Comparative Proteomic Analysis of Genotypic Variation in

Article pubs.acs.org/jpr

Comparative Proteomic Analysis of Genotypic Variation in Germination and Early Seedling Growth of Chickpea under Suboptimal Soil−Water Conditions Saeedreza Vessal,†,‡ Kadambot H.M. Siddique,‡ and Craig A. Atkins*,†,‡ †

School of Plant Biology, Faculty of Natural and Agricultural Sciences and ‡Institute of Agriculture, The University of Western Australia, 35 Stirling Highway, Crawley WA 6009, Australia S Supporting Information *

ABSTRACT: Protein expression patterns in imbibed seeds of three cultivars of chickpea (Cicer arietinum L.) with different rates of germination under limiting water supply in soil (>10% water holding capacity) were compared. A large number of soluble proteins expressed earlier and at higher levels in cv Rupali seeds compared to two other genotypes that germinated less rapidly (KH850) or not at all (KJ850). Among the proteins identified were those with chaperone-like functions, including LEA and HSP proteins and proteins involved in metabolism of reactive oxygen species (ROS). Only NAD-malate dehydrogenase was identified as an early, differentially abundant enzyme of the TCA cycle, but in cv Rupali, expression of phospho-enol-pyruvate carboxykinase rose very rapidly to a high level, indicating that an anaplerotic C input to the TCA cycle may have been important. Proteinase inhibitors were more highly expressed in the genotype that did not germinate compared to cv Rupali. Clustering analysis of proteomic data indicated a link between groups of proteins, implying a common regulatory mechanism possibly at the transcriptional level. The chaperone-like proteins and enzymes of ROS homeostasis provide a useful starting point for molecular genetic analysis that may well identify other important genes for the early germination trait. KEYWORDS: chickpea, comparative proteomic, genotypic variation, germination, water content

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among genotypes,5 suggesting that there may be sufficient genetic variation within the species to identify accessions that can germinate under severely limiting soil moisture conditions. Following imbibition, a number of metabolic activities that cease as the seed matures and desiccates resume together with expression of a new suite of proteins and consequent metabolism.6,7 If the ingress of water is adequate, within a few hours seeds enter full metabolic activity with de novo mRNA and protein synthesis completing germination and initiating radicle emergence/elongation.7 Thus both the initial functional molecular components present in dry seeds, such as stored RNA and proteins, together with newly transcribed RNA and expressed proteins are equally important to the germination process and establishment of a seedling. Although gene expression at the transcript level has been used in studies on the response to drought stress,8 expressed sequence tags alone are insufficient without information as to their function.9 Moreover, the level of protein synthesis is not

INTRODUCTION Chickpea (Cicer arietinum L.) is largely cultivated on marginal areas under rain-fed conditions where water availability is the limiting factor for growth and yield. Inadequate soil moisture contributes up to 60% yield loss in the crop, depending on region and climate of the growing area,1 and while limiting moisture at any stage of growth may reduce yield, poor germination and crop establishment2 due to suboptimal seedbed moisture is a major contributor.1 Imbibition is the most critical requirement for germination, and factors such as the size and shape of seeds influence successful early establishment.3 These characters are maternally controlled by features of the seed coat that physically restrict water uptake and may modify nutrient supply to the growing embryo and cotyledons.4 Considerable variation in seed size among chickpea genotypes has been recorded,5 and although the soil moisture requirement for germination is well below field capacity, differences in germination rate among genotypes have not been well established.1 However, in a recent glasshouse study the percentage of emerged chickpea seedlings under soil moisture contents of 25% field capacity varied © 2012 American Chemical Society

Received: May 3, 2012 Published: July 6, 2012 4289

dx.doi.org/10.1021/pr300415w | J. Proteome Res. 2012, 11, 4289−4307

Journal of Proteome Research

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6.25% WHC was used throughout this study. At 0, 48, and 72 h after sowing, germinated seeds and seedlings were removed from the assay containers and brushed clean of adhering sand grains, and the length of radicle and epicotyl (if any) measured. The seeds, excluding emergent organs, were immediately extracted.

always predictable from mRNA level, most likely because of post-translational modifications and widespread changes in protein turnover10 or effects of micro RNAs and other regulators that might modulate gene expression.11 For these reasons proteomic analysis provides a more accurate assessment of plasticity of gene expression and biochemical processes associated with the physiological state of tissues and cells. In recent years, plant responses to dehydration or drought have been investigated using proteomic analyses in some important crops such as maize,12 soybean,13 and chickpea,9 and expression of a number of proteins (associated with metabolism, antioxidant enzymes, signaling, cell defense, and rescue) were shown to be directly or indirectly involved in the plant’s response. However, these studies have been restricted to relatively mature plants or seedling organs at some later point of their life cycle rather than the initial stages of seed germination. Proteomics has also been used to successfully investigate aspects of germination, mainly in the model plant Arabidopsis thaliana, including the role of gibberellins,14 mechanisms of seed dormancy,15 the effect of salicylic acid,16 and of seed priming.17 Studies aimed at determining genotypic differences in the rate of imbibition and initiation of germination require an assay system that establishes and maintains constant soil−water contents, preferably under conditions that relate to those in soil. Several attempts have been made to compare germination differences among genotypes using osmotic solutes such as PEG and NaCl to generate variation in water potential, but results from these studies cannot be easily extrapolated to soil conditions.1 Recently, a simple yet highly reproducible assay system that provides conditions similar to soil and permits the establishment of uniform low levels of moisture has been developed for germination studies in chickpea.18 The methodology was assessed at very low water supply (as low as 5% field capacity) and revealed clear genotypic differences in the rate and extent of germination.18 The present study exploited this assay system to provide tissue from germinating seeds that could be used to assess differences in gene expression among genotypes with the aim of identifying mechanisms responsible for more rapid seedling emergence under limiting soil moisture. To date, there has been no comparative proteomic analysis dealing with germination under suboptimal water supply with particular emphasis on genotypic variation for this trait. Potentially, a detailed protein profile could provide a useful foundation for further investigation of mechanisms involved in the initiation of germination and particularly of likely metabolic events that permit rapid germination.

