- Research article
- Open Access
Analysis of gene expression in response to water deficit of chickpea (Cicer arietinum L.) varieties differing in drought tolerance
© Jain and Chattopadhyay; licensee BioMed Central Ltd. 2010
- Received: 19 March 2009
- Accepted: 9 February 2010
- Published: 9 February 2010
Chickpea (C. arietinum L.) ranks third in food legume crop production in the world. However, drought poses a serious threat to chickpea production, and development of drought-resistant varieties is a necessity. Unfortunately, cultivated chickpea has a high morphological but narrow genetic diversity, and understanding the genetic processes of this plant is hindered by the fact that the chickpea genome has not yet been sequenced and its EST resources are limited. In this study, two chickpea varieties having contrasting levels of drought-tolerance were analyzed for differences in transcript profiling during drought stress treatment by withdrawal of irrigation at different time points. Transcript profiles of ESTs derived from subtractive cDNA libraries constructed with RNA from whole seedlings of both varieties were analyzed at different stages of stress treatment.
A series of comparisons of transcript abundance between two varieties at different time points were made. 319 unique ESTs available from different libraries were categorized into eleven clusters according to their comparative expression profiles. Expression analysis revealed that 70% of the ESTs were more than two fold abundant in the tolerant cultivar at any point of the stress treatment of which expression of 33% ESTs were more than two fold high even under the control condition. 53 ESTs that displayed very high fold relative expression in the tolerant variety were screened for further analysis. These ESTs were clustered in four groups according to their expression patterns.
Annotation of the highly expressed ESTs in the tolerant cultivar predicted that most of them encoded proteins involved in cellular organization, protein metabolism, signal transduction, and transcription. Results from this study may help in targeting useful genes for improving drought tolerance in chickpea.
- Drought Stress
- Drought Tolerance
- Relative Water Content
- Tolerant Cultivar
- Subtract cDNA Library
Drought continues to be one of the most significant environmental stresses as a result of continuous decrease in soil moisture content and increase in global temperature . Rapid expansion of water-stressed areas necessitates improvement of crops with traits such as drought tolerance and adaptation, through conventional breeding and/or genetic manipulation. For cultivated crops like chickpea, where improvement through conventional breeding is difficult because of a narrow genetic base, comparative gene expression profiling is an alternate way to identify pathways and genes regulating the stress response . Plants induce expression of a number of genes in response to water limitation. The early response at the cellular level results partly from cell damage, and corresponds partly to adaptive processes that initiate changes in the metabolism and structure of the cell that allows it to function under low water potential . A wide range of techniques and strategies are being deployed these days to identify genes involved in stress responses . But, while the advent of microarrays and protein profiling has generated a lot of information on gene expression during stress response, conventional gene-by-gene analysis is needed to validate these claims.
Most of the data on gene expression in plants in response to drought and other abiotic stresses has been generated using Arabidopsis [5–7]. However, in view of the wide genetic diversity that exists in the plant kingdom, this data may not hold true for other species. Therefore, individual crop-types should be studied to understand crop-specific responses to a particular stress. Among crop plants, cereals are the most studied with respect to gene expression because of their economic value and ample resources for research [8–17]. For example, a comparative gene expression study between a salt-tolerant and a salt-sensitive rice cultivar has shown that expression of genes related to protein synthesis and turnover were delayed in the sensitive variety and were perhaps responsible for the differential response . However, a recent report suggested that salt-tolerance was due to the constitutive expression of some stress responsive genes that in the sensitive variety were inducible . Transcriptional profiling of developing maize kernels in response to water deficit indicated that two classes of stress-responsive genes exist; one being specific to concurrent application of stress and another remains affected after transient stress . A previous study from our group also indicated that the dehydration-induced expression of some genes in chickpea remain unaffected even after removal of dehydration stress and may lead to adaptation . All these data point towards a hypothesis that a plant that is well adapted to stress has two basic mechanisms of stress-tolerance; constitutive expression of genes required for adaptation and quick expression of genes required to repair cellular damage and physiological reprogramming in adverse conditions. Comparative gene expression studies using cultivars with contrasting stress-tolerance features has become a useful tool to identify these two classes of genes.
