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JXB Advance Access originally published online on November 28, 2006
Journal of Experimental Botany 2007 58(2):253-265; doi:10.1093/jxb/erl213
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© The Author [2006]. Published by Oxford University Press [on behalf of the Society for Experimental Biology]. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org

RESEARCH PAPER

Identification of stress-responsive genes in an indica rice (Oryza sativa L.) using ESTs generated from drought-stressed seedlings

Markandeya Gorantla1, PR Babu1, VB Reddy Lachagari1, AMM Reddy1, Ramakrishna Wusirika2 *, Jeffrey L. Bennetzen3 and Arjula R. Reddy1,{dagger}

1Department of Plant Sciences, School of Life Sciences, University of Hyderabad, Hyderabad-500046, AP, India
2Department of Biological Sciences, Purdue University, West Lafayette, IN 47906, USA
3Department of Genetics, University of Georgia, Athens, GA 30602, USA

{dagger} To whom correspondence should be addressed. E-mail: arjulsl{at}uohyd.ernet.in

Received 25 November 2005; Accepted 22 September 2006


   Abstract
Top
 Abstract
Introduction
 Materials and methods
 Results
 Discussion
 Supplementary data
 References
 
The impacts of drought on plant growth and development limit cereal crop production worldwide. Rice (Oryza sativa) productivity and production is severely affected due to recurrent droughts in almost all agroecological zones. With the advent of molecular and genomic technologies, emphasis is now placed on understanding the mechanisms of genetic control of the drought-stress response. In order to identify genes associated with water-stress response in rice, ESTs generated from a normalized cDNA library, constructed from drought-stressed leaf tissue of an indica cultivar, Nagina 22 were used. Analysis of 7794 cDNA sequences led to the identification of 5815 rice ESTs. Of these, 334 exhibited no significant sequence homology with any rice ESTs or full-length cDNAs in public databases, indicating that these transcripts are enriched during drought stress. Analysis of these 5815 ESTs led to the identification of 1677 unique sequences. To characterize this drought transcriptome further and to identify candidate genes associated with the drought-stress response, the rice data were compared with those for abiotic stress-induced sequences obtained from expression profiling studies in Arabidopsis, barley, maize, and rice. This comparative analysis identified 589 putative stress-responsive genes (SRGs) that are shared by these diverse plant species. Further, the identified leaf SRGs were compared to expression profiles for a drought-stressed rice panicle library to identify common sequences. Significantly, 125 genes were found to be expressed under drought stress in both tissues. The functional classification of these 125 genes showed that a majority of them are associated with cellular metabolism, signal transduction, and transcriptional regulation.

Key words: Abiotic stress, candidate genes, drought, stress-responsive genes, transcriptome


   Introduction
Top
 Abstract
 Introduction
Materials and methods
 Results
 Discussion
 Supplementary data
 References
 
Rice, the world's most important cereal crop, is the primary source of food and calories for about half of mankind (Khush, 2005). In Asia, rice provides as much as 80% of the dietary calories in countries such as Bangladesh and Indonesia. Rice-growing areas span the tropics, subtropics, semi-arid tropics, and temperate regions of the world. The predominantly rice-growing areas in Asia (~130 million hectares) are often threatened by severe abiotic stresses, the most common being drought. These areas include irrigated and rainfed lowlands, which together account for more than 85% of total world rice production. Drought has become the most significant constraint to realizing the yield potential of rice across all agro-climatic zones. In some years, abiotic stresses cause crop losses by as much as 50% (Boyer, 1982; Bray et al., 2000) and drought alone may cause yield losses of as much as 15% (Dey et al., 1996). Drought spells across Asia have become more frequent and severe, leading to irregular and insufficient irrigation of the crop and depletion of groundwater resources leading to 100% yield losses in certain areas. Some genetic improvement of rice for water-limited environments has been achieved by crop breeding and improved crop husbandry. At least part of the reason for the slow progress in improving the genetic foundation of drought tolerance in rice has been a lack of sufficient genetic information about genes that govern this complex trait and its component secondary traits.