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Protein Extraction and Two-Dimensional Electrophoresis (2-DE)

Soluble proteins were extracted from 2.5 g imbibed or dry tissue from a pooled sample of five seeds, excluding the radicle, using a method based on that developed by Goggin et al.19 After 30 min incubation at room temperature with gentle rocking, the extract was centrifuged for 10 min at 12000g. Protein recovered in the supernatant was precipitated in methanol chilled to −80 °C, incubated overnight at −80 °C, and then centrifuged for 30 min at 10000g. The pellet was airdried, then resuspended in minimal iso-electric focusing (IEF) sample buffer (8 M urea, 2% [w/v] CHAPS, 60 mM DTT, 2% [v/v] IPG buffer) for 10 min with rocking, and centrifuged at 12000g for 30 min. The concentration of protein in the supernatant was determined by Peterson-Lowry assay20 using crystalline BSA as standard. The recovery of soluble proteins using this extraction buffer has been successful and highly reproducible for seed of other legume species.19 IEF was carried out with samples containing 500 μg protein in 250 μL 2-DE rehydration buffer on 13 cm IPG gel strips (GE Healthcare Bio-Science). This protein sample (250 μL) was loaded onto IPG strips (13 cm) with nonlinear pH 3−11 or linear pH 4−7 gradients and electro-focused in a Multiphor II Electrophoresis Unit (Amersham Biosciences) under the following conditions: 300 V for 1 min, gradient from 300 V to 3500 over 1.5 h, and constant 3500 V for 4 h (total = 17 kVh). The focused IPG strips were reduced in 10 mL equilibrium buffer (50 mM Tris pH 8.8, 6 M urea, 30% [v/ v] glycerol, 2% [w/v] SDS, 0.002% [w/v] bromophenol blue) with 65 mM DTT, followed by alkylation using the same buffer but containing 135 mM iodoacetamide for 15 min at each step separately. For SDS-PAGE, the developed strips were placed on gels containing 12.5% [w/v] polyacrylamide. The initial analysis of all 2-DE gels indicated that there was poor resolution of protein spots toward the acidic end of the gradient in the pH 3−11 gels. This area comprised approximately 90% of all protein spots with nearly the same pattern recorded for each genotype (Supplementary Figure 1, left panel). The proteins were more clearly resolved on pH 4−7 gels (Supplementary Figure 1, right), and although around 40 and 20 kDa there was some crowding and a number of proteins were not completely resolved, overall the number of spots resolved was suitable for further analysis. Some recent proteomic studies of mature chickpea tissues have also used narrow pH 4−7 IPG strips to successfully resolve a greater proportion of proteins with fewer overlapping spots.9 Only the narrow range strips were used in this study.

MATERIALS AND METHODS

Plant Material and Germination Conditions

Seed from three chickpea (Cicer arietinum L.) genotypes (cv Rupali, KH850, and KJ850) characterized in a previous study18 and found to have different germination rates were used with the sand medium germination assay system developed earlier.18 For each genotype, four seeds of uniform size were planted equidistant from one another, with their embryonic axis facing downward, exactly 10 mm beneath the soil surface. There were four replicate containers per treatment arranged in a randomized block design and maintained at 18.5 ± 0.4 °C in the dark. Preliminary experiments indicated that genotypic differences in germination rate were expressed using levels of water below 10% water holding capacity (WHC). Accordingly

Gel Staining, Image Acquisition, and Data Analysis

Protein spots on the SDS-PAGE gel were stained overnight by the Blue Silver technique21 followed by destaining overnight in 2% [v/v] acetic acid. The gels were then digitalized at 800 dpi resolution using a GS-800 Calibrated Densitometer (Bio-Rad). PDQuest Software (Version 8.0.1, Bio-Rad) was used to analyze the images and create master gel images following the user’s manual (PDQuest 8.0 2D Image Analysis, Bio-Rad) from three replicate gels for individual genotypes at each time points 4290



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after sowing. Values for experimental molecular mass and isoelectric point (pI) were determined using standard molecular mass markers with digitized images generated by the software. To ensure the requisite quality of spots, several parameters were assessed manually. Only spots that were recorded on at least two replicates with consistent shape and size were selected. The average quantity of these “high quality” spots was used on the standard gels in further analyses. The correlation coefficients between replicate gels were at least 0.8 to make a first level match set. Then the second level match sets for each time course were developed to compare genotypes over the time courses.

proteins identified from a single peptide. An extra built-in submission for BLAST search was also performed to further verify the matches at www.ncbi.nlm.nih.gov/BLAST/. Protein Classification, Clustering Analysis, and Statistical Analysis

The function of identified proteins was sought by comparison against standard protein databases at InterPro, Pfam, and UniProt to minimize any redundancy in functional assignment. Additional literature searches, if available, were also used to divide the proteins into different classes based on function and likely metabolic role. SOTA (Self-Organizing Tree Algorithm) clustering of the expression data was conducted on log2 transformed fold abundance values across genotype × time points using MultiExperimental Viewer (MeV) version 4.5.1 (The Institute for Genomic Research-TIGR). The parameter for the hierarchical clustering was the Pearson correlation assessed as metric distance with maximum cycle of 10 and maximum cell diversity of 0.8.9 In order to differentiate those spots showing statistically different abundance, the constitutional Student’s t test in PDQuest software at p < 0.05 was initially used with each match set. This analysis aimed at significant expression changes for a given spot at a specific time point (0, 48, and 72 h). The results generated by the software were further validated by extracting the intensity of every spot and subjecting it to additional statistical analysis for all time points as follows:23 the intensity of each spot was normalized further by dividing the average density of the given spot on the sum of the spot across time courses and then multiplied by 100 to determine normalized spot density (density %).24 This transformation enabled cross point comparisons and produced more smooth and normalized data for parametric statistical testing. A twoway ANOVA as a factorial design was then carried out using GenStat software (version 12.0) to compare the effect of time and genotype and the interaction between them. For convenience, the results of this statistical analysis have been displayed as mean ± SE for each spot with p-value for each factor (genotype and time) and their interaction in the data sets.