In this study chickpea (Cicer arietinum), a popular food legume crop was used for analysis of gene expression under drought stress. Although chickpea is generally grown in relatively less irrigated lands and some cultivars adapt well to the water-limited environment , drought poses a serious threat to chickpea production causing 40-50% reduction of its yield potential . Lack of adequate genetic and genomic resources impede progress of crop improvement in chickpea. In one study, a pulse microarray, containing about 750 cDNAs from chickpea, grass-pea and lentil, was used for the analysis of gene expression in response to water limitation, cold temperatures, and high salinity, in chickpea cultivars with contrasting stress-tolerance features . Similarly, a database was generated from an EST library constructed by subtractive suppressive hybridization (SSH) of root tissue of two chickpea cultivars . Comparative proteome maps of chickpea nucleus and cell wall also revealed differentially expressed proteins during water-deficit stress [25, 26]. An exhaustive study on rapid dehydration-induced 26 bp SuperSAGE tags that were generated from root EST libraries of untreated and 6 h rapid dehydration-treated chickpea seedling has been reported. In addition, over 7000 UniTags having more than 2.7 fold abundance were identified in the dehydration libraries. Microarray analysis of 3000 of them exhibited about 80% congruency with the SuperSAGE data .
We have previously reported 101 ESTs of chickpea that were up-regulated more than 2 fold in response to rapid dehydration as compared to control conditions in the laboratory . However, drought conditions in the field are quite different. Furthermore, transcriptional activation of a particular gene by drought might not be directly related to drought tolerance. In this study, the gene expression of a relatively drought-tolerant and a drought-sensitive chickpea cultivar were compared in response to progressive depletion of water. We have constructed SSH libraries from whole seedlings of the two cultivars at different stages of water depletion. A number of genes that express constitutively, as well as many that were induced quickly after application of stress in the tolerant cultivar, were identified. Annotation by homology search indicated that these genes are involved in cellular organization, protein metabolism, signal transduction and transcription.
Differential drought tolerance in two chickpea cultivars
Cloning and sequencing of chickpea ESTs differentially expressed during drought stress
Comparative transcript profile of PUSABGD72 with respect to ICCV2
The expression of 319 unique ESTs obtained from the SSH libraries was analyzed by reverse-northern experiment as described previously . PCR amplified ESTs were spotted in duplicate on nylon membranes in a 96-spot format. Chickpea Actin gene was spotted as a control for normalization and the kanamycin resistance gene, NPTII was used as the negative control for background subtraction. Radio-labeled first strand cDNA probes prepared using poly (A+) RNA isolated from control/stressed samples of PUSABGD72 or ICCV2 were used for hybridization and ESTs expressed differentially in the two cultivars were identified by the obtained differential hybridization intensities. Expression of each clone was tested in at least three independent drought stress experiments to confirm reproducibility. Expression ratio was calculated following the methods described in previous studies [5, 20]. Signal intensity of each spot was normalized by subtracting the intensity of the negative control (NPTII). Fold expression was presented as the expression ratio (control/stressed) of PUSABGD72 to ICCV2 relative to the ratio of intensity of Actin. Genes showing ≥ 2 fold higher expression in PUSABGD72 at any time point in comparison to ICCV2 were considered as differentially expressed and studied further. Approximately 23%, 42.5%, 55.62% and 53.5% of the ESTs showed more than two-fold abundance in PUSABGD72 at control, 3 d, 6 d and 12 d DH conditions respectively. Relatively higher number of ESTs expressed differentially during DH treatment in PUSABGD72. 19.5% of all the ESTs showed ≥ 2 fold higher abundance in PUSABGD72 relative to that in ICCV2 at all the time points. ESTs expressing more in PUSABGD72 in comparison to ICCV2 at control condition naturally include drought-responsive and non-responsive genes.