Insufficient water availability leads to a host of biochemical, physiological, and metabolic changes in rice. These changes, many apparently adaptive, include a host of biochemical pathways associated with signal perception, transduction, and regulation of gene expression in a temporal and spatial pattern. A significant number of genes, gene products, and pathways associated with drought response have been identified in rice using a variety of experimental approaches (Rabbani et al., 2003; Kawasaki et al., 2001; Matsumura et al., 1999; Gibbings et al., 2003; Gowda et al., 2004). Numerous laboratory water-stress experiments investigating dehydration-induced changes in rice gene expression have revealed several candidate genes that may be associated with drought tolerance. Molecular genetic analysis of drought tolerance through phenotyping and marker assisted selection (MAS) has identified several genomic regions, quantitative trait loci (QTLs), associated with drought tolerance.

With the near-completion of the rice genome sequence (Goff et al., 2002; Yu et al., 2002; IRGSP, 2005) and rapidly growing databases, complex traits like drought tolerance are now amenable to a detailed molecular analysis using genomic tools. The rice genome was variously estimated to have 37 000–60 000 genes (Goff et al., 2002; Yu et al., 2002, 2005; IRGSP, 2005). One reason for this variation in gene number estimation is a lack of supporting evidence from deep Expressed Sequence Tag (EST) coverage. Many ESTs have been generated for rice, and these have been valuable in confirming and cataloguing genes (Sasaki et al., 1994; Uchimiya et al., 1992; Umeda et al., 1994; Yamamoto and Sasaki, 1997; Reddy et al., 2002a; Markandeya et al., 2003; Zhang et al., 2005) and in deciphering the role of transcriptionally regulated genes in different tissues (Ewing et al., 1999). However, only a few studies have focused on the analysis of transcriptome profiles of rice seedlings subjected to abiotic stress (Umeda et al., 1994; Matsumura et al., 1999; Kawasaki et al., 2001) or drought (Babu et al., 2002; Markandeya et al., 2005). ESTs provide a direct approach for discovering genes associated with a stress response. This has been demonstrated in several plant systems (Michalek et al., 2002; Fernandes et al., 2002; Echenique et al., 2002; Reddy et al., 2002a; Markandeya et al., 2005). Rice has good EST coverage, in general, and a relatively large collection of ESTs generated from drought-stressed plants has been reported (Reddy et al., 2002a, b; Markandeya et al., 2003). These resources can be valuable for further expression studies using microarrays and in single nucleotide polymorphism (SNP) analysis for discovering specific alleles of target genes associated with the drought-stress response. Numerous putative drought-responsive genes have been uncovered by genome wide expression studies in rice (Rabbani et al., 2003; Gibbings et al., 2003; Gowda et al., 2004; Kawasaki et al., 2001; Matsumura et al., 1999, 2003). Most of these are dehydration-associated expression profiles of rice conducted under laboratory conditions, and therefore may not mimic true field drought responses. However, because the experiments were rigorously controlled and environmental variables kept at a minimum, these expression analyses provided uniquely valuable information.

The EST approach has been taken to identify genes associated with drought-stress response and tolerance in rice. A normalized cDNA library has been constructed from drought-stressed seedlings of indica rice cultivar, N22, and large-scale EST data sets have been generated (Reddy et al., 2002a) that have been deposited in GenBank (Reddy et al., 2002b; Markandeya et al., 2003). In the present study, more than 6000 additional ESTs are generated, allowing construction of an N22 unigene set of 1677 sequences. This unigene set was used in a comparative analysis of rice, Arabidopsis, maize, and barley for discovering shared genes for the plant drought response. The identification of 589 candidate shared genes for the plant drought response is reported here. Their predicted molecular functions and potential utility are discussed.


