Comparative analysis of drought-responsivetranscriptome in Indica rice genotypes with contrastingdrought toleranceSangram K. Lenka1, Amit Katiyar1, Viswanathan Chinnusamy2 and Kailash C. Bansal1,*

1National Research Centre on Plant Biotechnology, Indian Agricultural Research Institute, New Delhi, India2Department of Botany and Plant Sciences, University of California, Riverside, CA, USA

Received 24 March 2010;

revised 24 May 2010;

accepted 2 June 2010.

*Correspondence (Tel 91 11 25843554;

fax 91 11 25843984; email

kailashbansal@hotmail.com)

Keywords: drought, rice, microarray,

pathway analysis, a-linolenic acid.

SummaryGenetic improvement in drought tolerance in rice is the key to save water for sus-

tainable agriculture. Drought tolerance is a complex trait and involves interplay of a

vast array of genes. Several genotypes of rice have evolved features that impart tol-

erance to drought and other abiotic stresses. Comparative analysis of drought stress-

responsive transcriptome between drought-tolerant (DT) landraces ⁄ genotypes and

drought-sensitive modern rice cultivars will unravel novel genetic regulatory mecha-

nisms involved in stress tolerance. Here, we report transcriptome analysis in a highly

DT rice landrace, Nagina 22 (N22), versus a high-yielding but drought-susceptible rice

variety IR64. Both genotypes exhibited a diverse global transcriptional response under

normal and drought conditions. Gene ontology (GO) analysis suggested that drought

tolerance of N22 was attributable to the enhanced expression of several enzyme-

encoding genes. Drought susceptibility of IR64 was attributable to significant down-

regulation of regulatory components that confer drought tolerance. Pathway analysis

unravelled significant up-regulation of several components of carbon fixation, glycol-

ysis ⁄ gluconeogenesis and flavonoid biosynthesis and down-regulation of starch and

sucrose metabolism in both the cultivars under drought. However, significant up-

regulation of a-linolenic acid metabolic pathway observed in N22 under drought

appears to be in good agreement with high drought tolerance of this genotype.

Consensus cis-motif profiling of drought-induced co-expressed genes led to the

identification of novel cis-motifs. Taken together, the results of the comparative

transcriptome analysis led to the identification of specific genotype-dependent genes

responsible for drought tolerance in the rice landrace N22.

Introduction

Yield potential of rice is drastically reduced by drought

across all agro-climatic regions of the globe. Hence, it is

necessary to produce more ‘crop per drop’ to sustain our

agricultural production (Witcombe et al., 2008). Rice has

very low water-use efficiency, and about 50% of irrigated

fresh water is used for rice cultivation alone. Therefore,

genetic improvement in drought tolerance in rice assumes

significance owing to dwindling water resources exacer-

bated by global climate change. Centuries of continuous

selection and breeding efforts in different agro-climatic

conditions led to the evolution of rice cultivars with wide

range of drought and other abiotic stress tolerance. Some

traditional cultivars and landraces of rice have evolved

mechanisms that impart tolerance to various abiotic stres-

ses such as drought, salinity, etc. These tolerant genotypes

are excellent genetic resources for stress tolerance but are

poor yielders. The Indian landrace selection Nagina 22

(N22) is one such example of a traditional genotype that is

highly tolerant to drought and is a donor of drought toler-

ance traits in breeding programmes (Reddy Ch et al.,

2009). On the contrary, most high-yielding modern rice

varieties are developed for best performance under

ª 2010 The AuthorsPlant Biotechnology Journal ª 2010 Society for Experimental Biology and Blackwell Publishing Ltd 1

Plant Biotechnology Journal (2010), pp. 1–13 doi: 10.1111/j.1467-7652.2010.00560.x

irrigated conditions. One such widely grown lowland

Indica variety IR64 is high yielding but susceptible to

drought stress (Lafitte et al., 2007). Identification and

characterization of novel genes for drought tolerance by

comparative transcriptomic studies in a drought-tolerant

(DT) versus susceptible cultivar will help in understanding

evolutionary basis of stress adaptation mechanisms, facili-

tating thereby crop improvement via genetic engineering

and precision breeding.

Drought tolerance is a complex trait that involves several

metabolic and morphological adaptive pathways. Hence,

deciphering genetic basis of drought tolerance mecha-

nisms in plants still remains a challenging task (Price et al.,

2002). Drought-responsive genes encode proteins involved

in signalling, gene expression, stress damage control and

repair (Valliyodan and Nguyen, 2006). Transcriptional reg-

ulation is an important regulatory mechanism of repro-

gramming of transcriptome in response to drought.

Several genes induced by abiotic stresses, including those

encoding transcription factors (TFs), have been identified,

and some of them have been shown to be essential for

stress tolerance (Hu et al., 2006; Guo et al., 2008; Oh

et al., 2009). Genome-wide identification of drought-

responsive regulons in contrasting DT genotypes will help

to unravel system-level interplay between different genetic

pathways that impart drought tolerance. Although some

genes involved in stress response have been identified, the

key water-deficit stress signalling components that are

responsible for genotype-dependent drought tolerance

have mostly remained unidentified (Wang et al., 2007;

Degenkolbe et al., 2009).