Protein Identification

Protein spots were identified by MALDI-TOF/TOF MS/MS according to the standard techniques described by Bringans et al.22 Candidate Coomassie-stained gel spots were excised and, after initial washing with water, destained by washing three times for 45 min with 25 mM ammonium bicarbonate in acetonitrile (ACN)/water (50:50 v/v) and vacuum-dried. Each destained excised gel piece was subsequently digested with 10 μL trypsin solution (12.5 μg mL−1 trypsin [Roche, Cat. No. 11418475001], 25 mM ammonium bicarbonate) and incubated overnight at 37 °C. Extraction of the resultant peptides was achieved by two 20-min incubations of 10−20 μL ACN with 1% (w/v) trifluoroacetic acid (TFA); the volume depended on the size of the gel pieces. Large pieces were subjected to three extractions, which were pooled, then dried by rotary evaporation, and stored at −20 °C before MS analysis. Extracted dried peptides were reconstituted with 2 μL ACN/ water (30:70 v/v) and further diluted 1:10 with matrix solution (containing 10 mg mL−1 α-cyano-4-hydroxycinnamic acid [CHCA]). A total of 0.6 μL reconstituted peptide sample was first spotted in duplicate on 384-well Opti-TOF plates, followed by 0.6 μL matrix solution. The spotted samples were analyzed using MALDI-TOF/TOF MS/MS on a 4800 MALDI-TOF/TOF analyzer (Applied Biosystems, MDS SCIEX). The MS/MS spectra were analyzed automatically with DeNovo Explorer version 3.6 (Applied Biosystems). All derived peptide sequences in the peak list were searched against LudwigNR (release date April 13, 2009; 8,777,915 sequences and 3,084,943,043 residues) and MSDB database using Mascot search engine version 2.2.1 (Matrix Science) for protein identification. A homology search was carried out as the chickpea genome has not been sequenced. The MS/MS ion search parameters were as follows: Taxonomy, Viridiplantae (Green Plant, 630487 sequences); Enzyme, Trypsin; Fixed modification of cysteine, Carbamidomethylation (C); Allowance missed cleavage, 1; Variable modification, Oxidation (M); Peptide and MS/MS tolerance, 0.6 kD each; Mass value, Monoisotopic. Based on the size of the database and p < 0.05, individual ion scores more than 43 (threshold) were indicated as having identity or extensive homology by the Mascot algorithm. Final protein scores were obtained from ion scores as a non-probabilistic basis for ranking protein hits. In case the peptides matched with different members of a family or the identified protein had various accession numbers and names, the best match was chosen as the identity by considering other parameters, such as higher Mascot score, expression pattern on 2-D gel, and putative function of the protein. Details for analysis of spectra and also peptide view with MS/MS fragmentation and its corresponding table of matched fragment ions is in Supplementary Data (Table 1) specifically for those

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RESULTS As noted previously,18 cv Rupali performed better over a wide range of reduced water availability in sand compared with other genotypes. KJ850, with low pre-emergence seedling growth and failure to germinate at very low WHC, was the most sensitive genotype to water supply in this range (Supplementary Figure 2), while KH850 was intermediate in response to limited water (data not shown). The critical level of water in the sand medium was 6.25% WHC, and this was used throughout the proteomic study. Delayed germination in KJ850 at 48 and 72 h coincided with radicle emergence in cv Rupali and KH850, respectively (Figure 1), providing comparable phenology of germination events for further comparison at the proteome level. Analysis of the Soluble Proteome during Germination with Limited Water Supply

On average, 1600 resolved spots were initially detected on each individual 2-DE gel with some obvious changes within genotypes or time of imbibition (Supplementary Figures 1 and 2). For each genotype at each specific time of imbibition (0, 24, and 72 h), three replicate gels derived from separate 4291



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312 were confirmed to be significantly different in abundance or presence. To compare high quality spots across all time points, normalization was first achieved using the density of constantly expressed protein spots across all genotypes and time points (identified as i and ii in Figure 2 and also in Figure 5). Filtered spot intensities were then extracted using normalized master gels (Figure 2) and transferred to a data matrix consisting of 760 spots (Table 1) for further statistical analysis by two-way ANOVA across all time points to show the interaction between genotypes and time of imbibition (Table 2). This analysis found 630 responsive (significantly differentially abundant) protein spots (RPs) had more than 1.5-fold difference in expression in at least one combination of genotype × time across the nine treatments. Identification of Differentially Expressed Proteins

MS/MS spectral analysis of the RPs identified 442 proteins; 29% of analyzed RPs did not significantly match to any peptides in the protein database (187 spots, Table 1). Around 75% of the significant matches found in the protein database were identified as different isoforms of seed storage proteins from a number of legume species including chickpea (Supplementary Table 1). Legumin was the most abundant, being identified in more than 80% of the peptides sequenced. Other most abundant peptides were identified as provicilin followed by convicilin, which like legumin were found as a number of spots across gels (Supplementary Table 1). Spots identified as storage proteins were excluded from further analysis. Thus a total of 92 proteins, positively identified with significant hits in the databases used, were further analyzed for their expression profile across genotype and time of imbibition. On the basis of statistical analysis of their abundance, 16 protein spots were significantly different at 0 h imbibition, 40 spots at 48 h, and 36 spots at 72 h after initiating imbibition (Table 1). Each set of the spots is shown separately on its corresponding advanced in silico match set image for 0, 48, and 72 h in Figure 2A−C. The number of proteins that appeared to be different in abundance was greater at 48 h among genotypes (Table 1, Figure 2B), followed by 72 h (Figure 2C). These time points coincided with radicle emergence in cv Rupali at 48 h with further radicle elongation by 72 h. The radicle tip emerged in KH850 at 72 h (Figure 1), while no radicle emerged in KJ850 during the time course.