It is already mentioned that about 23% of the ESTs (77 ESTs) showed ≥ 2 fold higher abundance in PUSABGD72 relative to that in ICCV2 at unstressed condition. Comparative transcript profiling revealed that 84% (65 ESTs) of these ESTs expressed more during the course of DH stress in PUSABGD72 in comparison to ICCV2. This result indicated that expression of most of the ESTs in this category is regulated by DH stress.
Monitoring expression profiles of selected high expressing genes
More stringent criteria were applied to shortlist the genes that were showing drastic expression differences in the two cultivars. The genes showing ≥ 2 fold higher relative expression at the unstressed condition and ≥ 3 fold higher relative expression at any point of DH stress treatment in PUSABGD72 in comparison to ICCV2 were considered. This stringent parameter screened 49 genes (Additional File 4) from the 77 described above. Eight of these belonged to signal transduction category e.g. CBL-interacting protein kinase (CIPK) [FL512440], putative protein kinases [CD051343, CD051317], protein phosphatase 2C [CD051312], G-protein coupled receptor [CD051322], 14-3-3 protein homolog [FL512351]. Implication of SOS2-like protein kinases (CIPKs) in providing abiotic stress tolerance by activating the membrane-bound transporters is well documented [7, 34–36]. Protein phosphatase 2C was shown to interact with SOS2 and mediate ABA-responsive signals [36, 37, 48]. Seven genes of transcription factor category mostly represented AP2-domain containing proteins. Members of the AP2/EREBP family of transcription factors, especially those that recognize drought-responsive element (DRE) in target promoters mediate distinct responses to abiotic stresses such as drought, salt and cold [38, 39]. Another gene in this group putatively encoded a α-NAC transcription factor. NAC belongs to a family of proteins specific to plants and are found to play a role in a diverse set of developmental processes including formation and maintenance of shoot apical meristem and floral morphogenesis [40, 41]. Overexpression of a NAC transcription factor in Arabidopsis up-regulated several stress-responsive genes in the transgenic plants, and thereby conferred drought tolerance . Zinc finger proteins [FL512439] are ubiquitous; some of them were shown to provide tolerance against abiotic stresses [43, 44]. Six ESTs represented well-known stress responsive genes encoding ABA-responsive protein [FL512397], stress activated protein [FL512411], salt tolerance proteins [FL512396, FL518936], dehydration-induced protein [FL512471]. High expression of ten genes under cellular organization category was well understood as they putatively encoded LEAs and dehydrins. Higher accumulation of dehydrin mRNA transcript in drought tolerant sunflower was associated with cellular turgor maintenance under drought stress . Dehydrin, LEA and proline rich proteins are thought to provide stability to other proteins in osmotic stress . High relative expression of six genes related to protein metabolism corroborates the results of a previous study with rice cultivars .
We recently reported the functional validation of two chickpea genes corresponding to two differentially expressed ESTs described in this study; one (FL512440) codes for a CBL-interacting proteins kinase (CaCIPK6; GenBank: DQ239702) and another (FL512348) for a zinc finger protein (CaZF; GenBank: EU513298). Expression of CaCIPK6 in tobacco and Arabidopsis conferred improved tolerance against high concentration of sodium chloride and mannitol . Ectopic expression of CaZF improved germination efficiency of transgenic tobacco in presence of high salinity .