Availability of diverse genetic resources for stress toler-

ance coupled with advanced genomics tools made rice a

popular model system for abiotic stress research (IRGSP,

2005; Xu et al., 2006). Perception of drought stress fol-

lowed by succession of signal transduction events to

switch on molecular, cellular and whole plant adaptive

processes are critical steps for stress tolerance. The spatio-

temporal gene regulation in plants is governed by combi-

natorial interactions of cis-acting DNA elements in the

promoters with trans-acting protein factors. Genes encod-

ing TFs represent a considerable fraction of the genomes

of all eukaryotic organisms, including higher plants

(Riechmann et al., 2000). Out of total annotated genes,

TFs constitute approximately 2.6% of the rice genome

(Gao et al., 2006). Molecular genetic and transgenic analy-

sis of abiotic stress tolerance during the past one decade

revealed that transcriptome engineering (use of master

switch genes such as signalling proteins and TFs) is a

viable option for enhancing abiotic stress tolerance of crop

plants (Sreenivasulu et al., 2007). However, functional

characterization of TFs in rice lags considerably behind the

progress made in the model dicotyledonous species Ara-

bidopsis thaliana. Application of modern high-throughput

genomics tools is proving useful in understanding the

molecular mechanisms responsible for the expression of

the abiotic stress tolerance traits. Discovery of novel abi-

otic stress regulatory genes, identification of key pathways

that are altered in response to stress, and functional char-

acterization of the genes involved are imperative to under-

stand stress tolerance mechanisms. Over the past decade,

various studies compared the expression profiling of

stressed versus non-stressed plants and reported several

abiotic stress–related genes (Reddy et al., 2002; Rabbani

et al., 2003; Kushwaha et al., 2009). However, compara-

tive expression profiling of DT versus susceptible geno-

types offers a way to identify novel genes and regulatory

mechanisms with evolutionary adaptive significance.

Here, we report genome-wide drought-responsive tran-

scriptome changes in drought tolerant N22 (DT) versus

drought-susceptible IR-64 (DS) Indica rice genotypes. GO

and enrichment of conserved cis-regulatory elements

within the 1- kb upstream sequences from translational

start site (ATG) of the co-expressed drought-responsive

genes were also investigated. Expression level of selected

drought-responsive TFs was validated using quantitative

real-time PCR (qRT-PCR). Pathway analysis was carried out

to investigate the influence of genotype-dependent

drought-responsive transcriptome on metabolic changes.

Results and discussion

Genotype-dependent drought-responsive

transcriptome

Comparison of the differential expression profiling of

effector and master regulatory genes between a stress-

tolerant and a susceptible genotype of a species in

response to similar level of abiotic stress may help in iden-

tification of underlying metabolic pathways and regulatory

mechanism(s) responsible for adaptation of plants to stress

conditions. Two well-known rice genotypes with contrast-

ing drought stress response were chosen for this study.

N22 is a drought-tolerant variety (DT) adapted to upland

conditions, whereas IR64 is a widely grown shallow-land

variety with high susceptibility to drought (DS) stress

(Reddy et al., 2002; Lafitte et al., 2007; Reddy Ch et al.,

2009). To investigate the intrinsic variation in drought

ª 2010 The AuthorsPlant Biotechnology Journal ª 2010 Society for Experimental Biology and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–13

Sangram K. Lenka et al.2

tolerance of these two genotypes, various indices of

‘stress-induced injury’ were scored in this study. Relative

water content (RWC) under control condition was

recorded as 96% in DT and 94.4% in DS, whereas under

drought treatment, the RWC was reduced to 64.8% and

56.2% in DT and DS, respectively. Similarly, total leaf chlo-

rophyll content was higher in DT (1.7 mg ⁄ g FW under

control and 1.4 mg ⁄ g FW under drought) compared to DS

(1.2 mg ⁄ g FW under control and 0.98 mg ⁄ g FW under

drought). The excised-leaf water content in DT was about

50% in 5- h duration from leaves of both control and

drought-treated plants. In case of DS, the retention of

excised-leaf water was 58% for control leaves but reduced

to 44% in leaves from drought treatment (Figure S1).

Water retention in N22 (DT) excised leaves was always sig-

nificantly higher than in IR64 (DS) leaves at different time

intervals. This shows better ability of DT to conserve leaf

moisture when compared to DS in response to dehydra-

tion. In addition, DT exhibited better drought tolerance

and recovery ability than the DS as observed by visual

comparison of leaf senescence and wilting symptoms in

the two cultivars. DT seedlings developed new leaves and

recovered faster than the DS. Hence, based on the previ-

ous reports and the various physiological parameters mea-

sured in this study, we confirmed that DT and DS differ

significantly in their response to drought stress.

Previous studies reported a comparative analysis of

large-scale expression profiling in a drought stress-treated

and non-treated rice genotype as well in upland and

lowland rice cultivars (Rabbani et al., 2003; Wang et al.,

2007; Zhou et al., 2007; Degenkolbe et al., 2009). These

transcript-profiling data suggested that rice plants per-

ceive and respond to water stress quickly by altering

gene expression. Thus, it is important to analyse tran-

script changes in parallel with biochemical pathway alter-

ations. In the above-mentioned studies, several genes

involved in drought stress response were identified; how-

ever, the key water stress signalling components that are

responsible for genotype-dependent drought tolerance

have mostly remained unidentified. In this study, tran-

scriptome profiling of whole seedlings of Indica rice

genotypes with contrasting drought tolerance was analy-

sed comprehensively. We used transcriptome data for

the identification of differences in drought-induced

changes in biochemical pathways between DT and DS.

Important cis-regulatory element profiling within the

1- kb upstream sequences from translational start site

(ATG) of the co-expressed drought-responsive genes was

also investigated.

Two rice genotypes N22 (DT) and IR64 (DS) were used

for expression analysis under non-stress and water-deficit

stress using GeneChip� Rice Genome Array with three

biological replications (Figure S2). We compared transcrip-

tome response of whole rice seedlings to drought stress.

