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Comparative analysis of ESTs in response to drought stress in chickpea (C. arietinum L.)
Biochemical and Biophysical Research Communications 376 (2008) 578–583
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Biochemical and Biophysical Research Communications journal homepage: www.elsevier.com/locate/ybbrc
Comparative analysis of ESTs in response to drought stress in chickpea (C. arietinum L.) Wen-Rui Gao a,1, Xian-Sheng Wang a,1, Qing-You Liu b,1, Hui Peng a, Chen Chen a, Jian-Gui Li c, Ju-Song Zhang c, Song-Nian Hu b,*, Hao Ma a,* a
State Key Lab of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Department of Agriculture, Nanjing Agricultural University, Xuanwu District, Weigang No. 1, Nanjing 210095, China b Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China c Key Lab of Agriculture Biotechnology, Xinjiang Agricultural University, Urumqi 830052, Xinjiang Uygur Autonomous Region, China
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Article history: Received 21 August 2008 Available online 17 September 2008
Keywords: Chickpea Drought stress Drought tolerance cDNA library Expressed sequence tags Quantitative real-time PCR
a b s t r a c t Chickpea (Cicer arietinum L.) is an important pulse crop grown mainly in the arid and semi-arid regions. To identify the water-stress-induced genes, two non-normalized cDNA libraries were constructed from the seedling leaves of a drought-tolerant chickpea cultivar under PEG-treated and -nontreated conditions. About 2500 clones from each library were selected randomly for sequencing analysis. Based on IDEG6 online software analysis, 92 genes were differentially expressed, and these genes were involved in diverse biological progresses, such as metabolism, transcription, signal transduction, protein synthesis and others. Most of the up-regulated genes were related to drought tolerance, and the down-regulated genes were mainly involved in photosynthesis. The differential expression patterns of five functional unigenes were confirmed by quantitative real-time PCR (qPCR). The results will help in understanding the molecular basis of drought tolerance in chickpea. Ó 2008 Elsevier Inc. All rights reserved.
Drought stress is the most common adverse environmental condition that can seriously reduce crop productivity. Increasing crop tolerance to drought stress would be the most economical approach to improve productivity and to reduce agricultural use of fresh water resource. To survive against the stress, plants have evolved a number of morphological, physiological, biochemical, and metabolic responses. Many changes in plant drought-induced gene expressions have been revealed, and a large number of genes have been identified. Proteins encoded by some of these identified genes have been confirmed to tolerate drought stress and protect cellular structures or involve in the signal transduction pathway [1–5]. Understanding the underlying mechanism will be of great benefit to the breeding of drought-tolerant crops. Chickpea (Cicer arietinum L.) is the third important legume crop grown mainly in the arid and semi-arid regions. Due to its taxonomic proximity with the model legume genome Medicago truncatula and its ability to grow in soil with relatively low water content, chickpea has its unique advantage to understand how plant responds to drought stress [6–8]. EST analysis has been a useful tool to elucidate the mechanism of its drought tolerance. However, only
* Corresponding authors. Fax: +86 10 82995373 (S.-N. Hu), +86 25 84395324 (H. Ma). E-mail addresses: [email protected] (S.-N. Hu), [email protected] (H. Ma). 1 These authors contributed equally to this work. 0006-291X/$ - see front matter Ó 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.bbrc.2008.09.030
2000 ESTs from chickpea have been deposited in NCBI EST database (March 10, 2008; http://www.ncbi.nlm.nih.gov/dbEST/ dbEST_summary.html). Due to its restraining availability of water without causing physiological damage, PEG has been widely used to simulate water-stress in study of plant drought tolerance. In this investigation, to identify the water-stress-induced genes, two cDNA libraries were constructed from the PEG-treated and -nontreated seedling leaves of a drought tolerant chickpea cultivar, respectively. About 2500 ESTs from each library were sequenced and analyzed, and a comparative analysis of the differentially expressed genes was performed. Furthermore, to test the reliability of libraries, five genes with various expression patterns under dehydration stress were selected and their expressions were examined by qPCR. Materials and methods Plant materials. The seeds from a drought-tolerant chickpea cultivar (cv. Xj-209) were germinated in quartz sand in a growth chamber with a day/night cycle of 14 h/10 h at 28 °C/20 °C. When grew for 10 days, the seedlings were carefully transferred to oneoff cups with water. 24 h later, the seedlings were separated into two groups: one was exposed to 60 mM PEG 4000, and another was using water as control.