   Materials and methods
Top
 Abstract
 Introduction
 Materials and methods
Results
 Discussion
 Supplementary data
 References
 
Drought-stress treatment and cDNA library construction
A drought tolerant, deep-rooted indica rice genotype Nagina 22 was used for drought-stress induction under defined field capacity. Rice seedlings were grown in pots and maintained in a growth chamber simulating upland growth conditions. The seedlings were maintained at 32±1 ° C during the day and 20±1 ° C during the night in 60% relative humidity. The control plants were grown at 100% FC and 1-month-old plants grown at 70% FC were gradually subjected to drought stress in order to reach 50% FC by regulating the water supply. The physiological condition of plants at 50% FC was monitored by RWC and leaf samples were collected from the plants exhibiting 50–60% RWC. The drought-stress symptoms such as leaf rolling and basal leaf senescence were apparent at this stage in stress-induced plants, while control plants growing at 100% FC were observed to grow well showing 95% RWC. Total RNA isolation, cDNA synthesis, normalization, and the cDNA library construction technique were elaborately discussed in our previous report (Reddy et al., 2002a) wherein 1200 ESTs had been generated and deposited at NCBI (Reddy et al., 2002b).

cDNA cloning and EST sequencing
In the present study, this normalized cDNA library was used for further EST generation. Chemically competent E. coli (DH5Formula) cells were transformed with this library and individual colonies were selected randomly. Cultures from individual colonies were grown overnight and used in plasmid DNA preparation for sequencing, after column purification (Qiagen). The quality and concentration of the template plasmid DNA was checked on 1% agarose (USB Biochemicals) gels. Acceptable quality plasmid DNAs were used directly for sequencing. ESTs were generated from 3' end single-pass sequencing of 6144 cDNA clones using M13 (–40) reverse primer 5'-CGCCGAGGTTTTCCCAGTCACGAC-3' or M13 (–20) reverse primer 5'-GTAAAACGACGGCCAGTG-3', on an automated capillary genetic analysis system (MegaBACE 500). DYEnamic ET terminator chemistry (Amersham Biosciences) was used for sequencing reaction set-up. Post-sequencing reaction clean-up and loading of samples onto the MegaBACE 500 were according to the manufacturer's instructions with adjustments to suit our conditions. The average run time was about 180 min at 6–7 kV.

Sequence repositories and software resources used in EST analysis
The EST sequences generated in the present study, as well as those reported earlier from the same library (Reddy et al., 2002a, b) and IR64 drought-stressed panicle ESTs (Bennett et al., 2002) were the primary data sources for the analyses performed. Standard sequence processing tools, Phred (Ewing and Green, 1998), Phrap and cross_match (Smith and Waterman 1981; Gotoh, 1982) were used with Codoncode InterPhace (http://www.codoncode.com). Homology searches in the NCBI database were carried out using network client software with the DNATools interface (http://www.crc.dk/dnatools).

Genome sequence data for O. sativa subsp. japonica cv. Nipponbare collected from TIGR (http://www.tigr.org/tdb/e2k1/osa1/) and draft sequence for O. sativa subsp. indica cv. 93–11 of the Beijing Genomics Institute (http://btn.genomics.org.cn/rice/) were used in the analysis. In addition, full-length cDNA sequences from Nipponbare (The Rice Full-Length cDNA Consortium, 2003) and full-length cDNA sequences of putative candidate genes derived from Arabidopsis expression profiling studies from The Arabidopsis Information Resource (www.arabidopsis.org) database were also employed. The nucleotide, protein, and EST databases at NCBI (http://www.ncbi.nlm.nih.gov) were utilized for homology searches using the BLAST program (Altschul et al., 1997).