Figure 1. Pictorial demonstration of varied germination performance at key time points after imbibition coincident with morphological response of radicle emergence and further elongation together with corresponding typical protein profiles for each genotype. At the 0 h time point, only dry seeds were used to extract protein without sowing in the sand, but for the other two time points each genotype was directly exposed to 6.25% WHC in the sand medium.

tissue extractions were developed to obtain a combination of nine treatments of genotype × time as a factorial design. The replicate gel images were then digitally combined with the software to construct a standard master gel for each time point separately. By manually removing artifacts (specks or debris that were stained) only “high quality” spots that passed several strict criteria (e.g., showing the same relationship for at least two replicates, at least 1.5-fold change, and correlation of more than 80% for replicate gels) were selected to determine spot density and permit further analysis (Table 1). For example, 844 spots were found by built-in statistical analysis to be differentially represented among three genotypes at 48 h imbibition, but through rigorous manual checking of spots only

Table 1. Frequency of Differentially Expressed Protein Spots Detected from Image Analysis of Gels among Three Genotypes (cv Rupali, KH850, and KJ850) at Three Time Points of Imbibitiona identified spots imbibition time course (h)

initial differentially expressed protein spotsb

high quality spotsc

MS/MS analyzed spotsd

storage proteinse

functional proteinsf

nonsignificant hitsg

0 48 72 total

709 844 640 2193

208 312 240 760

172 246 211 629

101 128 117 346

17 41 38 96

54 77 56 187

a These spots were subsequently used for MALDI TOF/TOF mass spectrometric analysis and protein identification. bProtein spots were judged to be different in expression by built-in statistical analysis in the PDQuest software. cThe number of protein spots were established to be differentially expressed after manually checking the characteristics of each spot and removing artifacts (specks, debris, etc. stained by blue silver) or inconsistent (absence on at least two replicates) spots within replicate gels. dIncludes those spots that could be excised from gels and used for further downstream MS/MS identification. eRefers to the proteins identified as seed storage proteins from the database analysis. fAll defined non-storage proteins were considered as functional proteins. Additionally unknown proteins and putative uncharacterized protein were grouped in this category. gThe MS/MS spectra resulting from these protein spots could not be matched with any peptides in the protein database.

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Figure 2. continued

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Figure 2. Advanced match set images with protein spots detected on the basis of a set of defined criteria for validation of each spot using PDQuest software from all genotypes for (A) 0, (B) 48 and (C) 72 h imbibition under limited water availability. These match sets were developed in silico by analysis of nine 2-D gels for each time point as shown in Supplemental Figure 3. Based on this analysis all consistent spots on replicate gels for each genotype were computationally merged to create this higher level match image after a set of defined criteria. The filtered images for each genotype were compared with this image to reveal the difference between genotypes. Many of the spots (Tables 1 and 2) belonged to storage proteins; these are not marked on the gel images to avoid complexity in tracking non-storage proteins. Spot numbers with red refer to those listed in Table 2. Spots designated as i and ii, indicated with black arrows, identify two unchanged spots in all three genotypes within all time points used to normalize the intensity of the spots for cross-comparisons between time points.

transduction, nucleotide sugar metabolism and unknown proteins) on the basis of their putative function (Table 2 and Figure 4). Functions of these proteins were mostly annotated using standard database information in Interpro, Pfam, and UniProt with particular emphasis on the features related to seed biology, germination, and water stress. Other published reports related to each family of proteins were also considered in assigning putative function. A significant group (25.3%) comprised proteins implicated in defense, stress response, and cellular detoxification, which were markedly influenced by limited water availability during germination within the three genotypes (Figure 4). A number of important proteins in this group are members of the heat shock proteins (HSPs) and late embryogenesis abundant proteins (LEAs), superoxide dismutase (Cu−Zn), glutathione peroxidase, 1-cys peroxiredoxin, dehydrin-1, and a number of pathogenesis related proteins (Table 2). Some of these proteins were over-represented in terms of abundance level in cv Rupali at 0 h and/or especially after 48 h imbibition, compared with the other two genotypes. The second largest group (19% designated as nucleotide sugar metabolism, Table 2) corresponded to the cupin domain associated with the enzyme RmlC (dTDP-4-dehydrorhamnose 3,5-epimerase). The protein spots containing this domain were widely distributed on the gel. Four spots with three unique proteins (4.2%) were involved in respiration and specifically in TCA cycle metabolism. Two of them, phosphoenolpyruvate carboxykinase (spot 736), and

Two-way ANOVA analysis using the normalized values of the expression profile for 92 protein spots showed that these changed significantly in abundance either across genotypes or time points with at least more than 1.5-fold differences (Table 2). This analysis also permitted the interaction between time and genotype to be assessed. Interestingly, 11 spots (for example, spots 201, 993, 830, 994, 630, 730 and 504) were absent or expressed at very low levels on gels for cv Rupali, compared with six spots for KH850 and only one (spot 200) for KJ850; the latter spot was also common with KH850. The Venn analysis diagram (Figure 3) of these differentially abundant protein spots (Table 2) within genotypes during the whole imbibition time indicated significant differences between and overlap among them. The number of more abundant spots in cv Rupali was some 25% greater in comparison with KH850 and KJ850 (39 versus 30 or 31 spots, Figure 3A). Of these spots, 10 were common across the three genotypes and five increased in all genotypes. Also the number of unique spots showing increases in abundance was 75% greater in cv Rupali than other genotypes (13 versus 8 spots), while the total number of spots that decreased in abundance across genotypes was fewer than half this number (Figure 3B). Functional Cataloguing of Proteins

All 92 identified proteins were classified into nine major categories (metabolism, respiration and energy, defense, stress and detoxification as well as allergen/nutrient reserve, protein processing, nucleic acid (DNA, RNA) processing, signaling and 4294



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Table 2. Differentially Abundant Proteins Identified by MALDI TOF/TOF Analysis with Details of Protein Identities and Their Relative Expression Density Profile during Germination over Various Times of Imbibition in Three Genotypes under Limiting Soil Water Content (6.25% WHC)