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Library construction. Two non-normalized cDNA libraries, named MH1 and MH2, were constructed from the PEG-treated and -nontreated chickpea leaves, respectively. Total RNA of PEGtreated chickpea seedling leaves at 12, 24, 36, and 48 h and the PEG-nontreated chickpea seedling leaves were extracted, respectively, according to Chomczynski et al. [9]. Then the four PEG-treated total RNA samples were mixed equally. The mRNA was subsequently purified with Oligotex mRNA Kits (Qiagen). The cDNA was synthesized with Superscript II-RT (Invitrogen) and DNA polymerase I (Promega). The double strand cDNA was separated by electrophoresis, and two fractions (0.75–2 kb and >2 kb) were extracted and then cloned into pBluescript II vector (Stratagene) at EcoRI and XhoI sites. The plasmid was transformed into E. coli DH10B to amplify the cDNA. 5’ EST sequencing. The cDNA clones with different length of inserts were grown in LB medium containing 100 lg/ml ampicillin in 96-well plate for 16 h. The plasmids were extracted according to standard alkaline lysis protocol and sequenced on ABI 3730 sequencers using the T3 universal primer. Bioinformatic analysis. The chromatogram files were processed for base-calling and quality assessing by Phred software, the lowquality sequences were trimmed off with Q13 (95% accuracy) program. Cross-match program was run to trim vector and E. coli DNA sequences. Acceptable results (>100 bp) were saved in a FASTA format. High-quality ESTs were assembled using Phrap software (http://www.phrap.org/). Default settings were used except 40 bp minimum overlap and 99% identity [10]. Assembled contigs and singletons (called clusters) were manually revised by Consed software (http://www.phrap.org/) [11]. All clusters were compared with the NCBI nucleotide (nt) and non-redundant protein (nr) databases by BLASTN (E-values 61 1010) and BLASTX (E-values 61 105), respectively. The standards of exact choice of the most related entry in each group of alignments depended not only on the best hit values but also on the information of matched sequences in detail. All assembled sequences having the same annotation were further clustered into a unigene. Based on Gene Ontology (GO) classification, all unigenes were analyzed for their functional characteristics and gene expression profiles under drought stressed and controlled conditions. IDEG6 was used to identify differentially expressed genes, with general Chi-squared test (a = 0.05) used. Quantitative real-time PCR. The cDNA from chickpea seedlings leaves at different time (0, 3, 6, 9, 12, 24, 36, and 48 h) after PEGtreatment was synthesized with M-MLV Reverse Transcriptase (Invitrogen), respectively. qPCR was performed using EvaGreenTM qPCR Master Mix Kit (OPE Technology Development). The actin gene (Accession No.: AJ012685) was assigned for control. Relative Quantity (DCT) was calculated using the comparative CT method: Relative quantity sample (gene x) = 2ðCT ðcontrolÞCT ðsampleÞÞ The primers used for qPCR are listed in Table 1. Table 1 Primers used for qPCR Gene
Sequence
Strand
CapLEA-1
5’-ACAGACAACCGAAGCAAC-3’ 5’-GGGCCATACCCTTAACCT-3’ 5’-TGGTGGCACTGGAGATG-3’ 5’-AACTACCTGGGTTGTGGG-3’ 5’-GCTGGTTCTATTTCTCGTTTG-3’ 5’-AAGATGAGCCTCCATCACTC-3’ 5’-CAACACTTGAACAGCCTCAG-3’ 5’-GGGATGGGTTCCTTGCT-3’ 5’-AGTCCACCCTCACTAAAGATTTG-3’ 5’-TGGGAGCATTGGGATGTG-3’ 5’-TGTCTTGAGTGGTGGTTCTAC-3’ 5’-TTCATCATATTCTGCCTTTG-3’
Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse
Dehydrin 1 Nonspecific LTP precursor RuBisCO small subunit Chlorophyll a/b binding protein Actin
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Results cDNA library construction, EST sequencing, and clustering The titers of MH1 and MH2 were 4.9 105, 7.5 105 clones, respectively, which could meet almost all requirements of obtaining a cDNA clone from a low abundance mRNA. In MH1, 2772 clones produced readable sequences from 5’ end. After removing vectors and poor-quality segments, 2530 high-quality ESTs were obtained with a mean length of 491.59 bp. In MH2, 2689 clones produced readable sequences from 5’ end and 2567 high-quality ESTs were obtained with a mean length of 503.08 bp. The high-quality ESTs have been deposited in the GenBank database under Accession Nos.: FE668437–FE673533. The length distributions of the high-quality ESTs of the two libraries were similar (Fig. 1). By assembling, MH1 produced 1750 clusters with 305 contigs and 1445 singletons, while MH2 produced 1362 clusters with 328 contigs and 1034 singletons. Cluster identification In MH1, 67.4% and 77.8% clusters had significant similarities with sequences in the nt and nr databases, respectively. Similarly, in MH2, 69.5% and 80.9% clusters had significant similarities with sequences in nt and nr databases, respectively. The clusters with the same annotation were further manually combined to a unigene (Table 2). Finally, MH1 generated total 1663 unigenes. Among them, 82.32% had significant hits to known sequences. The other 17.68% (294 unigenes) were no hit to any gene in other plants in nr and nt databases), suggesting that they were novel and unique genes in chickpea. For MH2, 84.98% of 1292 unigenes had significant hits to known sequences, and remaining 15.02% (194 unigenes) were novel. Therefore, the unigenes expressed in MH1 were 371 more than those in MH2 There. There were 259 genes expressed in both libraries (Fig. 2). The highly expressed unigenes (P7 ESTs) and their annotations in MH1 and MH2 were shown in Supplementary data 1 and supplementary data 2, respectively. MH1 contained 28 highly expressed unigenes, many of which were found to be correlative with drought tolerance. These genes encoded for late embryogenesis abundant (LEA) proteins, nonspecific lipid-transfer protein precursor (LTP), and probable cytochrome P450 monooxygenase, etc. MH1 also contained some unigenes maintaining normal metabolism, such as genes encoding oxygen-evolving enhancer protein, ribulose 1,5-bisphosphate carboxylase small subunit, etc. The highest expressed unigene in MH1 was the gene encoding dehydrin 1, which contained 82 ESTs. Dehydrins appear to be common products responding to drought stress in plant [12]. In MH2, many unigenes were related to photosynthesis process. This might be due to that MH2 was constructed from the normal growth leaves of PEG-nontreated chickpea seedlings. All clusters were further analyzed by the GO assignments. 496 clusters (1351 GO terms) of MH1 and 464 clusters (1298 GO terms) of MH2 were classified into three broad categories, including biological processes, cellular components and molecular functions (Fig. 3). The detailed GO assignment results are listed in Supplementary data 3. Identification of differentially expressed genes According to IDEG6 online software analysis, 36 genes PEG upregulated and 56 genes PEG down-regulated were identified (Supplementary data 4). Up-regulated genes Thirty-six up-regulated genes could be classified into four different categories: metabolism, genetic information processing, cel-
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600
MH1
MH2
500
Numbers
400 300 200 100 0
100-150 150-200 200-250 250-300 300-350 350-400 400-450 450-500 500-550 550-600 600-650 650-700 700-750
High-quality ESTs Length Fig. 1. The length distributions of high-quality ESTs of MH1 and MH2.