The correlation coefficients of normalized samples

between any two biological replications ranged from

0.960 to 0.998 indicating that microarray analyses were

highly reproducible in this experiment (Table S1). Data on

expression profiling of probe sets showing more than

twofold up or down-regulation compared with corre-

sponding control condition were chosen for further analy-

sis. These are illustrated in Venn diagrams and profile plot

(Figure S3a–e) and listed in the (Tables S2–S6). To evalu-

ate the genome-wide response of DT and DS under con-

trol and drought conditions, linkage hierarchical clustering

was performed (Figure 1). Genes that expressed under

control condition were clustered together and separated

from the drought-responsive clusters for the two geno-

types, which were also clustered together. This analysis

clearly demonstrated that there was a genotype- and

environment-dependent change in gene expression, and

we presume that the differential gene regulation observed

here was attributable to genotype · environment interac-

tion (G · E). Identification of significantly regulated target

genes, which differed in their expression between DT and

DS under drought stress, might potentially serve as candi-

date alleles for genetic improvement in drought tolerance

in rice. In DS under drought stress, 3097 and 2391 probes

were up- and down-regulated, respectively, compared to

that under control conditions (Table S2), whereas in DT,

4513 probes were up-regulated and 2606 probes down-

regulated under drought compared with control condi-

tions (Table S3). These probe sets represent stress-

responsive genes as per their annotation and belong to

different categories including TF-encoding genes. Many of

these genes were shown previously to be involved in abi-

otic stress response (Reddy et al., 2002; Rabbani et al.,

2003; Wang et al., 2007). Interestingly, comparison of the

expression levels of genes between DT and DS genotypes

revealed that 711 and 757 probes were up -and down-

regulated, respectively in DT compared with DS under

control conditions (Table S4). These genes might be

responsible for higher intrinsic tolerance to abiotic stresses

in DT. Comparison of the expression levels of transcrip-

tome under drought revealed that 1900 probes were

up-regulated and 920 probes were down-regulated in DT

when compared to DS (Table S5). In both basal and

induced transcriptomes, expression levels of several

ª 2010 The AuthorsPlant Biotechnology Journal ª 2010 Society for Experimental Biology and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–13

Drought-responsive transcriptome analysis in rice 3

stress-responsive genes showed differential expression

between DT and DS. This clearly demonstrates that prefer-

ential gene regulation owing to G · E is crucial for deter-

mining transcriptome composition and thus stress

responses of the genotypes suggesting thereby that the

genomes of both the genotypes were preprogrammed to

modulate expression of different sets of genes in response

to both control and drought stress conditions. To identify

drought-responsive probe sets that are regulated by

drought, independent of the genotype, expression pattern

of drought-responsive genes of DS (IR64 control vs.

drought) and DT (N22 control vs. drought) was compared.

In response to drought in the two genotypes, 1789

common probes showed up-regulation, whereas 1573

common probes showed down-regulation (Table S6). We

considered these consensus set of drought-responsive

transcripts across the two genotypes (DT and DS) as

drought-responsive co-expressed genes for over-

represented motif discovery.

Validation of expression of selected TF-encoding

genes

From 1900 probes that were up-regulated and 920 down-

regulated probes under drought in DT compared to DS

(Figure 2, Table S5), 77 up-regulated probes and 14

down-regulated probes represented TFs and displayed a

versatile regulation pattern under different conditions

(Table S7). The identified TFs belonged to diverse family of

TFs such as ZFP, MADS-box, LZP, WRKY, HSF, NAC, NFY,

etc. Interestingly, we found that the genes encoding pro-

teins with zinc finger domains were the most enriched

IR64

con

trol

IR64

dro

ught

N22

con

trol

N22

dro

ught

–3.5

3.5

0

Figure 1 Hierarchical clustering. Hierarchical clustering of significantly

expressed genes is displayed by average linkage and Euclidean dis-

tance as a measurement of similarity.

4

3

2

1

–6 –4 –2 2 4 60Log2 (fold change)

–Log

10 (

corr

ecte

d P

val

ue)

Figure 2 Volcano plot. Volcano plot illustrating the differentially

expressed probes in DT (drought tolerant landrace N22) versus DS

(high-yielding cultivar IR64) under drought stress. The X-axis repre-

sents the fold change in DT compared to DS (on a log2 scale), and

the Y-axis represents the negative log10-transformed P-values

(P < 0.05) of the t-test for finding differences between the samples.

ª 2010 The AuthorsPlant Biotechnology Journal ª 2010 Society for Experimental Biology and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–13

Sangram K. Lenka et al.4

(46.15%) among the up-regulated TFs in DT. The role of

different members of zinc finger protein–encoding genes

in abiotic stress tolerance is well documented (Liu et al.,

2007; Huang et al., 2008; Xu et al., 2008). These TFs have

also been reported to activate cascades of genes that act

together in enhancing tolerance to multiple stresses

(Bhatnagar-Mathur et al., 2008). Investigating the role of

these TF-encoding candidate genes identified in this study

(Table 1, Table S7) by over-expression in transgenic plants

will further enhance our understanding of the mechanisms

that impart abiotic stress tolerance to rice. As N22 is a

well-known DT land race, we expect that N22 alleles of

the identified candidate TFs (Table 1, Table S7) may per-

form better than the DS IR64 alleles. Similarly, functional

characterization of down-regulated master regulatory

genes (TFs) under drought in DT can be tested by RNAi

approach (Table S7).

To validate the microarray results and quantify the

expression of regulatory genes, 38 probe sets representing

TF-encoding genes that showed differential regulation

between DT and DS under drought stress were chosen.

Although the microarray log2-fold change value of a probe

differed somewhat from the corresponding value of qRT-

PCR, the high correlation (R2 = 0.92, P < 0.05) between

microarray and qRT-PCR expression values indicated that

the expression analysis by both the approaches was in

good agreement with each other (Figure 3).