Sequence processing and analysis
The low quality regions present at the beginning and end of each sequence were trimmed using a Phred 20 cutoff value. Vector screening was performed using the cross_match program with Codoncode InterPhace software. Sequences were edited for the removal of oligodT tracks and other contaminants. A batch file of ESTs having greater than 100 bp length of sequence reads were submitted to the NCBI dbEST division of GenBank. After the rice genome sequence was largely completed (IRGSP, 2005), all ESTs from this project were compared to the genomic sequence. All sequences that did not exhibit excellent nucleotide homology with the Nipponbare genomic sequence were removed from GenBank, with the assumption that they were most likely to be derived from microbial contaminants. Phrap and CAP3 (Huang and Madan, 1999) assembly algorithms were used to assemble the individual ESTs into clusters of sequences derived from the same transcript as tentative consensus sequences (TCs) and singletons representing unique transcripts.

Annotation
Homology searches were performed against non-redundant (nr) nucleotide and protein sequence databases using BLASTN 2.2.2 and BLASTX 2.2.2 versions of the BLAST programs (Altschul et al., 1997) through BLAST 2.0 network client software with the DNAtools interface (http://www.crc.dk/dnatools). The BLASTN program was used to identify rice EST hits and rice BAC/PAC clones in the non-redundant (nr) nucleotide sequence database, High Throughput Genomic Sequences (HTGS) division of GenBank and the Beijing WGS (whole genome shotgun contigs) draft sequence of the indica rice genome (Yu et al., 2002) in the NCBI database.

Identification of ESTs consistently associated with abiotic stress
The ESTs associated with stress responses were identified from multiple sources, based on the compiled list of stress-regulated genes documented in more than one plant species (http://stress-genomics.org/stress.fls/expression/expression.html). In addition, data from microarray expression profiles of possible candidate gene sequences comprising 650 from Arabidopsis (Seki et al., 2001, 2002a, b; Kreps et al., 2002), 150 from barley (Ozturk et al., 2002), and 100 from rice (Matsumura et al., 1999; Kawasaki et al., 2001; Rabbani et al., 2003) have been used. All stress-responsive gene sequences were retrieved from the above studies and a local database was constructed and utilized for BLAST analysis. These were compared to the EST data set using TBLASTX with E-value >1e–20.


   Results
Top
 Abstract
 Introduction
 Materials and methods
 Results
Discussion
 Supplementary data
 References
 
Expressed sequence tag generation and analysis
A total of 7794 cDNA clones were sequenced from the 3' end; of these 6694 readable sequences were obtained with a high quality index (Phred score >20). The sequencing strategy proved to be very efficient, with a success rate of ~85%. Our optimized sequencing efforts, through preparation of high-quality, uniform concentrations of sequencing templates and reduced dye chemistries, drastically reduced the costs of single-pass sequencing. The high-quality readable sequences were screened for vector contamination, highly redundant ribosomal RNA sequences, E. coli DNA contamination, and these clones were eliminated from further analysis. Low-quality sequence regions were trimmed and sequences less than 100 bp in length were excluded. The resulting 5815 sequences were submitted to the dbEST division of NCBI (GenBank accession numbers: BI305180 to BI306756; BU672765 to BU673915; and CB964418 to CB967504). Of these, 390 were found to have no homologues in the nearly-completed Nipponbare rice genome sequence (IRGSP, 2005). Although it is possible that some of these are from the few rice genes that have not yet been sequenced from Nipponbare, or even very rare genes that might be found in indica cultivar Nagina 22 and not in japonica cultivar Nipponbare (Bennetzen et al., 2004; Ma and Bennetzen, 2004), but it is probable that most or all of these are ESTs from microbial contaminants in our field-grown rice seedlings. For instance, 380 ESTs were removed prior to the submission of the 5815 sequences because it was clear they were viral sequences from Adenoviral type 2 encoding minor capsid protein VI (Table 1). Microbial contamination is an unavoidable outcome of EST analysis on field-grown plants, but they can easily be excluded from data analysis, now that a full rice genome sequence is available (IRGSP, 2005). A summary of the EST data is provided in Table 1.