Table 2 Representative unigenes from the ESTs derived from two libraries Library
Category
Number of unigenes
Number of clones
Redundancy (clone/cluster)
MH1
No. nr and nt database match Matching sequences
1663 1369
2530 2226
1.52 1.63
MH2
No. nr and nt database match Matching sequences
1292 1098
2567 2355
1.99 2.14
MH1 total 1663 genes
1404
MH2 total 1292 genes
259
1033
Common Fig. 2. The numbers of expressed genes of two libraries.
lular processes and stress-related genes. Most of the genes were categorized as stress-related genes. Seven genes were classified into metabolism category. Among them, four genes were found to be very significantly (P 6 0.01) up-regulated, and these genes encoded acidic endochitinase precursor (chickpea.1024), nonspecific lipid-transfer protein precursor (LTP, Chickpea.0131), S-adenosylmethionine synthetase (SAMs, chickpea.1354), and Cytochrome P450 monooxygenase (chickpea.0943), respectively. Acidic endochitinase precursor may play a particular role in plant development or in protecting plants from pathogen attack during water-stress [13]. LTP may have functions in repair of stress-induced damages in membranes or changes in the lipid composition of membranes, and perhaps in regulation of the permeability of toxic ions and the fluidity of the membrane [14]. SAMs catalyzes biosynthesis of S-adenosylmethionine, which provides a methyl group for many metabolites including important compounds under stress conditions, such as glycinebetaine, methylated polyols and polyamines. And S-adenosylmethionine is also a precursor of ethylene biosynthesis [15]. Cytochrome P450 monooxygenase catalyzes the first step in the
oxidative degradation of ABA and is considered to be a pivotal enzyme in controlling the degradation rate of plant hormone [16]. Twenty-seven genes were classified as stress-related genes. Among them, four genes encoded LEA proteins, which belonged to three groups of LEA proteins [17,18]. LEA proteins mainly play functions in whole-plant tolerance to drought, cold, etc. [19]. Chickpea.1600, similar to Os02g0252100 protein has RNA-binding region and RNP-1 (motif). It has been suggested that RNA-binding proteins involve in general molecular responses to stress conditions [20]. High mobility group 1 protein (HMG-1, chickpea.1356) is a small and relatively abundant chromatin-associated protein found in the nuclei of higher eukaryotes, and its expression is regulated by various abiotic stresses including drought [21]. Protein phosphatase type 2C (PP2C, chickpea.0922) has been found as the regulator of signal transduction pathways [22]. Cysteine proteases (chickpea.0122) seem to play a major role in the senescing leaves with the ability to degrade the large subunit of RuBisCO. Many forms of cysteine proteases induced by drought can not be observed during natural senescence [23]. Two genes, encoding Mss4-like protein (chickpea.0743) and Concanavalin A-like lectin/glucanase (chickpea.0750), were classified into ‘‘genetic information processing” and ‘‘cellular processes”, respectively. Some PEG up-regulated genes in this study were also found to be up-regulated by the other abiotic and biotic stresses, such as genes encoding cold acclimation responsive protein BudCAR4 (chickpea.0164), Al-induced protein (chickpea.1122), and thaumatin-like protein PR-5a (chickpea.1114), etc. This suggests that there is cross-talk among abiotic and biotic stresses [14]. Down-regulated genes Many researches have focused on elucidating how relevant genes are up-regulated during drought stress. However, the response to drought also involves the down-regulated genes [24]. 56 down-regulated unigenes were identified, and they could be classified into 3 different categories (metabolism, genetic information processing and stress-related genes). Four genes were classified into carbohydrate metabolism. These genes encoded GAE1 (chickpea.0048), granule-bound starch synthase (chickpea.1867), glycolate oxidase (chickpea.0095) and phosphoglycerate kinase (chickpea.2313), respectively. Twentysix unigenes were photosynthesis-related in energy metabolism subcategory. Among them, 11 genes (chickpea.0132, chickpea.0152, chickpea.1734, chickpea.2173, chickpea.2309, chickpea.0006, chickpea.2236, chickpea.0198, chickpea.2190, chickpea.
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0147, and chickpea.2404) encoded chlorophyll a/b binding (Cab) proteins. Cab proteins are located in the chloroplast thylakoid membrane, and their gene expression is regulated by light and developmental cues [25]. Ribulose 1,5-bisphosphate carboxylase (RuBisCO), which is the most abundant protein in chloroplasts, catalyzes the first step of Calvin cycle. In the present study, the transcription level of the small subunit (chickpea.0133) of RuBisCO was suppressed by PEG stress. Three genes, encoding Type IIB calcium ATPase MCA5 (chickpea.1952), 4-hydroxyphenylpyruvate dioxygenase (chicpea.2178) and putative desaturase-like protein (chickpea.1671), were classified into ‘‘sulfur metabolism”, ‘‘amino acid metabolism”, and ‘‘cofactor and vitamin metabolism”, respectively. Two genes, encoding Ribosomal protein L9 precursor (chickpea.2206) and
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RuBisCO large subunit-binding protein subunit beta (chickpea.2002), belonged to genetic information processing. In addition, 21 genes were classified as stress-related genes. These genes included chickpea.0029 (encoding putative senescence-associated protein), chickpea.0186 (encoding putative mitochondrial dicarboxylate carrier protein), chickpea.0042 (encoding MAPKKK14), and so on. Quantitative real-time PCR analysis To validate the EST analysis, 5 genes with different expression patterns under dehydration stress were selected for qPCR analysis (Fig. 4A-E). Among them, 3 genes, chickpea.0097 (encoding CapLEA-1), chickpea.0106 (encoding dehydrin 1) and chickpea.0131
Fig. 3. The Gene Ontology (GO) categories of genes from MH1 and MH2. The genes were functionally categorized according to the Gene Ontology Consortium and two levels of the assignment results were plotted here. In this ontology, ‘‘biological process”, ‘‘cellular component” and ‘‘molecular function” are categorized independently. In total, 496 clusters (corresponding to 1351 GO terms) of MH1 and 464 clusters (corresponding to 1298 GO terms) of MH2 were classified into three broad categories.
Fig. 4. qPCR analysis the expression changes of five genes under PEG stress. The error bars indicated the standard deviations of measurements (n = 3).