Consensus cis-motif pattern finding

Integration of co-expression profile data with cis-motif

consensus pattern or promoter structure is crucial to

understand the universal and organism-level molecular

networking (Lenka et al., 2009). Promoter sequences of

the co-expressed genes were analysed to find the cis-motif

consensus pattern. Top five significant cis-motif patterns

were sampled from the drought up-regulated genes, and

their features are given in Table 2. The motifs identified in

this study were novel, and the respective TF family mem-

bers binding to them were unknown except CCA1

(TTTTTTTTHYW) to which the members of MYB-related

TFs were reported to interact in Arabidopsis (Wang et al.,

1997). Interestingly, CCA1 and the GC-rich SCGSCGSCG-

SCG motifs were found to be a common feature of rice

genome, as reported in our previous study (Lenka et al.,

2009). It will be useful to further characterize the above

cis-elements by deletion analysis of promoters and linker

scanning approach. More TFs could also be identified

using these cis-elements as bait by yeast one-hybrid

screening approach. Detailed characterization of the over-

represented drought-responsive cis-motif identified in this

study (Figure S4a–e) might lead to the identification of

sub-regulons that play a key role in drought tolerance in

rice. There was no significant enrichment of a particular

cis-motif obtained from the down-regulated genes.

Gene ontology enrichment for molecular function

Broad molecular functions of the differentially expressed

genes were analysed in terms of GO enrichments

(P £ 0.05) for molecular function. In DS control versus DS

drought down-regulated probes, the significantly down-

regulated genes belonged to three important regulatory

functional classes, namely nucleic acid binding

(GO:0003676) (P = 0.04) representing 261 probes, DNA

binding (GO:0003677) (P = 3.6e)04) with 212 probes and

transcription regulator activity (GO:0030528) (P =

2.9e)05) represented by 187 probes. The above regula-

tory GO classes notably include four probes of DREB, three

probes of bZIP and ten probes of NAC TF family members

among other important regulatory classes of genes that

were earlier reported to confer drought tolerance in trans-

genic rice (Fang et al., 2008; Wang et al., 2008; Xiang

et al., 2008). This analysis suggests that the susceptibility

of IR64 is probably attributable to significant down-

regulation of regulatory components that confer drought

tolerance. Comparison of the transcriptome expression

pattern under control condition between DT and DS led to

the identification of 264 up-regulated probes that

possessed catalytic activity (GO:0003824) and were signifi-

cantly enriched (P = 8.9e)03) in DT. Comparative expres-

sion analysis between DT and DS under drought stress

unravelled that 263 probes having catalytic activity

(GO:0003824) were up-regulated significantly (P = 0.01)

in DT. Similarly, hydrolase activity (GO:0016787) was also

enriched (124 representative probes) considerably

(P = 3.7e)04) in DT. The analyses suggest that drought

tolerance in DT was attributable to enhanced enzymatic

activity in DT compared to DS. Hydrolases are known to

impart stress tolerance to plants by participating in diverse

physiological activities (Cho et al., 2006). Drought-induced

catalytic components participate in the synthesis or catab-

olism of drought stress–associated metabolites, and some

of these have been used to enhance stress tolerance in

transgenic plants (Umezawa et al., 2006). Our results thus

suggest that the preparedness of DT with higher enzy-

matic activity even under control conditions might be an

evolutionarily acquired adaptive trait.

ª 2010 The AuthorsPlant Biotechnology Journal ª 2010 Society for Experimental Biology and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–13

Drought-responsive transcriptome analysis in rice 5

Table 1 Up-regulated genes in drought-tolerant rice landrace Nagina 22 (DT) identified by microarray analysis and validated by qRT-PCR

Probe set ID Protein function

Abiotic stress-responsive expression ⁄ phenotype of

transgenic plants References

Os.4174.1.S1_at AGL12 (AGAMOUS-LIKE 12);

transcription factor (TF)

AGL12 induces multiple responses that are related to various

stresses in transgenic rice and Arabidopsis

Lee et al. (2008)

Os.4175.1.S1_at MADS-box protein Abiotic stress-induced response reported in MADS-box

gene family

Arora et al. (2007)

Os.4706.1.S1_a_at MADS box-like protein Abiotic stress-induced response reported in MADS-box

gene family

Arora et al. (2007)

Os.40018.1.S1_at Heat stress TF Imparts enhanced tolerance to heat and high salinity in

transgenic Arabidopsis

Yokotani et al. (2008)

Os.7928.1.S1_at Putative CCAAT-binding TF Induction in transcription observed owing to drought stress This study

Os.37813.1.S1_at MADS-box TF 56 Abiotic stress-induced response reported in MADS-box

gene family

Arora et al. (2007)

Os.15711.1.S1_at Putative bZIP TF Transgenic rice over-expressing OsbZIP23 showed

significantly improved tolerance to drought and

high-salinity stresses and sensitivity to abscisic acid

Xiang et al. (2008)

OsbZIP72 enhanced ability to tolerate drought stress Lu et al. (2009)

NAC domain TF ONAC063 imparts high-salinity and osmotic stress

tolerance

Yokotani et al. (2009)

Transgenic rice plants over-expressing ONAC045 showed

enhanced tolerance to drought and salt treatments

Zheng et al. (2009)

Os.23103.1.S1_at Ethylene-responsive element

binding factor

Enhances osmotic and drought tolerance in rice by

modulating the increase in stress-responsive gene

expression

Quan et al. (2010)

OsAffx.12789.1.S1_s_at TF MADS47 Abiotic stress-induced response reported in MADS-box

gene family

Arora et al. (2007)

OsAffx.24128.1.S1_s_at Putative ethylene-responsive

element binding factor

Enhanced osmotic and drought tolerance in rice by

modulating the increase in stress-responsive gene

expression

Zheng et al. (2009)

Os.56298.1.S1_at WRKY TF 35 Enhanced heat and drought tolerance in transgenic

rice seedlings

Wu et al. (2009)

OsAffx.14373.1.S1_s_at Ethylene-responsive element

binding protein homolog

Enhanced osmotic and drought tolerance in rice by

modulating the increase in stress-responsive gene

expression

Quan et al. (2010)

Os.3710.1.S1_at Predicted RING-H2 finger protein

ATL3F

Confers drought tolerance upon induction Ko et al. (2006)

Os.11773.1.S1_at WRKY33; TF Responsive to abiotic stresses Ramamoorthy et al. (2008)