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  		Table 1. Summary of EST generation and analysis

 
Construction and functional classification of a unigene set from EST data
Clustering of the 5815 ESTs allowed construction of a unigene set of 2067 unique gene expression products from our drought-stressed rice library. The assembly of sequences produced 1239 singletons and the remaining 4576 sequences were grouped into 828 contiguous sequences (contigs). Of these 2067, 390 were removed as microbial contaminants, leading to the identification of the 1677 N22 unigene set.

Sequence analysis of the N22 unigene set
The assembled N22 unigene set comprising 1677 unigenes have been annotated and functionally classified based on the GO database (Gene Ontology Consortium, 2001). Annotation of the assembled unigene set, through homology searches in the NCBI nr nucleotide and protein databases, revealed that 57% of the unigene set has hits with known putative functions, the remaining 43% of the unigene set comprised hits with no functional characterization and include expressed proteins, unknown proteins, hypothetical proteins, putative proteins, and predicted proteins. Among the functionally classified unigenes, the transcription factor class constitutes the third highest category of functionally classified unigenes, the first two being that of cellular metabolism and protein synthesis (Table 2). Among the ESTs identified, 334 did not show any homology to rice dbEST or rice cDNAs, but were localized onto the rice genome sequence (IRGSP, 2005). These constitute 19% of the total N22 unigene set. These novel ESTs provide expression evidence for the in silico predicted genes and will assist in their intron and exon annotation. As the ESTs in this study were from a cDNA library constructed from drought stress, these novel ESTs may mainly represent genes involved in the drought-stress response. The N22 unigene set was mapped onto rice genomic sequences, and the number of unigenes mapped onto each chromosome is given in Table 3.


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  		Table 2. Functional classification of N22 unigene sequences

 

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  		Table 3. Distribution of unigene sequences in the rice genome

 
Identification of putative abiotic stress-responsive genes
This additional coverage of the rice transcriptome with the drought-stressed leaf library resulted in the identification of potential stress-related genes. As these are from a normalized library constructed from drought-stressed seedling tissue, the profiles may provide clues in the identification of drought-stress responsive genes. The highly represented transcripts were further verified by annotation and comparison with those described in previous studies on the abiotic stress response in several plant species. Accordingly, the redundancy of the stress-responsive genes were considered for in silico northern analysis and expression profiles of these highly expressed genes are listed in Table 4. Those ESTs that exhibit an abundance of 10 or more are considered here. Comparative in silico analysis of paralogues from multiple sources of rice (Matsumura et al., 1999; Kawasaki et al., 2001; Rabbani et al., 2003) and orthologues from other plants (Seki et al., 2001, 2002a; Kreps et al., 2002; Ozturk et al., 2002) led to the identification of 589 putative stress-responsive genes (SRGs). These are classified into 15 functional groups (Fig. 1). Interestingly, the distribution of the 589 putative stress-responsive ESTs among the functional categories showed that transcription factors were particularly well represented. The list of abiotic stress-responsive genes identified from our ESTs, along with the source for paralogues or orthologues from rice and other plants, respectively, is given in supplementary Table S1 at JXB online. All of the identified SRGs were mapped to the rice genomic sequence (IRGSP, 2005) (Table 5).


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  		Table 4. Abundantly expressed stress-responsive genes in N22 seedlings

 

Figure 1
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  		Fig. 1. Functional classification of 589 putative stress-responsive genes of rice.

 

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  		Table 5. Distribution of putative stress-responsive genes (SRGs) in the rice genome

 
Digital northerns
Apart from providing an efficient method for gene discovery, EST data sets can be used to provide low precision estimates of mRNA levels in a tissue through estimations of EST redundancy (Ohlrogge and Benning, 2000; Audic and Claverie, 1997). The EST library used in this study has relatively low redundancy because it was normalized (Reddy et al., 2002a), but still contains many more copies of some transcripts than others. The levels of redundancy among the contigs derived from the CAP3 assemblies have been studied. Of the 828 assembled sequences with more than one EST representation, the most highly represented transcripts were from metallothioneins, followed by transcripts involved in oxidative stress, novel genes, and expressed proteins with no known function. The in silico expression profiles are represented in Table 4.