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(encoding LTP), which played important roles in drought tolerance, were up-regulated under drought stress. The other 2 genes, chickpea.0132 (encoding chlorophyll a/b binding protein) and chickpea.0133 (encoding RuBisCO small subunit), which were important in photosynthesis, were down-regulated under drought stress. The gene expression of CapLEA-1 and LTP was found to increase gradually and reach culmination at 48 h after PEG stress (Fig. 4A and C). The gene expression level of Dehydrin 1 reached culmination at 12 h after PEG stress, and then decreased (Fig. 4 B). Instead, RuBisCO small subunit is a PEG repressive gene. Its expression level decreased after PEG stress (Fig. 4 D). However after PEG-treatment, the gene expression level of chlorophyll a/b binding protein slightly decreased from 0 to 24 h but highly increased at 36 h, and then again rapidly decreased (Fig. 4 E). Discussion The qPCR results were in agreement with those of the EST analysis in this study, so the two cDNA libraries could be used to analyze transcript profiles of genes. Water deficit has been found to alter plant gene expression and lead to specific gene induction [3]. In is study, we have observed 36 up-regulated and 56 down-regulated genes in chickpea seedling leaves under dehydration stress. Some of those differentially expressed genes, such as genes encoding LEA proteins, LTP, Cytochrome P450 Monooxygenase, SAMs, etc. have been reported [14,24,26,27]. These genes might be universal drought-responsive genes in the plant kingdom. LEA proteins is generally found to play special roles in protecting cytoplasm from dehydration and in protecting plants by palliating the toxicity produced by the high concentrations of ions, however, their functions related to other aspects remain unknown [18,19]. Plant LTP is involved in a variety of biological processes, such as contribution to formation of an impermeable layer, which impedes water loss, and defence reactions against phytopathogens, and plant adaptation to various environmental conditions. However, it is not clearly understood how LTPs exert their effects and how LTPs are mediated in these processes [18,28]. LEA proteins are generally classified into six groups (families) and the first three groups are the major ones, which are basically localized in cytoplasm and nuclear region. We found that four proteins belonged to the first three groups of LEA proteins were PEG-induced. These four proteins were soybean seed maturation polypeptides (chickpea.1273, belonging to Group 1), Dehydrin 1 (chickpea.0106, belonging to Group 2), CapLEA-1 (chickpea.0097, belonging to Group 3), and CapLEA-2 (chickpea.1214, belonging to Group 3). In MH1, the highest expressed unigene was Dehydrin 1 (chickpea.0106), with 82 clones (Supplementary data 2). Second was nsLTP (chickpea.0131), with 48 clones. While the genes encoding CapLEA-1 (chickpea.0097), CapLEA-2 (chickpea.1214) and soybean seed maturation polypeptides (chickpea.1273) had 30, 20 and 20 clones, respectively. This indicated that under PEG stress, some genes belonging to the groups 2 and 3 LEA proteins had higher expression than those belonging to the group 1 LEA proteins. Based on the IDEG6 results, LTP and LEA proteins (Groups 1, 2 and 3) seemed to be very important in chickpea response to PEG stress. The gene expression level of Dehydrin 1 reached culmination at 12 h after PEG stress and then showed a decrease tendency, while the gene expression level of CapLEA-1 increased continuously (Fig. 4A and B). This suggests that some of the group 2 LEA proteins play significant functions at earlier stage of PEG stress, while some of the group 3 LEA proteins play more important functions at latter stage of PEG stress. The similar result is only found in Loblolly Pine [29]. These results also indicate that LEA proteins and LTP play pivotal roles in drought tolerance of chickpea [18].
In this study, the gene expression level of putative senescenceassociated protein (chickpea.0029) was down-regulated under PEG stress, while the gene expression level of cysteine proteinase (chickpea.0122) was up-regulated. It indicates that senescence-related proteins have different express responses to drought stress [23]. Through IDEG6 analysis, the photosynthesis-related genes, such as genes encoding RuBisCO small subunit and chlorophyll a/b binding proteins, were also regulated under PEG stress (Fig. 4D and E). It suggests that chickpea exhibits a physiological adaptation to drought stress. When chickpea abruptly met drought stress, the photosynthesis was inhibited. After sometime under PEG-treatment, chickpea adapted to the stress and the photosynthesis increased. However, when drought stress was overtime, the photosynthesis was inhibited again. Other studies have revealed that drought, cold, high salinity, and ABA stresses can inhibit photosynthesis [26]. Interestingly, some differentially expressed unigenes have not been reported in other species. For example, chickpea.0620 and chickpea.0627 only have BLASTn annotation, and they were induced under PEG stress. Some other up-regulated unigenes, including chickpea.0007, chickpea.0143, chickpea.1230, and chickpea.0925 (Supplementary data 4), encoded unknown proteins, and they might have specific functions in chickpea specific response to drought stress. In summary, our comparative analysis of ESTs in response to drought stress and finding of some up-regulated genes involved in signal transduction pathway, ABA catabolism, protein degradation, and SAM anabolism, etc., which indicates that multi-processes may play important roles in drought tolerance in chickpea, will contribute to understanding of the underlying molecular basis of chickpea drought tolerance. This will also be helpful for the discovery of the mark genes involved in drought tolerance and crop improvement. Acknowledgments We thank Ph.D. Tianbao Yang (Department of Hortculture/Center For Integrated Biotechnology, Washington State University) for revising the manuscript. We gratefully acknowledge the partial financial support from the 111 Project, from projects supported by National Science and Technology Ministry (2007BAC15B06, 2006BAD09A04, 2006BAD09A08), from Project supported by National Natural Science Foundation of China (30860152), and from Program of Education Department of Xinjiang Autonomous Region (XJEDU2004I17) for this research. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.bbrc.2008.09.030. References [1] L. Xiong, R.G. Wang, G. Mao, J.M. Koczan, Identification of drought tolerance determinants by genetic analysis of root response to drought stress and abscisic acid, Plant Physiol. 142 (2006) 1065–1074. [2] E.A. Bray, Plant responses to water deficit, Trends Plant Sci. 2 (1997) 48– 54. [3] J. Ingram, D. Bartels, The molecular basis of dehydration tolerance in plants, Annu. Rev. Plant Physiol. Plant Mol. Biol. 47 (1996) 377–433. [4] K. Shinozaki, K. Yamaguchi-Shinozaki, Molecular responses to drought and cold stress, Curr. Opin. Biotechnol. 7 (1996) 161–167. [5] A. Campalans, R. Messeguer, A. Goday, M. Pages, Plant responses to drought, from ABA signal transduction events to the action of the induced proteins, Plant Physiol. Biochem. 37 (1999) 327–340. [6] P. Winter, T. Pfaff, S.M. Udupa, B. Hüttel, P.C. Sharma, S. Sahi, R. ArreguinEspinoza, F. Weigand, F.J. Muehlbauer, G. Kahl, Characterization and mapping