Os.12032.1.S1_at TF OsWRKY71 Responsive to abiotic stresses Ramamoorthy et al. (2008)

Os.2364.1.S1_at Homeodomain-leucine zipper TF Implicated in stress adaptation Agalou et al. (2008)

Os.2363.1.S1_a_at Homeodomain TF HOX6 Implicated in stress adaptation Agalou et al. (2008)

Os.7912.1.S1_at Zinc finger TF ZF1 Members of zinc finger TF family shown to confer

drought tolerance in rice

Huang et al. (2009)

Os.27227.1.S1_at WRKY family TF-like Responsive to abiotic stresses Ramamoorthy et al. (2008)

Os.11450.1.S1_at RING finger and CHY zinc finger

domain-containing protein

Drought-induced transcription observed This study

Os.11774.1.S1_s_at Zinc finger TF-like protein Members of zinc finger TF family shown to confer

drought tolerance in rice

Huang et al. (2009)

OsAffx.11050.2.S1_x_at OsWRKY1v2—Superfamily of TFs Responsive to abiotic stresses Ramamoorthy et al. (2008)

Os.54569.1.S1_x_at Putative tuber-specific and

sucrose-responsive element

binding factor

Induction in transcription observed owing to drought

stress

This study

Os.19172.1.S1_at Putative zinc finger protein Members of zinc finger TF family shown to confer

drought tolerance in rice

Huang et al. (2009)

Os.14823.1.S1_s_at P-type R2R3 Myb protein Over-expression of R2R3 Myb improves drought and

salt tolerance

Ding et al. (2009)

Os.30568.1.S1_at WRKY TF 48-like protein Responsive to abiotic stresses Ramamoorthy et al. (2008)

ª 2010 The AuthorsPlant Biotechnology Journal ª 2010 Society for Experimental Biology and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–13

Sangram K. Lenka et al.6

Drought-responsive pathway analysis

The common drought-responsive probes that were up-regu-

lated in both DT and DS under drought stress were analy-

sed. Three important pathways were found to be

significantly up-regulated (FDR = 0.05) in both the cultivars.

These pathways included carbon fixation (17 enzymes,

P = 9.52e)08) (Figure S5a), glycolysis ⁄ gluconeogenesis (13

enzymes, P = 3.94e)03) (Figure S5b) and flavonoid biosyn-

thesis pathway (seven key enzymes, P = 2.63e)02) (Fig-

ure S5c). On the contrary, 11 vital enzymes of starch and

sucrose metabolism (P = 2.28e)02) were considerably

down-regulated under drought stress in both the rice culti-

vars (Figure S5d). Induction of energy-related functions (gly-

colysis and gluconeogenesis) indicates the evolutionarily

conserved process in both the genotypes to sustain the

housekeeping function even under dehydration stress. Simi-

lar categories of ESTs were also abundantly induced in

adaptive response to dehydration stress in Selaginella lepid-

ophylla, an ancient lineage of vascular plants that can with-

stand complete desiccation for years and can be revived

after only a few hours of rehydration (Iturriaga et al., 2006).

Flavonoids are an important group of secondary metabolites

with diverse molecular functions, including stress protection

in plants (Winkel-Shirley, 2002). Previous reports have

shown that flavonoid biosynthetic pathway is induced in

salt-sensitive rice genotype IR29 during salt stress at the

vegetative growth stage (Walia et al., 2005). Similarly, in

cultivar N22 (DT), transcript level and ⁄ or transcript stability

of components of the flavonoid pathway were reported to

be significantly enhanced in seedlings treated with abscisic

acid (ABA), dehydration or high salt stress (Ithal and Reddy,

2004). Induction of genes encoding important enzymes

involved in the flavonoid biosynthetic pathway, as also

observed in this study, appears to be a characteristic

response of rice for protection against various stress-

induced injuries.

Reduction in starch and sucrose metabolism in both DT

and DS (Figure S5d) suggests that drought stress negatively

affects the export or the utilization of assimilates in the sink

organs. Reduction in starch and sucrose metabolism also

leads to reduction in starch and sucrose content—a well-

documented phenomenon in several higher plant species

under drought conditions (Lawlor and Cornic, 2002). Inter-

estingly, ten enzymes of the starch and sucrose metabolism

pathway (P = 3.56e)02) were down-regulated in DT when

compared to DS under drought (Figure S5e). This suggests

that the level of starch and sucrose metabolism might be

20

R 2 = 0.924615

10

5

5 10 15–15

–15Log ratio (microarray)

Log

rat

io (

real

-tim

e R

T P

CR

)

–10

–10

–5

–5

00

Figure 3 Validation of the expression of selected probes representing

transcription factor (TF) from microarray by qRT-PCR. Correlation anal-

ysis shows selected probes representing TF between microarray, and

qRT-PCR experiments are in good agreement with each other. The

fold changes in gene expression were transformed to log2 scale. The

microarray data log2-values (X-axis) were plotted against the real-time

RT-PCR log2 values (Y-axis).

Table 1 (Continued)

Probe set ID Protein function

Abiotic stress-responsive expression ⁄ phenotype of

transgenic plants References

Os.10172.1.S1_at Putative myb protein Members of MYB family well implicated in drought

and other abiotic stress tolerance

Dai et al. (2007)

OsAffx.3135.1.S1_at Putative MYB TF Members of MYB family well implicated in drought

and other abiotic stress tolerance

Dai et al. (2007)

Os.49023.1.S1_x_at Zinc finger (C3HC4-type RING finger)

family protein

Abiotic stress-responsive regulation of C3HC4-type

RING finger gene family observed in rice

Ma et al. (2009)

Os.6261.1.S1_at Zinc finger (C3HC4-type RING finger)

family protein

Abiotic stress-responsive regulation of C3HC4-type

RING finger gene family observed in rice

Ma et al. (2009)

Os.11945.1.S1_at Putative TF WRKY5 Responsive to abiotic stresses Ramamoorthy et al. (2008)

ª 2010 The AuthorsPlant Biotechnology Journal ª 2010 Society for Experimental Biology and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–13

Drought-responsive transcriptome analysis in rice 7

reduced in DT (low yielding land race) under drought when

compared to DS (high-yielding cultivar).

