

0.0

46

0.1

06

0.0

83

Plant Cell Rep (2012) 31:827–838 835

123

BG1 significantly up-regulated in inoculated samples

(Table 4). So, the two combinations showed potential as

reference genes in the quantitative analysis of target genes

in common bean subjected to biotic stress, but IDE/Act11

can be highlighted due to the lower standard error

generated.

The relative expression of a DREB gene was carried out

to validate reference genes determined for the analysis of

common bean samples subject to different abiotic stresses.

For this, stems from the BAT 477 variety cultivated under

control conditions were compared with their counterparts

under drought, salinity and cold stress. As expected, in all

cases, DREB expression was significantly up-regulated at

1% (Table 5). DREB genes belong to a family of tran-

scription factors that contain an AP2 binding domain and

act on cis-elements, regulating plant responses to several

environmental stress conditions (Agarwal et al. 2006). The

gene sequence of P. vulgaris analyzed here is homologous

to GmDREB2 and AtRAP2.10. It belongs to one of the six

DREB subgroups, and in a study of expression patterns in

soybean, was demonstrated to be up-regulated under

drought, salinity and cold stresses (Chen et al. 2007).

Relative quantification of the results achieved through

individual expression normalization of DREB with IDE,

Ubq and Skip16 separately corresponded to the high gene

stability observed in stressed samples (Fig. 3f; Table 3).

Table 4 Relative quantification (RQ) of BG1, standard error (SE) and p values in SEL 1308 leaves under incompatible interaction with C.lindemuthianum for 48 and 96 h post-inoculation

Normalizer 48 hpi 96 hpi

RQ SE p RQ SE p

Ubq 1.159 0.141 0.788 4.403 0.285 0.077

b-Tub9 1.284 0.135 0.499 5.732 0.745 0.036

IDE 1.869 0.201 0.001 5.383 0.288 0.036

Skip16 1.831 0.279 0.001 5.116 0.819 0.036

Ukn1 1.996 0.253 0.001 6.543 0.438 0.036

Ukn2 2.022 0.423 0.001 6.065 1.034 0.036

Act11 1.738 0.272 0.042 5.548 0.401 0.036

b-Tub8 2.069 0.228 0.001 6.744 0.535 0.036

IDE/Act11a 1.802 0.194 0.001 5.465 0.293 0.036

IDE/Ukn2b 1.944 0.209 0.001 5.714 0.306 0.036

Candidate reference genes were used as normalizers individually and following the geNorm and NormFinder best combinationa Best combination by NormFinder considering all samplesb Best combination by geNorm considering all samples

Table 5 Relative quantification (RQ) of DREB, standard error (SE) and p values in BAT 477 stem under drought, salinity and cold temperature

stresses

Normalizer Drought Salinity Cold

RQ SE p RQ SE p RQ SE p

Ubq 3.021 0.331 0.001 5.441 0.509 0.001 5.400 0.435 0.001

b-Tub9 3.394 0.810 0.001 6.206 1.241 0.001 3.297 0.841 0.045

IDE 3.507 0.501 0.001 5.556 0.674 0.001 6.324 1.199 0.001

Skip16 2.771 0.308 0.001 7.072 0.700 0.001 7.647 0.687 0.001

Ukn1 5.203 0.595 0.001 12.355 1.143 0.001 6.298 0.680 0.001

Ukn2 6.079 1.316 0.001 10.327 2.283 0.001 29.930 9.406 0.001

Act11 2.710 0.406 0.001 7.623 1.150 0.001 4.566 0.658 0.001

b-Tub8 5.811 1.364 0.001 8.402 1.321 0.001 4.493 0.681 0.001

Act11/b-Tub8/Ukn1 4.343 0.651 0.001 9.250 1.395 0.001 5.055 0.729 0.001

Act11/b-Tub8 3.968 0.595 0.001 8.003 1.207 0.001 4.529 0.653 0.001

Skip16/Act11 2.740 0.411 0.001 7.343 1.108 0.001 5.909 0.852 0.001

Candidate reference genes were used as normalizers individually and following the geNorm and NormFinder best combination

836 Plant Cell Rep (2012) 31:827–838

123

Moreover, gene combinations obtained by statistical

methods for reference gene selection (geNorm and Norm-

Finder) could also be validated, once DREB repeated the

results of significant up-regulations, presenting low stan-

dard errors in the RQ analysis (Table 5). Still, combina-

tions of just two normalizer genes were able to reproduce

the same results as described above, with Skip16/Act11

contributing to the achievement of lower standard errors in

the treatments that included drought stress and salinity

(Table 5).

Final considerations

Different combinations of reference genes were suggested

in order to analyze specific experimental conditions

involving biotic and abiotic stresses. However, considering

all samples and treatments, it is possible to highlight the

genes IDE and Act11 as important normalizers for the

expression analysis of common bean under biotic stress,

and Skip16 and Act11 for abiotic stress. Potential normal-

izers were determined; however, the analysis for each

condition should be conducted once gene expression shows

considerable experimental and tissue-specific variations.

The data generated in this work contribute to increasing the

set of reference genes available for the normalization of

common bean genes by the RT-qPCR method.

Acknowledgments We greatly thank Dr. Adriane Wendland from

the National Rice and Beans Research Center (EMBRAPA), Goias,

Brazil, for providing strains of the fungus; the Center for Phytosan-

itary Research and Development (IAC), Campinas, Brazil, for the

preparation of fungus inoculum; the Center for Analysis and Tech-

nological Research of Grain and Fiber Agribusiness (IAC), Campinas,

Brazil, for verifying the pathogen race; CNPq (National Council for

Scientific and Technological Development) for the scholarship and

financial support (Universal-474337/2008-1); and CAPES for the

post-doctoral fellowship.

Conflict of interest The authors declare that they have no conflicts

of interest.

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