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Biochemical and Biophysical Research Communications journal homepage: www.elsevier.com/locate/ybbrc
Comparative analysis of ESTs in response to drought stress in chickpea (C. arietinum L.) Wen-Rui Gao a,1, Xian-Sheng Wang a,1, Qing-You Liu b,1, Hui Peng a, Chen Chen a, Jian-Gui Li c, Ju-Song Zhang c, Song-Nian Hu b,*, Hao Ma a,* a
State Key Lab of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Department of Agriculture, Nanjing Agricultural University, Xuanwu District, Weigang No. 1, Nanjing 210095, China b Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China c Key Lab of Agriculture Biotechnology, Xinjiang Agricultural University, Urumqi 830052, Xinjiang Uygur Autonomous Region, China
a r t i c l e
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Article history: Received 21 August 2008 Available online 17 September 2008
Keywords: Chickpea Drought stress Drought tolerance cDNA library Expressed sequence tags Quantitative real-time PCR
a b s t r a c t Chickpea (Cicer arietinum L.) is an important pulse crop grown mainly in the arid and semi-arid regions. To identify the water-stress-induced genes, two non-normalized cDNA libraries were constructed from the seedling leaves of a drought-tolerant chickpea cultivar under PEG-treated and -nontreated conditions. About 2500 clones from each library were selected randomly for sequencing analysis. Based on IDEG6 online software analysis, 92 genes were differentially expressed, and these genes were involved in diverse biological progresses, such as metabolism, transcription, signal transduction, protein synthesis and others. Most of the up-regulated genes were related to drought tolerance, and the down-regulated genes were mainly involved in photosynthesis. The differential expression patterns of five functional unigenes were confirmed by quantitative real-time PCR (qPCR). The results will help in understanding the molecular basis of drought tolerance in chickpea. Ó 2008 Elsevier Inc. All rights reserved.
Drought stress is the most common adverse environmental condition that can seriously reduce crop productivity. Increasing crop tolerance to drought stress would be the most economical approach to improve productivity and to reduce agricultural use of fresh water resource. To survive against the stress, plants have evolved a number of morphological, physiological, biochemical, and metabolic responses. Many changes in plant drought-induced gene expressions have been revealed, and a large number of genes have been identified. Proteins encoded by some of these identified genes have been confirmed to tolerate drought stress and protect cellular structures or involve in the signal transduction pathway [1–5]. Understanding the underlying mechanism will be of great benefit to the breeding of drought-tolerant crops. Chickpea (Cicer arietinum L.) is the third important legume crop grown mainly in the arid and semi-arid regions. Due to its taxonomic proximity with the model legume genome Medicago truncatula and its ability to grow in soil with relatively low water content, chickpea has its unique advantage to understand how plant responds to drought stress [6–8]. EST analysis has been a useful tool to elucidate the mechanism of its drought tolerance. However, only
* Corresponding authors. Fax: +86 10 82995373 (S.-N. Hu), +86 25 84395324 (H. Ma). E-mail addresses: [email protected] (S.-N. Hu), [email protected] (H. Ma). 1 These authors contributed equally to this work. 0006-291X/$ - see front matter Ó 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.bbrc.2008.09.030
2000 ESTs from chickpea have been deposited in NCBI EST database (March 10, 2008; http://www.ncbi.nlm.nih.gov/dbEST/ dbEST_summary.html). Due to its restraining availability of water without causing physiological damage, PEG has been widely used to simulate water-stress in study of plant drought tolerance. In this investigation, to identify the water-stress-induced genes, two cDNA libraries were constructed from the PEG-treated and -nontreated seedling leaves of a drought tolerant chickpea cultivar, respectively. About 2500 ESTs from each library were sequenced and analyzed, and a comparative analysis of the differentially expressed genes was performed. Furthermore, to test the reliability of libraries, five genes with various expression patterns under dehydration stress were selected and their expressions were examined by qPCR. Materials and methods Plant materials. The seeds from a drought-tolerant chickpea cultivar (cv. Xj-209) were germinated in quartz sand in a growth chamber with a day/night cycle of 14 h/10 h at 28 °C/20 °C. When grew for 10 days, the seedlings were carefully transferred to oneoff cups with water. 24 h later, the seedlings were separated into two groups: one was exposed to 60 mM PEG 4000, and another was using water as control.