To date, a limited number of studies on drought stress-mediated gene expression in chickpea have been reported. In this study we described an analysis of gene expression in chickpea in response to drought stress and intended to carry out a comparative transcript profiling between the contrasting chickpea varieties. We focused on a set of transcripts that exhibited higher abundance in a drought-tolerant cultivar in comparison to a drought-sensitive one. We took suppressive subtractive hybridization (SSH) approach to construct the EST libraries because chickpea EST resources are limited. We applied water-deficit stress by withdrawal of irrigation for three different periods. This allowed us to perform a series of comparison of transcript abundance between and within the chickpea varieties at different time points of stress treatment. Comparative expression profiles categorized the ESTs in 11 clusters according to their relative expression patterns. 53 ESTs were identified on the basis of their very high fold of relative expression in the tolerant variety. High fold of abundance of these transcripts in the tolerant variety might be just correlative and establishment of any relation between this transcript abundance and drought-tolerance in chickpea is beyond the scope of the experiments performed in this study. We also do not intend to comment that the mechanism of drought-tolerance in chickpea is limited to only transcriptional upregulation of some genes. The purpose of this study was to compare two contrasting chickpea varieties and to generate a resource to initiate gene-by-gene analysis for drought-tolerance mechanism.
The differential expression pattern of the transcripts observed might be applicable only to these two particular chickpea varieties used in this study, although the genes identified on the basis of differential expression patterns corroborate with results from some of the similar studies on other plants [8, 52]. In this study, a stress condition close to field drought was applied. Field drought is a slow process and the plants go through an adaptive process in contrast to the drastic condition of rapid dehydration. Furthermore, due to narrow genetic diversity among the cultivated legume varieties the genes that express co-incidentally due to DH stress may be common in both the varieties and, therefore, might not have been highlighted in a comparative gene expression analysis. These might be the reasons for less number of differentially expressed transcripts detected in our study in comparison to that in the SAGE analysis .
Temperate grain legumes such as pea, fava bean and lentil share similar gene arrangement with chickpea . It is, therefore, expected that this data will benefit the study of the similar grain legume crops. Since the genes that experience subtle changes in expression in DH stress might not have been detected due to the stringent method of construction of SSH cDNA library, much robust experimentation involving oligonucleotide-based microarrays supported by enough EST resources is required for clear understanding.
Plant materials and stress treatments
Chickpea (Cicer arietinum L. cv PUSABGD72 and ICCV2) seeds (provided by IARI, New Delhi, India and ICRISAT, Hyderabad, India respectively) were grown in 3 L pots with composite soil (peat compost to vermiculite, 1:1) for 12 d after germination at 22 ± 2°C and 50 ± 5% relative humidity with a photoperiod of 12 h. Both the cultivars were grown in the same pot so that they were exposed to the same soil moisture content. The pots were irrigated with 200 ml water everyday. For drought treatment, soil-grown 12 day-old plants were subjected to progressive drought by withholding water for 3, 6, and 12 d respectively. In this period the soil moisture content decreased from approximately 50% to approximately 15% at the end of 12 d. As a control, some plants were kept under the same condition for the same period with watering. Drought stressed plants were harvested at the same time of the day to avoid diurnal changes; immediately frozen in liquid nitrogen and stored at -80°C before RNA isolation. Relative water contents of the leaves were measured at the corresponding time points following standard method .
RNA isolation and construction of subtracted cDNA library
Total RNA was isolated from the harvested seedlings by using TRIzol Reagent (Life Technologies, Rockville, MD), and poly (A+) RNA was purified by mRNA isolation kit (Roche Applied Science, Manheim, Germany). Subtracted cDNA library was constructed by using CLONTECH PCR-Select cDNA subtraction kit (CLONTECH Laboratories, Palo Alto, CA) following the method provided by the manufacturer. In brief, tester (C/3 d/6 d/12 d drought PUSABGD72) and driver (C/3 d/6 d/12 d drought ICCV2) double stranded cDNAs were prepared from poly (A+) RNA (2 μg each) samples. The cDNAs were digested with RsaI and then ligated to different adaptors present in the kit. Two rounds of hybridization and PCR amplification (Advantage 2 PCR kit, CLONTECH) were performed to normalize and enrich the differentially expressed cDNAs. The forward subtracted and enriched DNA fragments were directly cloned into T/A cloning vector (pGEM-T Easy Vector Systems, Promega, USA). Competent cells of E. coli DH5α were prepared by CaCl2 method and transformed with the ligation mix and plated on Luria-Bertani (LB) agar plates containing ampicillin (selection marker), IPTG, and X-gal for blue-white selection . All the recombinant clones were pooled to establish the subtracted cDNA library.