Comparing the pathways between DS control and

drought, it was evident that 19 enzymes of carbon fixation

(P = 4.96e)06) and 19 enzymes of glycolysis ⁄ gluconeogen-

esis (P = 1.11e)04) were up-regulated, whereas 14 vital

enzymes of starch and sucrose metabolism (P = 1.54e)02)

were significantly down-regulated in response to drought

stress (pathway diagram not shown). Similar comparison in

DT also revealed significant up-regulation of 20 enzymes of

carbon fixation (P = 2.75e)06) and 17 enzymes of glycoly-

sis ⁄ gluconeogenesis (P = 2.21e)02) (pathway diagram not

shown). In addition to these two pathways, eight enzymes

of a-linolenic acid metabolism (P = 3.67e)02) were signifi-

cantly induced under drought in DT (Figure 4). a-linolenic

acid (18:3) is the major class of fatty acids in membrane lip-

ids of rice leaves. Under well-watered conditions, the con-

tent of 18:3 lipids was reported to be the highest among

the other membrane lipids. However, decreasing trend has

been observed in the content of unsaturated fatty acids

with increasing water stress in plants (Chen et al., 2004).

Constitutive antisense expression of an Arabidopsis omega-

3 fatty acid desaturase gene led to reduced salt ⁄ drought

tolerance in transgenic tobacco plants (Im et al., 2002). On

the other hand, over-expression of two fatty acid desaturase

genes FAD3 or FAD8 resulted in increased tolerance to

drought in tobacco plants and to osmotic stress in cultured

cells suggesting thereby an inverse correlation between

drought-responsive decreased levels of linolenic acid and

enhanced drought-induced damage to plant systems under

stress (Zhang et al., 2005). Thus, an elevated a-linolenic acid

metabolism in DT under drought appears to be in good

Table 2 MEME-generated motifs sampled from the 1-kb upstream region (from ATG) within the common drought up-regulated regulons

MEME-generated motifs

Log likelihood

ratio (llr) E-value

Occurrence of the

motif in drought

up-regulated

promoters (%)

Illustration as

WebLogo

(Suppl. Fig. No.) Description

CBCCKCCKCCKC 5414 2.0e)453 48.69 S4a Novel motif

RRRRRRGARRRR 5757 1.7e)341 57.1 S4b Novel motif

TTTTTTTTHYW 5722 2.0e)238 60.37 S4c CCA1 binding site (common feature of rice genome)

SCGSCGSCGSCG 3466 1.4e)237 30 S4d Common feature of rice genome

YYYCTCYYYYYC 5052 4.1e)264 49.62 S4e Novel motif

C01226

C16324 C04672

C16321

C06427

C00157

C04785

C04780 C16329 C16333 C16337 C16327 C16335 C16331 C16339 C08491

2.1.1.1412.3.1.161.3.3.61.1.1.354.2.1.745.3.99.6

4.2.1.92

1.13.11.12

3.1.1.32 3.1.1.4

1.3.1.424.2.1.17 1.1.1.211

C16328 C16332 C16336 C16330 C16334 C16338 C11512

Figure 4 Up-regulation of a-linolenic acid

metabolism in drought-tolerant (DT) land

race of rice (Nagina 22) under drought.

Eight enzymes of a-linolenic acid metabolism

(P = 3.67e)02) significantly induced under

drought in DT are highlighted in yellow, and

probes present in Affymetrix array and not

induced significantly are highlighted in grey.

Enzymes are given here as EC number, and

carbon compounds are given as KEGG

compound ID.

ª 2010 The AuthorsPlant Biotechnology Journal ª 2010 Society for Experimental Biology and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–13

Sangram K. Lenka et al.8

agreement with the inherent drought tolerance capacity of

the rice landrace N22; it might also explain better recovery

capacity from drought upon rehydration presumably

because of efficient membrane reconstitution ability. Our

results clearly demonstrate that N22 can serve as an excel-

lent genetic resource for the identification of novel alleles

encoding enzymes (Table S8) associated with drought toler-

ance for genetic improvement of rice and other cereals.

Conclusions

Using expression profiling of Affymetrix Rice Genome

Array, it was found that the drought-tolerant rice N22

(DT) and drought-susceptible rice IR64 (DS) exhibited a

diverse global transcriptional response under both control

and drought stress conditions. GO analysis suggested that

drought tolerance of DT was found to be linked to

enhanced enzymatic activity, whereas drought susceptibil-

ity of DS was governed by significant down-regulation of

transcriptional regulatory protein–encoding genes, some

of which were previously shown to confer drought toler-

ance in transgenic plants. Pathway analysis unravelled

up-regulation of several components of carbon fixation,

glycolysis ⁄ gluconeogenesis and flavonoid biosynthesis and

down-regulation of starch and sucrose metabolism in both

the cultivars under drought. Enhanced a-linolenic acid

metabolism in DT under drought appears to be in accor-

dance with its well-reported drought tolerance. The study

revealed genotype-dependent drought tolerance mecha-

nisms in DT versus DS. Functional validation of drought-

responsive genes identified in this study will help to dissect

the complexity of drought tolerance at molecular level and

subsequently enhance the pace of genetic improvement in

drought tolerance in rice and other crop plants.