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Library construction. Two non-normalized cDNA libraries, named MH1 and MH2, were constructed from the PEG-treated and -nontreated chickpea leaves, respectively. Total RNA of PEGtreated chickpea seedling leaves at 12, 24, 36, and 48 h and the PEG-nontreated chickpea seedling leaves were extracted, respectively, according to Chomczynski et al. [9]. Then the four PEG-treated total RNA samples were mixed equally. The mRNA was subsequently purified with Oligotex mRNA Kits (Qiagen). The cDNA was synthesized with Superscript II-RT (Invitrogen) and DNA polymerase I (Promega). The double strand cDNA was separated by electrophoresis, and two fractions (0.75–2 kb and >2 kb) were extracted and then cloned into pBluescript II vector (Stratagene) at EcoRI and XhoI sites. The plasmid was transformed into E. coli DH10B to amplify the cDNA. 5’ EST sequencing. The cDNA clones with different length of inserts were grown in LB medium containing 100 lg/ml ampicillin in 96-well plate for 16 h. The plasmids were extracted according to standard alkaline lysis protocol and sequenced on ABI 3730 sequencers using the T3 universal primer. Bioinformatic analysis. The chromatogram files were processed for base-calling and quality assessing by Phred software, the lowquality sequences were trimmed off with Q13 (95% accuracy) program. Cross-match program was run to trim vector and E. coli DNA sequences. Acceptable results (>100 bp) were saved in a FASTA format. High-quality ESTs were assembled using Phrap software (http://www.phrap.org/). Default settings were used except 40 bp minimum overlap and 99% identity [10]. Assembled contigs and singletons (called clusters) were manually revised by Consed software (http://www.phrap.org/) [11]. All clusters were compared with the NCBI nucleotide (nt) and non-redundant protein (nr) databases by BLASTN (E-values 61 1010) and BLASTX (E-values 61 105), respectively. The standards of exact choice of the most related entry in each group of alignments depended not only on the best hit values but also on the information of matched sequences in detail. All assembled sequences having the same annotation were further clustered into a unigene. Based on Gene Ontology (GO) classification, all unigenes were analyzed for their functional characteristics and gene expression profiles under drought stressed and controlled conditions. IDEG6 was used to identify differentially expressed genes, with general Chi-squared test (a = 0.05) used. Quantitative real-time PCR. The cDNA from chickpea seedlings leaves at different time (0, 3, 6, 9, 12, 24, 36, and 48 h) after PEGtreatment was synthesized with M-MLV Reverse Transcriptase (Invitrogen), respectively. qPCR was performed using EvaGreenTM qPCR Master Mix Kit (OPE Technology Development). The actin gene (Accession No.: AJ012685) was assigned for control. Relative Quantity (DCT) was calculated using the comparative CT method: Relative quantity sample (gene x) = 2ðCT ðcontrolÞCT ðsampleÞÞ The primers used for qPCR are listed in Table 1. Table 1 Primers used for qPCR Gene
Sequence
Strand
CapLEA-1
5’-ACAGACAACCGAAGCAAC-3’ 5’-GGGCCATACCCTTAACCT-3’ 5’-TGGTGGCACTGGAGATG-3’ 5’-AACTACCTGGGTTGTGGG-3’ 5’-GCTGGTTCTATTTCTCGTTTG-3’ 5’-AAGATGAGCCTCCATCACTC-3’ 5’-CAACACTTGAACAGCCTCAG-3’ 5’-GGGATGGGTTCCTTGCT-3’ 5’-AGTCCACCCTCACTAAAGATTTG-3’ 5’-TGGGAGCATTGGGATGTG-3’ 5’-TGTCTTGAGTGGTGGTTCTAC-3’ 5’-TTCATCATATTCTGCCTTTG-3’
Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse Forward Reverse
Dehydrin 1 Nonspecific LTP precursor RuBisCO small subunit Chlorophyll a/b binding protein Actin
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Results cDNA library construction, EST sequencing, and clustering The titers of MH1 and MH2 were 4.9 105, 7.5 105 clones, respectively, which could meet almost all requirements of obtaining a cDNA clone from a low abundance mRNA. In MH1, 2772 clones produced readable sequences from 5’ end. After removing vectors and poor-quality segments, 2530 high-quality ESTs were obtained with a mean length of 491.59 bp. In MH2, 2689 clones produced readable sequences from 5’ end and 2567 high-quality ESTs were obtained with a mean length of 503.08 bp. The high-quality ESTs have been deposited in the GenBank database under Accession Nos.: FE668437–FE673533. The length distributions of the high-quality ESTs of the two libraries were similar (Fig. 1). By assembling, MH1 produced 1750 clusters with 305 contigs and 1445 singletons, while MH2 produced 1362 clusters with 328 contigs and 1034 singletons. Cluster identification In MH1, 67.4% and 77.8% clusters had significant similarities with sequences in the nt and nr databases, respectively. Similarly, in MH2, 69.5% and 80.9% clusters had significant similarities with sequences in nt and nr databases, respectively. The clusters with the same annotation were further manually combined to a unigene (Table 2). Finally, MH1 generated total 1663 unigenes. Among them, 82.32% had significant hits to known sequences. The other 17.68% (294 unigenes) were no hit to any gene in other plants in nr and nt databases), suggesting that they were novel and unique genes in chickpea. For MH2, 84.98% of 1292 unigenes had significant hits to known sequences, and remaining 15.02% (194 unigenes) were novel. Therefore, the unigenes expressed in MH1 were 371 more than those in MH2 There. There were 259 genes expressed in both libraries (Fig. 2). The highly expressed unigenes (P7 ESTs) and their annotations in MH1 and MH2 were shown in Supplementary data 1 and supplementary data 2, respectively. MH1 contained 28 highly expressed unigenes, many of which were found to be correlative with drought tolerance. These genes encoded for late embryogenesis abundant (LEA) proteins, nonspecific lipid-transfer protein precursor (LTP), and probable cytochrome P450 monooxygenase, etc. MH1 also contained some unigenes maintaining normal metabolism, such as genes encoding oxygen-evolving enhancer protein, ribulose 1,5-bisphosphate carboxylase small subunit, etc. The highest expressed unigene in MH1 was the gene encoding dehydrin 1, which contained 82 ESTs. Dehydrins appear to be common products responding to drought stress in plant [12]. In MH2, many unigenes were related to photosynthesis process. This might be due to that MH2 was constructed from the normal growth leaves of PEG-nontreated chickpea seedlings. All clusters were further analyzed by the GO assignments. 496 clusters (1351 GO terms) of MH1 and 464 clusters (1298 GO terms) of MH2 were classified into three broad categories, including biological processes, cellular components and molecular functions (Fig. 3). The detailed GO assignment results are listed in Supplementary data 3. Identification of differentially expressed genes According to IDEG6 online software analysis, 36 genes PEG upregulated and 56 genes PEG down-regulated were identified (Supplementary data 4). Up-regulated genes Thirty-six up-regulated genes could be classified into four different categories: metabolism, genetic information processing, cel-
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600
MH1
MH2
500
Numbers
400 300 200 100 0
100-150 150-200 200-250 250-300 300-350 350-400 400-450 450-500 500-550 550-600 600-650 650-700 700-750
High-quality ESTs Length Fig. 1. The length distributions of high-quality ESTs of MH1 and MH2.