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Table 2. continued

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Table 2. continued

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Table 2. continued

a Numbers correspond to the numbers on the specific spots on the gels in Figure 4A−C . bAccession numbers correspond to Uniprot entries, TrEMBL. cSequence coverage percentage. dNP represents the number of peptides matched for identification of the proteins in the database. e Theoretical molecular weight (Mr) and pI, predicted from MS/MS analysis. fExperimental molecular weight (Mr) and pI were estimated using standard protein marker and automatic assignment by image analyzer (PDQuest) software. gThe average value of relative transformed density of each spot across nine treatments consist of three genotypes (cv Rupali, KH850, and KJ850, as G on histograms) at three different imbibition times (0, 48, and 72 h after sowing, as T on histograms). The interaction is designated as T×G. The small bars on each expression column represent the SE (N = 3), while * indicates p < 0.05, ** p < 0.01, and *** p < 0.001. The values (relative density %) for the histogram was normalized by calculating the density ratio of each individual spot over the sum of the spot density across genotype and time courses and their replicates. Accordingly, sum of the values in each graph is 100%.

not detected from cv Rupali but were significantly over represented in the other two genotypes, was the nucleic acid processing class (7.4%, Figure 4). This group included proteins such Snase (staphylococcal nuclease), SWI2/SNF2-like, RNA polymerase II and to some extent maturase 2 (spots 730, 201, 993, and 440, respectively), which were absent or had very low abundance at each time point in cv Rupali. In contrast, spot 720 and 23, corresponding to “reverse transcriptase” and “THO complex subunit1transcription elongation factor”, were significantly enriched in cv Rupali compared with other genotypes (Table 2). Generally, the experimental Mr values assigned using the 2DE gel position varied from theoretical values for a number of the differentially expressed proteins in Table 2. In many cases, the experimental sizes were less than theoretical values. This was most evident for cupin RmlC-type proteins and Allergen Len c, which were found in many spots on the gels. However the experimental Mr was closer to the theoretical values for most of the defense, stress, and detoxification related proteins (Table 2).

Figure 3. Venn diagram analysis of protein spots differentially expressed among the three genotypes (cv Rupali shown as purple, KH850 shown as green, and KJ850 shown as yellow) in response to limited water availability 72 h imbibition. Protein spots showing increased abundance (A), decreased abundance (B), or mixed changes in abundance (C). The number of proteins in each category is shown in each section with the numbers that overlap across genotypes indicated.

Clustering the Abundance of Differentially Expressed Proteins

Self-organizing tree algorithm (SOTA) analysis, which was achieved for 92 RPs, enabled the resolved spots to be classified into different clusters based on their expression profile (Figure 6). To perform such an analysis, the extracted expression data were changed to “-fold” expression against their corresponding expression value in cv Rupali (0 h) and then transformed to log2

mitochondrial NAD-dependent malate dehydrogenase (spot 879), increased significantly in cv Rupali but not in KH850 and KJ850. The other functional class, in which some proteins were

Figure 4. Distribution of 96 identified proteins into nine major functional classes. These proteins were classified according to assignment of their putative function(s) by searching in InterPro, Pfam, or associated literature. 4298



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Figure 5. Magnified genotype- and time-dependent display for some differentially expressed protein spots derived from the corresponding 2-DE gels. Numbers on the top and left refer to the time of imbibition (h) and spot numbers, respectively. Spots I and II are non-normalized views of unchanged spots shown in Figure 4A−C.

to normalize the expression and reduce the range of data sets so that they could be more easily presented. The analysis revealed 11 distinct clusters based on the protein expression patterns for three genotypes over the time of imbibition (Figure 6). A significant fraction of the proteins (just over 90%) appeared only in five clusters (cluster 1, 2, 7, 9, and 11, Figure 6, panels) with at least six proteins in each. In the most abundant group, the overall average pattern increased from cv Rupali to KH850 and further to KJ850. Around 55% of these coexpressed proteins belonged to unknown, miscellaneous, and DNA/RNA processing groups. A pattern of increased abundance was observed more or less in all genotypes with more than 40% of proteins involved in signaling and transduction and defense, stress, and detoxification. The average protein expression values in clusters 8, 10, and 11 were considerably greater in cv Rupali

with respect to the other genotypes over time in which a large percentage of DNA/RNA processing, protein processing and defense, stress, and detoxification functional classes were found in cluster 11 (Figure 6). The other important outcome of SOTA was to cluster the overall performance of each genotype according to their expression pattern for all 92 RPs over time (Figure 6, horizontal dendrogram). On the basis of this view, two main clusters were recognized; cv Rupali performance was far different and was separated into one cluster, while the data for KH850 and KJ850 in terms of overall expression patterns of proteins at all time points were classified into the other main cluster. Despite the fact that KH850 and KJ850 were both clustered together and behaved similarly at 0 and 72 h, the branching position (average linkage) for KH850 at 48 h was similar to cv Rupali. 4299



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Figure 6. Self-organizing tree algorithm (SOTA) clustering analysis with heat map representation of the expression profile for germination responsive proteins under limited water availability with particular emphasis on sample clustering (genotype over imbibition time) (top). All 92 proteins were also classified into 11 clusters based on their expression pattern (panels below) along with corresponding functional cataloguing for five selected clusters (pie diagrams). Numbers in the column at the bottom of the cluster are the spot numbers presented in Figure 3 and Table 2. The cluster analysis was conducted on log2 transformed data of fold change ratio. Blue triangles cover the range of two main clusters for in all genotypes. Gray lines show the specific trends for each protein in the cluster with the average trend for the cluster presented in pink. Numbers in black and N (in below) indicate number of proteins present in each specific cluster, and those in red signify the cluster number. P stands for proteins. Time of imbibition (0, 48, and 72 h) and genotypes (cv Rupali, KH850, and KJ850) have been defined for cluster 9, for example. RU, cv Rupali; KJ, KJ850; and KH, KH850.