Experimental procedures

Plant materials, growth conditions and stress

treatments

Rice cultivar Nagina-22 (N-22) and IR-64 were grown side by side

in plastic pots for 14 days at 28 ± 1 �C with a daily photoperiodic

cycle of 14 ⁄ 10 h light ⁄ dark provided by fluorescent tubes (Philips

TL 40 W ⁄ 54, @ 100–125 lmol ⁄ m2 ⁄ s). Sterile absorbent cotton

soaked with Hoagland’s solution was used as seed bed for

growing rice seedlings. Water stress was given to the plants by

withholding water supply till visible leaf rolling appeared in the

plants. To impose equal level of stress, the amount of cotton used

for seed bed, the pot size and the quantity of Hoagland’s solution

were kept constant for all the pots, and same number of seed-

lings per pot was raised. The level of stress was quantified by

measuring RWC of leaves of rice seedling.

Physiological analysis

To measure RWC, leaf samples were weighed immediately as

fresh weight (FW), then chopped into 2- cm pieces and floated on

distilled water for 4 h at 4 �C. The turgid leaf pieces were then

quickly blotted to remove extra surface water and weighed to

record turgid weight (TW). The leaf samples were then packed in

paper bags and oven-dried at 80 �C for 24 h, and the dry weight

(DW) of the samples were weighed. RWC was calculated as

RWC ð%Þ ¼ ½FW� DWÞ=ðTW� DW� � 100:

The chlorophyll content was measured using the method sug-

gested by Arnon (1949) (Arnon, 1949). Initially, 0.05 g of leaf tis-

sue was placed in 10 mL of DMSO in test tubes and incubated at

65 �C for 4 h. Then, the test tubes were cooled to room tempera-

ture, and the absorbance was recorded at 645 and 663 nm,

respectively. The excised-leaf water loss (ELWL) or retention of

water in excised leaves was measured by recording the rate of

water loss in leaf segments of 3 cm in length and by determining

weight loss in samples periodically; i.e. the leaves were weighed

immediately after sampling (FW), drying in an incubator at 28 �Cat 50% R.H. and weighing at each 1- h intervals up to 5 h and

then oven-drying for 24 h at 70 �C.

ELWL was then calculated as

ELWL ¼ Fresh weight

�Weight after desire time interval=Fresh weight

� Dry weight� 100

Whole-genome expression analysis

High-quality RNA was extracted from the whole seedlings (com-

bined root and leaf samples) using TRI Reagent (Ambion, Inc.,

Austin, TX) and pooled from 12 independent stressed and

non-stressed plant samples separately and treated with DNase-I

(QIAGEN GmbH, Hilden, Germany). Subsequently, RNA clean-up

was carried out using RNeasy Plant Mini Kit (QIAGEN GmbH), and

5 lg of total RNA from each sample with three biological replica-

tions was reverse-transcribed to double-stranded cDNA using the

GeneChip� One-Cycle cDNA Synthesis Kit. The biotin-labelled

cRNA was made using the GeneChip� IVT Labelling Kit (Affyme-

trix, Santa Clara, CA, USA). Twenty microgram of cRNA samples

was fragmented, out of which 7.5 lg cRNA were hybridized for

16 h at 45 �C to the Affymetrix GeneChip� Rice Genome Array.

After washing and staining with R-phycoerythrin streptavidin in a

Fluidics Station, using the Genechip� Fluidics Station 450, the

arrays were scanned by the Genechip� 3000 Scanner. The chip

images were scanned and extracted using default settings, and

the. CEL files were produced with the Affymetrix GeneChip Oper-

ating Software (GCOS 1.2). The resulting. CEL files were imported

ª 2010 The AuthorsPlant Biotechnology Journal ª 2010 Society for Experimental Biology and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–13

Drought-responsive transcriptome analysis in rice 9

into the GeneSpring GX 10 (Agilent Technologies Inc., Santa

Clara, CA, USA) and normalized with the PLIER16 algorithm (Hub-

bell et al., 2002). The resulting expression values were log2-trans-

formed. Average log signal intensity values of three biological

replicates for each sample were used for advance analysis. Probes

showing twofold up- or down-regulation compared to

corresponding control condition were taken only into consider-

ation for further analysis. All details of data are available for pub-

lic access online at http://www.ebi.ac.uk/microarray/submissions.

html (ArrayExpress accession: E-MEXP-2401).

Differential gene expression was identified using significance

analysis by unpaired Student’s t-test between appropriate pair

wise comparison of different samples under consideration. Benja-

mini and Hochberg false discovery rate (FDR) multiple testing cor-

rections was applied to the differentially expressed genes

(P < 0.05). Hierarchical clustering of significantly expressed genes

was carried out by average linkage and Euclidean distance as a

measurement of similarity using GeneSpring GX 10.

qRT-PCR analysis

Similar growth conditions and level of stress were used for qPCR

analysis as in the case of microarray experiments to validate the

expression patterns of TF genes. First strand cDNA was synthesized

using 2 lg of total RNA using Superscript-III RNase H– Reverse

Transcriptase (Invitrogen, Carlsbad, CA) with oligo (dT) 20 primer

following manufacturer’s instructions. qPCR of 20 lL each con-

taining 50 ng of cDNA was conducted in an Eppendorf realplex-4

Mastercycler ep gradients machine. Rice actin gene was used as

the endogenous control after testing positive for almost unaltered

expression across different conditions tested here. For microarray

data validation, QuantiFast SYBR Green PCR master mix (QIAGEN

GmbH) was used according to manufactures instruction. The pri-

mer combinations used here for real-time RT-PCR analysis were

specifically amplified only one desired band. The dissociation curve

testing was carried out for each primer pair showing only one

melting temperature. The efficiency test of three randomly

selected primer pairs showed approximately the same efficiency as

that of the normalizer actin at a series dilution of each group

cDNA at rates of 1 ⁄ 10, 1 ⁄ 100 and 1 ⁄ 1000. The threshold cycles

(CT) of each test target were averaged for triplicate reactions, and

the values were normalized according to the CT of the control

products (Os-actin). TFs expression data were normalized by sub-

tracting the mean reference gene CT value from individual CT val-

ues of corresponding target genes (DCT). The fold change value

was calculated using the expression, where DDCT represents DCT

condition of interest -DCT control. The results obtained were

transformed to log2 scale. The primer sets used in this study to val-

idate microarray results are given in the Table S9.