Amplification of cDNA inserts
The cDNA insert of individual clones of the subtracted cDNA library were amplified by polymerase chain reaction (PCR) (Perkin-Elmer GeneAmp PCR System 9600) using M13 forward and M13 reverse primers in a 50 μL reaction with thermo-cycling condition: an initial denaturation at 94°C for 10 min, followed by 30 cycles of 94°C for 30 s, 60°C for 1 min, 72°C for 2 min and a final extension at 72°C for 10 min. The PCR products were analyzed by agarose gel electrophoresis for insert size, amplification quality and quantity. The positive clones were then selected for sequencing.
The selected positive clones were all single-pass sequenced using Big Dye Terminator kit version 3.0 (Applied Biosystems, Foster City, CA) and analyzed with the ABI Prizm 3700 DNA analyzer. The base-calling of the chromatogram files was performed automatically by PHRED processing  with sequence quality value of 20. Vector sequences were removed by CROSS_MATCH http://www.genome.washington.edu, and the polyA tails were trimmed off by Trimest of EMBOSS application http://www.emboss.org. Finally, high quality sequences were selected with base-calling error of ≤ 1% and reads of ≥ 200 bp. Each edited EST was searched against non-redundant protein database of NCBI http://www.ncbi.nlm.nih.gov using BLASTX. The default BLAST parameters were used. Putative functions to the ESTs were assigned based on the results of BLASTX searches. All cDNA fragments are registered in NCBI EST database. Unique ESTs were selected for further analysis.
cDNA Macroarray preparation
Purified PCR products were denatured by adding an equal volume of 0.6 M NaOH. Equal volume of each denatured PCR product (≈ 100 ng) of ≥ 200 bp of size was spotted on two Hybond N membranes (Amersham Pharmacia Biotech, Uppsala) using dot-blot apparatus in 96 format to make two identical arrays. In addition, PCR products of chickpea Actin cDNA [GenBank: AJ012685] and Neomycin phophotransferase (NPTII) gene from the vector pCAMBIA 1305.1 [GenBank: AF354045] were spotted as internal and negative controls respectively to normalize the signals of two replicate blots corresponding to stressed/unstressed chickpea cultivars and to subtract the background intensity respectively. The membranes were neutralized with neutralization buffer (0.5 M Tris-HCl, pH 7.4; 1.5 M NaCl) for 3 min, washed with 2× SSC, and cross-linked by UV cross linker (Stratagene, La Jolla, CA).
Probe preparation and reverse northern hybridization
cDNAs were labeled with α32P-dCTP in the first-strand reverse transcription of mRNA. One microgram of mRNA was labeled in a 20 μL reaction volume containing 1× reaction buffer, 2 μg of 5'-(dT)30VN-3' (V = A/G/C and N = A/G/C/T) primer, 2.5 mM dATP, dTTP, dGTP, 0.02 mM dCTP, 5 μL of α32P-dCTP (10 μCi/μL; 3000 μCi/mmol), and 200 units of reverse transcriptase (Superscript II, Life Technologies, Grand Islands, NY). After incubation at 42°C for 1 h, mRNA was removed by incubating with RNaseH (Life Technologies, Grand Islands, NY) at 37°C for 20 min. Radiolabeled cDNAs were cleaned by Sephadex G-25 (Amersham-Pharmacia Biotech) and suspended in hybridization buffer (7% SDS, 0.3 M Sod-phosphate pH 7.4, 1 mM EDTA, 10 μg of sonicated salmon sperm DNA). Nylon membranes were pre-hybridized with the same buffer for 3 h at 65°C and hybridized with denatured cDNA probes at the same condition for 24 h. The membranes were washed three times (10 min each) with washing buffer (2XSSC, 0.1% SDS, 65°C). The replicate membranes were then exposed to same storage phosphor screen (Amersham Biosciences, Piscataway, NJ) for 2 d. Images of the membranes were acquired by scanning with a Typhoon 9210 scanner (Amersham Biosciences).