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DISCUSSION

influenced early events following imbibition is not known, but N supply particularly might become critical after radicle emergence and during later seedling growth. Ψb reflects water status of the seed for initiation of germination, and while it is likely to be determined by endogenous factors, environmental conditions during seed development may also be involved;25 in the current study, therefore, seeds of all genotypes were grown in the same suasion and except for few days difference, each genotype was harvested at the same time, stored under the same conditions, and used after the same period of postharvest

The contrasting germination and subsequent growth among genotypes, especially between cv Rupali and KJ850, indicated that these two genotypes should have different Ψb requirements to complete germination as there was no difference in their rate and extent of imbibition under conditions of adequate soil− water.18 Their dry seed differed in elemental composition, particularly in N and to a lesser degree in P and some microelements.18 Whether or not these nutrient differences 4300



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function during germination under water limitation compared to the slower germinating genotypes. Functions suggested for Group 2 LEA proteins and dehydrins (spot 920 and 930) are diverse, depending on abiotic stressors.28 They have been described in many tissues but particularly in seeds at late stages of embryogenesis,30 and not surprisingly, dehydrin 1 was expressed in dry seed of the three genotypes. However, the highest level was in KJ850, the genotype unable to germinate under severe water limitation; in cv Rupali the abundance was quite stable but significantly lower. This might imply no role for dehydrin 1 in the seed’s ability to complete germination under severe water limitation.31 The prominent protein (spot 511) identified as Group 5 LEA increased significantly, by many fold, in cv Rupali upon imbibition compared with much lower increases in abundance in the other genotypes (Table 2 and Figure 5). This protein was not detected in dry seeds, suggesting de novo synthesis during early stages of germination. A member of Group 5 LEA proteins (MtPM25) prevents aggregation of proteins during drought stress and is prominent in the radicle proteome under conditions of desiccation.32 In the present study, a high level of expression for this LEA group 5 coincided with radicle emergence in cv Rupali at 48 h and, after further radicle elongation, 72 h after imbibition (Figure 1). Abundance in the other two chickpea genotypes increased but remained much lower following imbibition, implying this protein might contribute to the rapid germination trait in cv Rupali. The other large group of potentially protective polypeptides identified belonged to the HSP family and included Hsp17.8, Hsp17.5, sHSP, Hsp20/DnaJ-Rel, and Hsp40/DnaJ-Rel (Table 2). These proteins were originally considered to be ATPindependent chaperones, expressed ubiquitously under conditions of heat stress in cells.33. However, diverse roles in cellular functions as protective chaperones have been suggested to include transcription, translation, cell signaling, and even secondary metabolism or facilitation of cell to cell trafficking through plasmodesmata.34 HSPs are very effective in binding to denatured/unfolded proteins to re-establish functional conformation, and in developing seeds they have a protective role in desiccation tolerance. Basically, the specific member of HSPs, identified as 17.5 kDa class I HSP (spot 931), was not detected in dry seeds but was expressed later after imbibition at a high level, especially in KJ850 (Table 2). While this HSP might be part of the response to water limitation its abundance was in fact lower in cv Rupali. Similarly, Hsp17.6 (spot 700) showed an increasing expression profile in all genotypes from dry seed to 72 h after imbibition. While these two HSPs may function in the stress response to limiting water supply, their broad expression suggests a role in providing normal cellular function.33 Small heat shock protein sHSP (spot 540) and Hsp20 (spot 530) were both induced and remained at higher levels in cv Rupali compared with KH850 or KJ850 either after radicle emergence (spot 540) or from the outset (Figures 1 and 5 and Table 2). Their role is also likely to be protective for a range of cellular functions initiated or restored during germination.33,34 Hsp40/DnaJ-Rel (spots 351, 47,1 and 651) is a cochaperone of the Hsp70 complex and regulates the activity of Hsp70 ATPase. This association functions in a number of cellular processes such as proper polypeptide folding, improvement of misfolded proteins, assembly of protein complexes or control of other regulatory proteins.35 There was a progressive increase in abundance of two corresponding protein spots (351 and 471) related to Hsp40

(1 month). A few candidate genes have been proposed that might initiate germination and play a role in the establishment of Ψb threshold.26 However, detailed biochemical and potential molecular mechanism(s) by which Ψb can be regulated or modulated are yet to be identified and analyzed at the level of gene expression. As seeds mature they become progressively dehydrated, with metabolic activity including protein synthesis exposed to severe water limitation. Under these conditions, proteins that protect essential enzymes from denaturation are expressed and protect the “metabolic” complement that is suddenly rehydrated following imbibition.6 The rate at which hydrolysis of reserves is activated is likely to limit overall metabolism, and this alone may determine differences in germination rate. Because of the significant hydraulic pressures that accompany the ingress of water, cell membranes and membrane-bound organelles, such as mitochondria, are thought to be severely stressed requiring repair and reactivation so that respiration and ATP synthesis can resume.27 Incoming water would contain dissolved O2, and it is equally likely that reactive oxygen species (ROS) would be generated from a series of rapidly activated reduction reactions so that regulation and metabolism of ROS are features of the newly activated metabolic pathways. These considerations provide a basis for predicting what sorts of proteins might be reactivated or synthesized de novo in the period immediately following imbibition. Excluding the storage proteins the expression profile of the remainder of differentially abundant proteins (92 identified protein spots, Table 2) were analyzed with particular emphasis on their putative function. A number of enzyme proteins involved in metabolism, respiration, stress response ,and cellular detoxification as well as protein and nucleic acid processing were successfully identified through peptide sequence analysis using MS/MS. Some of these proteins showed a consistent pattern of enhanced expression following imbibition (Figure 3 and Table 2), suggesting they were associated with the rapid germination trait shown by cv Rupali. Chaperone-Type Proteins and the Restoration of Metabolism