Comparative analysis of expression profiles between leaf and panicle ESTs under drought stress
Whether the identified stress-responsive genes also appear in other tissue under drought stress, they were compared with panicle ESTs of an IR64 (indica) library made from drought-stressed plants. Surprisingly, only 280 genes were found in common between the two libraries. Among these, 125 genes were identified as predicted stress-responsive genes (Table 6). Functional classification of common drought-responsive genes showed that a majority of them (65%) are associated with metabolism, cellular communication and signal transduction, transcription factor, cellular defence, and protein destination categories (Fig. 2).


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  		Table 6. Comparison of stress-responsive ESTs from drought-stressed N22 leaf and IR64 panicle libraries

 

Figure 2
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  		Fig. 2. Classification of 125 drought-stress responsive genes shared between leaf (N22) and panicle (IR64) tissues.

 

   Discussion
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
Supplementary data
 References
 
In this study the utility of an EST-based approach for gene discovery in rice has been demonstrated. Nagina 22, an indica rice cultivar, was chosen for EST generation and gene discovery, based on its phenology and the utility of this genotype in developing drought-tolerance lines. Nagina 22 is adapted for upland conditions and possesses a constellation of morphological and physiological characters such as early maturity, heat tolerance, two-point root system, accumulation and mobilization of carbohydrates, high regeneration and recovery processes, all associated with drought tolerance mechanisms in plants. The extensive EST resources from N22 were used in characterizing the N22 drought-stress transcriptome and in identification of drought-stress responsive genes. A classification of the unigene set revealed a significant number of novel genes with unknown functions. Since these are specific to the drought-induced indica library and are not represented in other stress libraries of rice, most of them presumably are stress-responsive genes. Molecular functional classification of 1677 unigenes shows a large number of genes that are predicted to be involved in signal transduction and transcriptional regulation (Table 2). Of the 1677 N22 unigenes, 81% showed homologous sequences to existing rice expressed genes and the remaining 19% have no expressional evidence for rice EST or cDNAs in databases. These 19% constitute novel rice genes which have been uncovered in this study. Analysis of the N22 unigene set revealed that 57% of them have a candidate functional role assigned and the remaining 43% belong to genes which have expressional evidence, but no functional role assigned. This suggests that there are many functionally unclassified genes that need to be characterized to discover new pathways and mechanisms adapted by plants to cope with drought stress.

Analysis of the N22 unigene set revealed many putative candidate genes for stress response that can be major targets for engineered stress tolerance. Among these are the genes encoding proteins that are associated with an osmotic stress response such as osmoprotectant synthesis (BU673697 [GenBank] , BU673025 [GenBank] ), the dehydration stress-induced proteins (BU673123 [GenBank] , BU672787 [GenBank] ), and the dehydration-responsive proteins like RD22 (BU672774 [GenBank] ). Data in Table 4 shows that the EST data revealed a number of genes associated with sugar metabolism and antioxidant pathways, as well as osmolyte synthesis. Of the two isoforms of glutathione-S-transferases (GST) (BU673645 [GenBank] ), one shows sequence similarity with Zea mays GST (AF244678 [GenBank] ) and the other, OsGSTZ1, to that of rice (AF309381 [GenBank] ). Evidence for a protective function of intracellular reactive oxygen species scavenging systems by glutathione S-transferase and glutathione peroxidase has been obtained from transgenic experiments in maize (Roxas et al., 1997). Homologues of these genes were identified through our Nagina 22 EST analysis, and thus provide both orthologues and paralogues that may have evolved during duplications and acquired a new functional role in the due course of evolution. Several Nagina 22 ESTs were identified from genes that encode enzymes which break down H2O2 to water; catalase (BU673091 [GenBank] , BU673392 [GenBank] ), ascorbate peroxidase (APX) (BU673288 [GenBank] ) showing homology to tomato APX (A3251882), and manganese superoxide dismutase (MnSOD) (BU673715 [GenBank] ) which is a homologue of rice MnSOD (L34039 [GenBank] ) seem to provide tolerance to oxidative stress. The over-expression of MnSOD in chloroplasts conferred tobacco paraquat tolerance (Tsang et al., 1991). In a field study McKersie et al. (1996) reported that transgenic alfalfa expressing MnSOD suffered reduced injury from water-deficit stress.