Data analysis for DNA-array
Data analysis was performed using ImageQuant software (Molecular Dynamics, Sunnyvale, CA). The radioactive intensity of each spot was quantified as volume value. The local background value was subtracted resulting in the subtracted volume values (sVol). Actin cDNA was used as the internal control and its subtracted volume value was designated as sRef. Normalization among all images was performed by dividing sVol of each spot by the sRef value within the same image resulting in a normalized volume value (nVol) for each spot. nVol values of each EST spot in two identical arrays were compared. Three independent experiments were conducted to assess the reproducibility of the macroarray analysis. Data presented for the expression profile analysis is an average of three independent experiments. Expression profiles of the stress inducible genes were analyzed by the hierarchical SOTA (Self-organizing tree algorithm) clustering on the log-transformed-fold induction expression values across four time points by using MultiExperiment Viewer (MEV) Software (The Institute for Genome Research; http://rana.lbl.gov/EisenSoftware.htm) .
Total RNA was isolated from seedlings at different stress time points, separated by electrophoresis in denaturing formaldehyde 1.2% (w/v) agarose gels and transferred to Hybond-N+ nylon membrane (GE Healthcare, Buckinghamshire, UK) following the method mentioned in Sambrook et al. (2001) . PCR-amplified individual cDNA fragment (with primers corresponding to adaptor 1 and 2R used for preparing SSH library) was purified from agarose gel and used as a probe. Probes were labeled with α32P-dCTP using Megaprime DNA labeling system (GE Healthcare) and purified through Sephadex G-25. Northern hybridization, washing and scanning were performed and band-intensity was calculated by following the procedure described above for nylon membrane array.
Determination of relative water content (RWC)
Chickpea leaf tissues were collected and immediately weighed [fresh weight, FW]. The tissues were rehydrated in water for 24 h until fully turgid, surface-dried, reweighed [turgid weight, TW] followed by oven drying at 80°C for 48 h, and reweighed [dry weight, DW]. The RWC was calculated by the following formula: RWC (%) = (FW-DW/TW-DW) ×100 . The experiment was carried out in triplicates.
Estimation of abscisic acid, proline, and chlorophyll
ABA content of chickpea seedlings with or without stress was measured according to Setter et al. (2001) . Lyophilized seedlings were crushed in chilled 80% methanol. The extracts were fractionated by C18 reverse-phase chromatography, and the ABA-content was assayed by enzyme linked immunosorbant assay (ELISA). The ABA-content is expressed as microgram of ABA per gram of dry weight. Free Proline content was measured as described by Bates et al. (1973) . The tissues were homogenized in 3% aqueous sulfosalicylic acid. The homogenate was centrifuged at 9000× g and the supernatant was collected. The reaction mixture consisted of 2 ml of supernatant, 2 ml of acid-ninhydrin, and 2 ml of glacial acetic acid, which was boiled at 100°C for 1 h. After termination of the reaction on ice, the reaction mixture was extracted with 4 ml of toluene, and the absorbance was read at 520 nm. The assays were done in triplicates using corrected weight calculated for the actual moisture content of tissue at each time point. For chlorophyll estimation, tissues harvested at different time points were ground in 80% chilled acetone. The supernatant was taken and absorbance was read at 663 nm, 645 nm and 480 nm and calculated according to Lichtenthaler et al. (2001) . The experiments were done in triplicates using corrected tissue weights calculated for actual moisture content of the tissue at the respective time points.
This work was supported by a research grant received from Council for Scientific and Industrial Research (CSIR), Government of India. Authors thank Dr AK Sinha, Dr. Sabhyata Bhatia, NIPGR, India and Dr Indrani Bose, WCU, USA for editorial support. DJ acknowledges CSIR for research fellowship.
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