A clear finding of the study was identification of a number of chaperone-type proteins, including late embryogenesis abundant (LEA) proteins and heat shock proteins (HSP), as being differentially abundant with different patterns of expression across the genotypes. Almost all members of the LEA proteins from Groups 1−6,28,29 but not Group 4, plus some other putative members of the family were identified. They comprised Group 1 (spot 922), Group 2 (spots 920 and 930), Group 3 (spot 780), Group 5 (spot 511), Group 6 (spot 64), seed maturation protein (spots 150 and 52), and dehyrin 1 (spot 850). This hydrophilic protein family is involved in responses to various abiotic stresses, especially dehydration, salinity, and low temperature, whether constitutively programmed, as in seed maturation, or as a consequence of environmental changes.28 Expression of Group 1 LEA gradually increased up to 72 h following imbibition for cv Rupali, consistent with de novo synthesis in response to water stress (Table 2). In the other two genotypes, the protein was abundant in dry seed but not detectable 72 h after imbibition. These proteins are believed to protect cell membranes from damage or prevent protein aggregation during water stress,29 and it seems likely that cv Rupali had an advantage in maintaining cell integrity and 4301



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that was more consistent in cv Rupali than in KJ850. Taken together, almost all identified members of the HSP family showed relatively high abundance or a more consistent expression pattern in cv Rupali. It seems that as a group of proteins the HSPs provide conditions that permit the genotype to cope better with the stress of germination under severe water limitation.

imbibition there was a very high abundance of MDH compared to the other genotypes, implying an increased respiration rate to meet the high demand of energy for the rapidly growing emergent radicle.40 This indicates a particular importance of the enzyme for heterotrophic growing organs such as the root, and this may well apply to the emerging radicle in the germinating chickpea seed. Taken together, the enhanced levels of MDH and PEPCK indicate the importance of C4 carboxylic acid metabolism in promoting rapid germination.

Proteins Involved in Metabolism

Anthranilate synthase (AS) was identified as spot 250, which in cv Rupali had an increased abundance, not only in dry seed but during germination. In contrast, the two other genotypes had less abundance during germination. AS is necessary for biosynthesis of the essential amino acid tryptophan (Trp) through the shikimate pathway.36 This pathway also involves production of some defensive secondary metabolites in plants including alkaloids and isoflavonoids as part of the broad response to biotic and abiotic stresses.36 Qb-SNARE protein (spot 360) increased exponentially from a low abundance in dry seed by nearly 20-fold after 48 h and up to 50-fold after 72 h imbibition in cv Rupali. Although the expression profile showed a several fold enhancement at 48 h in other genotypes, expression subsequently decreased, particularly in KJ850 (not germinated genotype). SNARE proteins are a large superfamily, members of which form part of a key complex responsible for molecular trafficking between intracellular compartments.37 This is likely an important feature of the restoration of metabolism and growth during early germination, and promotion of these events might enhance germination rate. Two differentially abundant protein spots (770 and 736) were identified as phosphoenolpyruvate carboxykinase (PEPCK). Separation of two different spots is consistent with the native protein being readily cleaved during isolation forming lower molecular mass polypeptides.38 The abundance of PEPCK (spot 736) increased significantly at 48 h imbibition in cv Rupali, but there was no significant increase in expression of the enzyme in other genotypes (Figure 6 and Table 2). Differential abundance of PEPCK at spot 770 was less clear. PEPCK activity could provide an anaplerotic 4C input to the TCA cycle through carboxylation of PEP and, in the catabolic direction, provides intermediates for gluconeogenesis. During germination, TCA cycle activity would be the major and most effective source of reductant (NADH) and, as a consequence, ATP synthesis in mitochondrial respiration. However, TCA cycle intermediates also provide C4 and C5 acids for the synthesis of a number of amino acids and other metabolites requiring an anaplerotic C input to the cycle. PEPCK is considered a necessary component in hypocotyl elongation and early seedling growth in Arabidopsis due to its role in gluconeogenesis providing additional sugars via oil reserve breakdown.39 While the level of storage lipid in chickpea seed is relatively low (2.5-fold lower in cv Rupali compared to the other genotypes at corresponding time points (Table 2), suggesting a role in slowing or inhibiting germination. CaM is known to be involved in H2O2 signal transduction pathways, and crosstalk between these two components plays a critical role in ABA signaling and relevant antioxidant defense in the whole plant.49 In seeds, increased ABA levels prolong dormancy and delay germination25such that elevated expression of CaM in KH850 and KJ850 compared to cv Rupali may indicate increased levels of H2O2 (beyond the level required for signaling) and/or ABA, which cause delay or inhibition of germination in these two genotypes. In contrast, calreticulin (CRT; Table 2 and Figure 6) increased exponentially from an insignificant level in dry seed to more than 20-fold in cv Rupali with a rather similar pattern in KH850. CRT has a multifunctional role50 in both calcium signaling and/or as a chaperonin-like component in protein folding, modification, and assembly under normal50 and stressed conditions.51 Such activity is likely to be significant in germination, and given the pattern of CRT abundance in cv Rupali, this protein might promote germination under water stress. Additional Proteins Associated with Germination

A pathogenesis related (PR) protein (spot 528) was not detected in dry seeds but was more highly expressed in cv Rupali following imbibition compared with the other two genotypes. Interestingly, transgenic plants of Brassica napus overexpressing pea PR genes have been shown to germinate and develop more effectively under saline conditions,52 but the exact PR function during germination is yet to be defined. Another interesting protein induced significantly and differentially expressed across genotypes was a serine proteinase inhibitor (spot 921). This protein was not detected in dry seeds but was expressed up to 20-fold more during imbibition in the non-germinating genotype (KJ850) when compared with cv Rupali. Proteinase inhibitors (PIs) regulate proteolytic function

Clustering Analysis

SOTA clustering analysis provided an integrated picture of protein abundance data presented in Table 2 permitting comparison of differential protein expression following imbibition with proteomic analysis of chickpea plants during dehydration9 or salinity stress.24 In a sense, severely decreased water availability in an imbibed seed mimics plant drought stress and protection against oxidative stress may be a common feature. Induction of cell defense through expression of proteins identified above reflects functioning of an overriding mecha4303