Table 2 Representative unigenes from the ESTs derived from two libraries Library
Category
Number of unigenes
Number of clones
Redundancy (clone/cluster)
MH1
No. nr and nt database match Matching sequences
1663 1369
2530 2226
1.52 1.63
MH2
No. nr and nt database match Matching sequences
1292 1098
2567 2355
1.99 2.14
MH1 total 1663 genes
1404
MH2 total 1292 genes
259
1033
Common Fig. 2. The numbers of expressed genes of two libraries.
lular processes and stress-related genes. Most of the genes were categorized as stress-related genes. Seven genes were classified into metabolism category. Among them, four genes were found to be very significantly (P 6 0.01) up-regulated, and these genes encoded acidic endochitinase precursor (chickpea.1024), nonspecific lipid-transfer protein precursor (LTP, Chickpea.0131), S-adenosylmethionine synthetase (SAMs, chickpea.1354), and Cytochrome P450 monooxygenase (chickpea.0943), respectively. Acidic endochitinase precursor may play a particular role in plant development or in protecting plants from pathogen attack during water-stress [13]. LTP may have functions in repair of stress-induced damages in membranes or changes in the lipid composition of membranes, and perhaps in regulation of the permeability of toxic ions and the fluidity of the membrane [14]. SAMs catalyzes biosynthesis of S-adenosylmethionine, which provides a methyl group for many metabolites including important compounds under stress conditions, such as glycinebetaine, methylated polyols and polyamines. And S-adenosylmethionine is also a precursor of ethylene biosynthesis [15]. Cytochrome P450 monooxygenase catalyzes the first step in the
oxidative degradation of ABA and is considered to be a pivotal enzyme in controlling the degradation rate of plant hormone [16]. Twenty-seven genes were classified as stress-related genes. Among them, four genes encoded LEA proteins, which belonged to three groups of LEA proteins [17,18]. LEA proteins mainly play functions in whole-plant tolerance to drought, cold, etc. [19]. Chickpea.1600, similar to Os02g0252100 protein has RNA-binding region and RNP-1 (motif). It has been suggested that RNA-binding proteins involve in general molecular responses to stress conditions [20]. High mobility group 1 protein (HMG-1, chickpea.1356) is a small and relatively abundant chromatin-associated protein found in the nuclei of higher eukaryotes, and its expression is regulated by various abiotic stresses including drought [21]. Protein phosphatase type 2C (PP2C, chickpea.0922) has been found as the regulator of signal transduction pathways [22]. Cysteine proteases (chickpea.0122) seem to play a major role in the senescing leaves with the ability to degrade the large subunit of RuBisCO. Many forms of cysteine proteases induced by drought can not be observed during natural senescence [23]. Two genes, encoding Mss4-like protein (chickpea.0743) and Concanavalin A-like lectin/glucanase (chickpea.0750), were classified into ‘‘genetic information processing” and ‘‘cellular processes”, respectively. Some PEG up-regulated genes in this study were also found to be up-regulated by the other abiotic and biotic stresses, such as genes encoding cold acclimation responsive protein BudCAR4 (chickpea.0164), Al-induced protein (chickpea.1122), and thaumatin-like protein PR-5a (chickpea.1114), etc. This suggests that there is cross-talk among abiotic and biotic stresses [14]. Down-regulated genes Many researches have focused on elucidating how relevant genes are up-regulated during drought stress. However, the response to drought also involves the down-regulated genes [24]. 56 down-regulated unigenes were identified, and they could be classified into 3 different categories (metabolism, genetic information processing and stress-related genes). Four genes were classified into carbohydrate metabolism. These genes encoded GAE1 (chickpea.0048), granule-bound starch synthase (chickpea.1867), glycolate oxidase (chickpea.0095) and phosphoglycerate kinase (chickpea.2313), respectively. Twentysix unigenes were photosynthesis-related in energy metabolism subcategory. Among them, 11 genes (chickpea.0132, chickpea.0152, chickpea.1734, chickpea.2173, chickpea.2309, chickpea.0006, chickpea.2236, chickpea.0198, chickpea.2190, chickpea.
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0147, and chickpea.2404) encoded chlorophyll a/b binding (Cab) proteins. Cab proteins are located in the chloroplast thylakoid membrane, and their gene expression is regulated by light and developmental cues [25]. Ribulose 1,5-bisphosphate carboxylase (RuBisCO), which is the most abundant protein in chloroplasts, catalyzes the first step of Calvin cycle. In the present study, the transcription level of the small subunit (chickpea.0133) of RuBisCO was suppressed by PEG stress. Three genes, encoding Type IIB calcium ATPase MCA5 (chickpea.1952), 4-hydroxyphenylpyruvate dioxygenase (chicpea.2178) and putative desaturase-like protein (chickpea.1671), were classified into ‘‘sulfur metabolism”, ‘‘amino acid metabolism”, and ‘‘cofactor and vitamin metabolism”, respectively. Two genes, encoding Ribosomal protein L9 precursor (chickpea.2206) and
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RuBisCO large subunit-binding protein subunit beta (chickpea.2002), belonged to genetic information processing. In addition, 21 genes were classified as stress-related genes. These genes included chickpea.0029 (encoding putative senescence-associated protein), chickpea.0186 (encoding putative mitochondrial dicarboxylate carrier protein), chickpea.0042 (encoding MAPKKK14), and so on. Quantitative real-time PCR analysis To validate the EST analysis, 5 genes with different expression patterns under dehydration stress were selected for qPCR analysis (Fig. 4A-E). Among them, 3 genes, chickpea.0097 (encoding CapLEA-1), chickpea.0106 (encoding dehydrin 1) and chickpea.0131
Fig. 3. The Gene Ontology (GO) categories of genes from MH1 and MH2. The genes were functionally categorized according to the Gene Ontology Consortium and two levels of the assignment results were plotted here. In this ontology, ‘‘biological process”, ‘‘cellular component” and ‘‘molecular function” are categorized independently. In total, 496 clusters (corresponding to 1351 GO terms) of MH1 and 464 clusters (corresponding to 1298 GO terms) of MH2 were classified into three broad categories.
Fig. 4. qPCR analysis the expression changes of five genes under PEG stress. The error bars indicated the standard deviations of measurements (n = 3).