Over-represented cis-element finding

Drought stress co-regulated probes were mapped onto the TIGR

rice pseudomolecules, release 5.0 (http://www.tigr.org), and cor-

responding loci were listed after removing the duplicates. The file

containing all the 1- kb upstream sequences (promoters) from

translational start site ATG of rice genome was downloaded from

TIGR and filtered to obtain sub-files with desired set of

co-regulated promoters using Perl script. The widely used expecta-

tion maximization algorithm of MEME (Multiple EM for motif elici-

tation) (Version 4.0.0) was used to find over-represented cis-

motifs of a width of 8–12 nucleotides, on Linux x86_64 machine

(Bailey et al., 2006). The relevance of discovered motifs was

analysed using PLACE (http://www.dna.affrc.go.jp/PLACE/) (Higo

et al., 1999).

Molecular function enrichment and pathway analysis

The broad molecular function of the differentially expressed

probes was analysed in terms of significantly enriched GO catego-

ries for molecular function using EasyGO, GO enrichment analysis

tool (http://bioinformatics.cau.edu.cn/easygo/) (Zhou and Su,

2007). Binomial statistical test and cut-off for FDR-adjusted

P-value £0.05 was used to screen the GO term enrichment. Sig-

nificantly influenced entire metabolic pathway analysis was carried

out using PathExpress analysis tool (http://bioinfoserver.rsbs.

anu.edu.au/utils/PathExpress/) with P-value threshold £0.05 where

FDR was used for P-value adjustment (Goffard and Weiller, 2007).

The list of unique enzymes (EC number) up- or down-regulated

by different conditions within a corresponding pathway is given in

Table S8.

Authors’ contributions

SKL performed all the wet-lab experiments, designed the

study, analysed the data and drafted the manuscript; AK

made Perl script and helped in data mining and manage-

ment; VC and KCB facilitated in designing of the study

and drafting the manuscript. All authors read and

approved the final manuscript.

Acknowledgements

SKL gratefully acknowledges University Grants Commission

(UGC) and Council of Scientific and Industrial Research

(CSIR) for CSIR-UGC Junior Research Fellowship grant. This

work was supported by the Indian Council of Agricultural

Research (ICAR)-sponsored Network Project on Transgenics

in Crops (NPTC).

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Supporting information

Additional Supporting Information may be found in the

online version of this article:

Figure S1 Retention of water (%) in excised leaf in DT

and DS under control and drought conditions up to 5 h

duration at 28 �C and 50% R.H.

ª 2010 The AuthorsPlant Biotechnology Journal ª 2010 Society for Experimental Biology and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–13

Sangram K. Lenka et al.12

Figure S2 Flow-chart showing the strategic outline of

microarray experiment.

Figure S3 Venn diagram showing differential expression

pattern of number of probes under different conditions in

both DT and DS cultivars (a–d). (e) Profile plot showing

the differential expression pattern of all the probes under

control and drought stress conditions in DS and DT. The

gene expression under different conditions (X-axis) is

plotted against normalized intensities in log2-values

(Y-axis).

Figure S4 MEME sampled cis-elements discovered within

the 1 kb up-stream sequence (from ATG) of drought up-

regulated regulons.

Figure S5 Up-regulation of different enzymes of carbon

fixation pathway (a), glycolysis/gluconeogenesis (b), and

flavonoid biosynthesis pathway (c). The enzymes that were

significantly induced in both DT and DS under drought

stress are highlighted in yellow, and probes present in

Affymetrix array and not induced significantly are high-

lighted in gray. Enzymes are given here as EC number and

carbon compounds are given as KEGG compound ID. (d)

Down regulation of different enzymes of starch and

sucrose metabolism under drought stress in both DS and

DT are highlighted in yellow, and probes present in Affy-

metrix array and not down regulated significantly are

highlighted in gray. Enzymes are given here as EC number

and carbon compounds are given as KEGG compound ID.

(e) Down regulation of different enzymes of starch and

sucrose metabolism pathway in DT as compared to DS

under drought stress are highlighted in yellow, and probes

present in Affymetrix array and not down regulated signif-

icantly are highlighted in gray. Enzymes are given here as

EC number and carbon compounds are given as KEGG

compound ID.

Table S1 The correlation coefficients among the normal-

ised samples of microarray experiment.

Table S2 List of probe sets (both up and down regulated),

fold change in expression and annotation of DS control vs.

DS drought.

Table S3 List of probe sets (both up and down regulated),

fold change in expression and annotation of DT control vs.

DT drought.

Table S4 List of probe sets (both up and down regulated),

fold change in expression and annotation of DS control vs.

DT control.

Table S5 List of probe sets (both up and down regulated),

fold change in expression and annotation of DS drought

vs. DT drought.

Table S6 List of common drought responsive probe sets

(both up and down regulated), fold change in expression

and annotation of DS control vs. DS drought and DT con-

trol vs. DT drought.

Table S7 List of all the common drought responsive probe

sets representing transcription factors encoding genes and

their regulation status across all the conditions.

Table S8 The list of unique enzymes (EC number) asso-

ciated with different pathways up or down regulated by

different conditions.

Table S9 List of probes sets, their level of expression in

microarray, qRT-PCR and corresponding primers used for

expression analysis by qRT-PCR.

Please note: Wiley-Blackwell are not responsible for the

content or functionality of any supporting materials sup-

plied by the authors. Any queries (other than missing

material) should be directed to the corresponding author

for the article.

ª 2010 The AuthorsPlant Biotechnology Journal ª 2010 Society for Experimental Biology and Blackwell Publishing Ltd, Plant Biotechnology Journal, 1–13

Drought-responsive transcriptome analysis in rice 13