The most abundant class of Nagina 22 drought-stressed transcripts represent a group of genes that encode metallothioneins and metallothionein-like proteins, which help in metal detoxification. These are low molecular weight, cysteine-rich, soluble, and metal-binding proteins found in both plant and animal tissues. These proteins sequester toxic metal ions. Seven groups of metallothioneins were found showing different levels of sequence similarity to rice metallothioneins (BU672908 [GenBank] , BU672800 [GenBank] , BU672917 [GenBank] , BU673120 [GenBank] , BU673768 [GenBank] , BU672968 [GenBank] , and BU672982 [GenBank] ). Rice metallothioneins expression is reported to be markedly increased under H2O2, heat shock, abscisic acid, and salicylic acid in shoots (Zhou et al., 2005), indicating their functional role during oxidative stress. Promoter analysis revealed heat-shock elements motifs, besides many light-responsive elements. Since the genotype under study is a heat-tolerant cultivar, these could be the reasons for high transcript abundance under drought stress. Further characterization of these classes of genes is needed to elucidate their role in the drought-stress response in rice. The other detoxifying proteins include thioredoxin (BU673762 [GenBank] ) showing homology to that of rice (AB053294 [GenBank] ), and the other showing homology to a gene in Arabidopsis (AY085055 [GenBank] ).

The stress-responsive gene sets also include those associated with water channels and transporters such as aquaporin (BU673363 [GenBank] ), an ABC transporter protein (BU673203 [GenBank] ), and an oligopeptide transporter protein (BU673275 [GenBank] ). The recently discovered aquaporins act as water channels and their transcript levels are shown to be influenced significantly by a wide variety of environmental stimuli (Weig et al., 1997). These are reported to be involved in water uptake and may function in metabolite or ion transport. These transport proteins are reported to show a 5-fold up-regulation under stress (Seki et al., 2002a).

Other important genes uncovered in Nagina 22 drought-stress ESTs include the membrane-stabilizing proteins and late embryogenic abundant proteins which enhance water-binding capacity, creating a protective environment for other proteins or structures, referred as dehydrins (BI305248 [GenBank] ). They play a major role in the sequestration of ions that are concentrated during cellular dehydration. Numerous genes involved in membrane stability and thermotolerance have been identified from the present EST collections. These include heat shock proteins (HSPs), which have been widely hypothesized to be a major factor in cell thermotolerance (Howarth and Ougham, 1993) and tolerance to other environmental assaults such as oxidative, chilling, salt, and heavy metal stresses. HSPs were also shown to regulate expression of other stress-inducible genes (Liu and Thiele, 1996).

Another group of genes uncovered include those encoding proteins involved in signal transduction and the regulation of gene expression. It is probable that these play a regulatory role in the plant stress response. These include protein kinases, protein phosphatases, transcription factors, and enzymes in phospholipid metabolism and other signaling molecules such as calmodulin-binding protein. Many kinases were observed in the collection (see supplementary Table S1 at JXB online), including mitogen activated protein kinases (MAPKs) (BU672858 [GenBank] , BI305201 [GenBank] ), calcium-dependent protein kinase (BU673731 [GenBank] ), adenosine kinases, and adenylate kinases (BU673745 [GenBank] , BU672936 [GenBank] ). In addition, the signalling molecule calmodulin (BU673090 [GenBank] , BU672925 [GenBank] , BU673775 [GenBank] ), a common participant in the MAPK signal transduction cascade, was found in the Nagina 22 EST libraries studied. The present EST analysis also revealed many more candidate signalling genes, such as MAP kinases and various transcription factors.