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(encoding LTP), which played important roles in drought tolerance, were up-regulated under drought stress. The other 2 genes, chickpea.0132 (encoding chlorophyll a/b binding protein) and chickpea.0133 (encoding RuBisCO small subunit), which were important in photosynthesis, were down-regulated under drought stress. The gene expression of CapLEA-1 and LTP was found to increase gradually and reach culmination at 48 h after PEG stress (Fig. 4A and C). The gene expression level of Dehydrin 1 reached culmination at 12 h after PEG stress, and then decreased (Fig. 4 B). Instead, RuBisCO small subunit is a PEG repressive gene. Its expression level decreased after PEG stress (Fig. 4 D). However after PEG-treatment, the gene expression level of chlorophyll a/b binding protein slightly decreased from 0 to 24 h but highly increased at 36 h, and then again rapidly decreased (Fig. 4 E). Discussion The qPCR results were in agreement with those of the EST analysis in this study, so the two cDNA libraries could be used to analyze transcript profiles of genes. Water deficit has been found to alter plant gene expression and lead to specific gene induction [3]. In is study, we have observed 36 up-regulated and 56 down-regulated genes in chickpea seedling leaves under dehydration stress. Some of those differentially expressed genes, such as genes encoding LEA proteins, LTP, Cytochrome P450 Monooxygenase, SAMs, etc. have been reported [14,24,26,27]. These genes might be universal drought-responsive genes in the plant kingdom. LEA proteins is generally found to play special roles in protecting cytoplasm from dehydration and in protecting plants by palliating the toxicity produced by the high concentrations of ions, however, their functions related to other aspects remain unknown [18,19]. Plant LTP is involved in a variety of biological processes, such as contribution to formation of an impermeable layer, which impedes water loss, and defence reactions against phytopathogens, and plant adaptation to various environmental conditions. However, it is not clearly understood how LTPs exert their effects and how LTPs are mediated in these processes [18,28]. LEA proteins are generally classified into six groups (families) and the first three groups are the major ones, which are basically localized in cytoplasm and nuclear region. We found that four proteins belonged to the first three groups of LEA proteins were PEG-induced. These four proteins were soybean seed maturation polypeptides (chickpea.1273, belonging to Group 1), Dehydrin 1 (chickpea.0106, belonging to Group 2), CapLEA-1 (chickpea.0097, belonging to Group 3), and CapLEA-2 (chickpea.1214, belonging to Group 3). In MH1, the highest expressed unigene was Dehydrin 1 (chickpea.0106), with 82 clones (Supplementary data 2). Second was nsLTP (chickpea.0131), with 48 clones. While the genes encoding CapLEA-1 (chickpea.0097), CapLEA-2 (chickpea.1214) and soybean seed maturation polypeptides (chickpea.1273) had 30, 20 and 20 clones, respectively. This indicated that under PEG stress, some genes belonging to the groups 2 and 3 LEA proteins had higher expression than those belonging to the group 1 LEA proteins. Based on the IDEG6 results, LTP and LEA proteins (Groups 1, 2 and 3) seemed to be very important in chickpea response to PEG stress. The gene expression level of Dehydrin 1 reached culmination at 12 h after PEG stress and then showed a decrease tendency, while the gene expression level of CapLEA-1 increased continuously (Fig. 4A and B). This suggests that some of the group 2 LEA proteins play significant functions at earlier stage of PEG stress, while some of the group 3 LEA proteins play more important functions at latter stage of PEG stress. The similar result is only found in Loblolly Pine [29]. These results also indicate that LEA proteins and LTP play pivotal roles in drought tolerance of chickpea [18].
In this study, the gene expression level of putative senescenceassociated protein (chickpea.0029) was down-regulated under PEG stress, while the gene expression level of cysteine proteinase (chickpea.0122) was up-regulated. It indicates that senescence-related proteins have different express responses to drought stress [23]. Through IDEG6 analysis, the photosynthesis-related genes, such as genes encoding RuBisCO small subunit and chlorophyll a/b binding proteins, were also regulated under PEG stress (Fig. 4D and E). It suggests that chickpea exhibits a physiological adaptation to drought stress. When chickpea abruptly met drought stress, the photosynthesis was inhibited. After sometime under PEG-treatment, chickpea adapted to the stress and the photosynthesis increased. However, when drought stress was overtime, the photosynthesis was inhibited again. Other studies have revealed that drought, cold, high salinity, and ABA stresses can inhibit photosynthesis [26]. Interestingly, some differentially expressed unigenes have not been reported in other species. For example, chickpea.0620 and chickpea.0627 only have BLASTn annotation, and they were induced under PEG stress. Some other up-regulated unigenes, including chickpea.0007, chickpea.0143, chickpea.1230, and chickpea.0925 (Supplementary data 4), encoded unknown proteins, and they might have specific functions in chickpea specific response to drought stress. In summary, our comparative analysis of ESTs in response to drought stress and finding of some up-regulated genes involved in signal transduction pathway, ABA catabolism, protein degradation, and SAM anabolism, etc., which indicates that multi-processes may play important roles in drought tolerance in chickpea, will contribute to understanding of the underlying molecular basis of chickpea drought tolerance. This will also be helpful for the discovery of the mark genes involved in drought tolerance and crop improvement. Acknowledgments We thank Ph.D. Tianbao Yang (Department of Hortculture/Center For Integrated Biotechnology, Washington State University) for revising the manuscript. We gratefully acknowledge the partial financial support from the 111 Project, from projects supported by National Science and Technology Ministry (2007BAC15B06, 2006BAD09A04, 2006BAD09A08), from Project supported by National Natural Science Foundation of China (30860152), and from Program of Education Department of Xinjiang Autonomous Region (XJEDU2004I17) for this research. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.bbrc.2008.09.030. References [1] L. Xiong, R.G. Wang, G. Mao, J.M. Koczan, Identification of drought tolerance determinants by genetic analysis of root response to drought stress and abscisic acid, Plant Physiol. 142 (2006) 1065–1074. [2] E.A. Bray, Plant responses to water deficit, Trends Plant Sci. 2 (1997) 48– 54. [3] J. Ingram, D. Bartels, The molecular basis of dehydration tolerance in plants, Annu. Rev. Plant Physiol. Plant Mol. Biol. 47 (1996) 377–433. [4] K. Shinozaki, K. Yamaguchi-Shinozaki, Molecular responses to drought and cold stress, Curr. Opin. Biotechnol. 7 (1996) 161–167. [5] A. Campalans, R. Messeguer, A. Goday, M. Pages, Plant responses to drought, from ABA signal transduction events to the action of the induced proteins, Plant Physiol. Biochem. 37 (1999) 327–340. [6] P. Winter, T. Pfaff, S.M. Udupa, B. Hüttel, P.C. Sharma, S. Sahi, R. ArreguinEspinoza, F. Weigand, F.J. Muehlbauer, G. Kahl, Characterization and mapping
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