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Figure 7. A proposed scheme for the essential role of ROS and chaperone proteins in rapid germination of chickpea under severe limitation of water availability. Only some of the chaperone proteins identified and those with a possible role in ROS and downstream pathways are shown using their abundance profile. The level corresponding to dry seed and 48 and 72 h imbibition are shown in red, light blue, and dark blue for cv Rupali, KH850, and KJ850, respectively. Numbered spots relate to proteins listed in Table 2. Arrows in pink represent signaling functions; those in blue show regulating functions and the dotted arrow indicates indirect signaling. LEAs, late embryogenesis abundant proteins; HSPs, heat shock proteins; AS, anthranilate synathase; SNARE, soluble N-ethylmaleimide-sensitive-factor attachment protein receptor proteins; PEPCK, phosphoenolpyruvate carboxykinase; MD, mitochondrial NAD-dependent malate dehydrogenase; SOD1, superoxide dismutase 1 (Cu/Zn); SOD, superoxide dismutase (Cu/Zn); GPX, glutathione peroxidase; 1-Cys Prx, 1-cys peroxiredoxin; PER1, 1-cys peroxiredoxin 1; CRT, calreticulin; CaM, calmodulin; Trx-like, thyroxin-like protein; Rev Tr, reverse transcriptase; THO1, THO complex unit1; PI, serine proteinase inhibitor.

may be related to the proteome expression profile in which many proteins are coordinately involved in a number of different metabolic pathways or specific metabolic events. A possible relationship between the groups of proteins associated with more rapid germination in cv Rupali is depicted in Figure 7. Pre-existing or newly expressed chaperone proteins encoded by stored RNA in seeds (LEAs and particularly HSPs) may be necessary components to protect or repair a wide range of proteins and re-establish normal biosynthesis or metabolic processes in the cell upon imbibition. This may have been particularly important for the rapid restoration of mitochondrial respiration and related enzymes. Reactivation of metabolism, especially respiration, upon water absorption by seeds leads to production of ROS, the level of which is modulated by antioxidant enzymes to an appropriate level for signaling. This is consistent with the expression pattern of most of the antioxidant enzymes identified in the present study which coincided with radicle emergence in cv Rupali. The increase may relate to an elevated level of ROS resulting from increased activity of ROS-producing processes such as respiration or other metabolic reactions. Interestingly, the subsequent decrease in abundance of these enzymes to levels similar to those seen in dry seeds may well reflect a changing role for

nism that occurs in response to a cue that is one of the earliest events after water enters the dry seed. The expression data presented in cluster 1 (Figure 6; the second largest class) demonstrated a significant decrease (down regulation) in protein abundance for nearly all proteins specifically in cv Rupali 72 h after imbibition. A similar but less consistent pattern also occurred in cluster 2 (the largest class). As this stage coincided with further and rapid growth of the radicle in cv Rupali, but which was not the case for other two genotypes, it may indicate that these proteins are functionally no longer involved, or at least are not as important as they were initially, in metabolic processes related to early stages of germination. A similar temporal or developmental expression of proteins has been reported in proteomic studies of germination and radicle elongation of barley under normal conditions58 and soybean primary root growth in response to water stress.13 SOTA analysis (Figure 6) was also able to show that the overall expression pattern of proteins within the 92 analyzed spots in cv Rupali at all time points was significantly different to the other genotypes, clustering in a separate class (Figure 6; top, vertical cluster tree). In other words, genotypic variation found during germination under severely limiting water supply 4304



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ROS in germination and the need for homeostasis.43 Generation of ROS at appropriate levels in response to the various cues that accompany or follow the ingress of water might have been more rapid in cv Rupali compared to the other genotypes. Although the proteomic data do not provide direct evidence to explain the more rapid germination trait, they do offer a testable hypothesis. In conclusion, the comparative proteomics analysis revealed significant differences in abundance among the non-storage protein complement present in cv Rupali seeds following imbibition compared to a genotype that germinated more slowly and one that imbibed but did not germinate effectively. Among the apparently important proteins were members of the LEA and HSP families, a group of enzymes involved in ROS metabolism and homeostasis together with proteins linked to the TCA cycle and the supply of C4 acids to the synthetic functions of the cycle. While some proteins, such as some members of HSP and LEA, were expressed during late seed development and stored until required for germination, others were newly expressed following imbibition. The clustering analysis used indicated a link between groups of proteins, implying a common regulatory mechanism. Thus the rapid germination trait shown by cv Rupali under severe water limitation could be accounted for by a number of integrated metabolic events and pathways (Figure 7) that together permit more rapid radicle emergence and elongation. This trait is important for early seedling vigor under limited moisture supply; perhaps QTL or other molecular markers can be developed to exploit the information obtained with the three chickpea cultivars in applying new breeding tools to enhance field performance of the chickpea crop.

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REFERENCES

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S Supporting Information *

This material is available free of charge via the Internet at http://pubs.acs.org.”

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AUTHOR INFORMATION

Corresponding Author

*Tel: +61864882262. Fax: +61864881001. E-mail: craig. [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Special thanks to Dr D. Goggin for her generosity in offering valuable advice and technical know-how in the area of protein separation and proteomic analysis. Thanks also to Dr. J. Palta for providing the seed material. We would like to thank the staff at Proteomics International for providing long-term laboratory facilities, excellent scientific and technical assistance for peptide extraction, and cutting edge mass spectrometric analysis. In this regard, we wish to thank Dr. R. Lipscombe, Dr. S. Bringans, Dr. T. Casey, Dr. T. Stoll, and of course Dr. R. Zareie for his help in interpretation of MS/MS spectra and also de novo peptide sequencing. We also thank UWA Statistical Consulting Group staff for their advice and fruitful discussion on statistical procedures. We also thank the Ministry of Science, Research and Technology of Iran, The University of Western Australia, School of Plant Biology and Institute of Agriculture for all the support provided to achieve this research. 4305



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