The identified transcription factors include proteins having typical DNA binding motifs such as bZIP, MYB, MYC, EREBP/AP2, and ZINC fingers. The role of various transcription factors in stress-responsive gene regulation has been investigated in plants, and several target genes and pathways have been identified (Thomashow, 1998; Park et al., 2001; Seki et al., 2001; Singh et al., 2002; Shinozaki et al., 2003). Overall, the normalized library proved to be a rich source of stress-responsive rice genes.

The EST data and analysis presented here are a first global overview of the transcripts that are expressed in indica rice under water stress. The annotation and comparative analysis of these ESTs have identified many genes associated with or having a potential role in drought-stress tolerance. These genes provide a starting point for understanding the nature of molecular mechanisms of a plant's response and tolerance to drought. EST analysis has uncovered numerous novel genes and transcriptional activators, the master switches that influence the expression of cascades of genes associated with a stress response.

Comparative analysis of SRGs of N22 (the present study) with IR64 panicle ESTs generated under drought-stress revealed that 125 (40%) of them are in common, demonstrating similar patterns of regulated pattern of gene expression between leaf and panicle tissues (Table 6). This pattern is largely similar to the one reported earlier (Tang et al., 2005). These genes are presumably associated with drought-stress response and tolerance during different growth stages of the rice plant. The remaining 60% of SRGs uncovered in this library could be genotype-specific or tissue-specific. However, whether these genes are actually genes involved in rice drought-tolerance cannot be definitely determined without further expression profiling, allele mining, QTL mapping, and reverse genetic experiments.

Identification of the genes in the rice genome has relied heavily on non-experimental methods such as ab initio gene prediction, sequence homology, and motif analysis. These efforts were limited by the insufficient ability of current gene-finding programs to identify and annotate genes from complex genomes effectively (Guigo et al., 2000; Mathe et al., 2002; Zhang et al., 2002; Bennetzen et al., 2004). So far, the identification of coding regions on a genome scale in rice has focused on EST and full-length cDNA analyses (Kikuchi et al., 2003). However, the available EST and cDNA resources do not comprehensively reveal all the genomic coding information as they are biased mostly toward highly expressed genes. Not surprisingly, exhaustive efforts to uncover the rice transcriptome have represented less than half of the predicted genes (Feng et al., 2002; Reddy et al., 2002; Sasaki et al., 2002; Yu et al., 2002; Markandeya et al., 2003, 2005; Zhao et al., 2004). Our EST data has aided in providing expression evidence for an additional 334 unigenes. Most EST sequencing projects have proven to be expensive due to high clone redundancy (Reddy et al., 2002a). In particular, transcript profiling under drought stress had not been carried out much in rice until our study to identify the drought transcriptome through large-scale EST generation. The EST resources of N22 have been found to be useful in the generation of high-density physical maps of stress-responsive genes in rice (Markandeya et al., 2005), to develop candidate gene molecular markers across selected cereals (Sivarama Prasad, 2005), including EST-PCRs (Chandrasekhar, 2005), and to identify SNPs (Lachagari et al., unpublished data). Further these ESTs are now being used as target probes in the fabrication of cDNA microarrays for expression profiling studies under field drought stress.


   Supplementary data
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Supplementary data
References
 
Supplementary data are available at JXB online.


   Acknowledgements
 
This work was financially supported by the Rockefeller Foundation, USA, through a grant to ARR. We thank the Department of Biotechnology, India and the Council of Scientific and Industrial Research, India for fellowships to MG, VBRL, and AMMR.


   Footnotes
 
* Present address: Department of Biological Sciences, Michigan Technological University, Houghton, MI 49931, USA. Back


   References
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Supplementary data
 References
 
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