Identification of Extracellular Matrix Proteins of Rice (Oryza sativa L

Identification of Extracellular Matrix Proteins of Rice (Oryza sativa L.) Involved in Dehydration-Responsive Network: A Proteomic Approach Aarti Pandey,† Uma Rajamani,† Jitendra Verma, Pratigya Subba, Navjyoti Chakraborty, Asis Datta, Subhra Chakraborty,* and Niranjan Chakraborty* National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi-110067, India Received December 2, 2009

Water-deficit or dehydration impairs almost all physiological processes and greatly influences the geographical distribution of many crop species. It has been postulated that higher plants rely mostly on induction mechanisms to maintain cellular integrity during stress conditions. Plant cell wall or extracellular matrix (ECM) forms an important conduit for signal transduction between the apoplast and symplast and acts as front-line defense, thereby playing a key role in cell fate decision under various stress conditions. To better understand the molecular mechanism of dehydration response in plants, four-week-old rice seedlings were subjected to progressive dehydration by withdrawing water and the changes in the ECM proteome were examined using two-dimensional gel electrophoresis. Dehydrationresponsive temporal changes revealed 192 proteins that change their intensities by more than 2.5fold, at one or more time points during dehydration. The proteomic analysis led to the identification of about 100 differentially regulated proteins presumably involved in a variety of functions, including carbohydrate metabolism, cell defense and rescue, cell wall modification, cell signaling and molecular chaperones, among others. The differential rice proteome was compared with the dehydrationresponsive proteome data of chickpea and maize. The results revealed an evolutionary divergence in the dehydration response as well as organ specificity, with few conserved proteins. The differential expression of the candidate proteins, in conjunction with previously reported results, may provide new insight into the underlying mechanisms of the dehydration response in plants. This may also facilitate the targeted alteration of metabolic routes in the cell wall for agricultural and industrial exploitation. Keywords: dehydration • rice • extracellular matrix • 2-DE • mass spectrometry • cell signaling

Introduction Environmental stress factors greatly influence the geographical distribution of many crop species and have been estimated to decrease crop yields by up to 70% compared to the yields under favorable conditions.1 One of the most crucial environmental factors that limits crop production worldwide is waterdeficit or dehydration, which is especially severe in developing countries. Of the 1,500 million hectares of global cropland, only 250 million hectares (17%) are irrigated. Nevertheless, this irrigated land provides about 40% of the world’s food production, while the remaining 60% comes from rain-fed agriculture.2 Dehydration leads to a disruption in the water potential gradients, loss of turgor pressure, disruption of membrane integrity, and denaturation of proteins.3 The dehydration response in plants is a complex phenomenon, and the exact * To whom correspondence should be addressed. Dr. Niranjan Chakraborty, National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi-110067, India. E-mail: [email protected]. Tel: 00-91-1126735178. Fax: 00-91-11-26741658. Dr. Subhra Chakraborty, National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi-110067, India. E-mail: [email protected]. Tel: 00-91-11-26735186. Fax: 00-91-1126741658. † These authors contributed equally to this work. 10.1021/pr901098p

 2010 American Chemical Society

structural and functional modifications induced by dehydration are poorly understood. The ability of a plant to respond to and survive dehydration depends on whole-plant mechanisms that integrate diverse cellular responses.4 Thus, the accurate perception of dehydration and transduction of the signal to activate adaptive responses are critical steps that determine the survival of plants exposed to such stress. Changes in gene expression that are induced during dehydration have been investigated widely in many plant species, but reports on the identification and elucidation of the functional roles of their products are limited. Although the proteome is largely, but not exclusively, regulated by the transcriptome, often the transcriptome itself is controlled through feedback from the expressed proteins or other changes in the cellular biochemical state. Therefore, proteomic interrogation of the dehydration response reveals the plasticity of gene expression because it facilitates a global analysis of geneproducts and the physiological state of the cells. The importance of investigation of the proteome is further strengthened by several studies that revealed weak or moderate correlations between mRNA and protein levels.5,6 Alteration of protein synthesis and/or degradation is one of the fundamental Journal of Proteome Research 2010, 9, 3443–3464 3443 Published on Web 04/30/2010

research articles metabolic processes that probably influences dehydration tolerance in plants.7,8 It has been postulated that there is a close relationship between the accumulation of dehydration-induced proteins and physiological adaptations to dehydration.9,10 Plants exposed to dehydration mostly rely on the protection of cellular integrity to prevent mechanical damage by changing the composition of the cell wall.11,12 The cell wall or extracellular matrix (ECM) is the first compartment that perceives extracellular signals, transmits them to the cell interior, and eventually influences the cell fate decision.13-15 It is a dynamic structure that is essential not only for cell division, growth, and differentiation but also for the response to adverse environmental conditions.16-18 Although proteins account for only 10% of the ECM mass, they comprise several hundreds of different molecules with diverse cellular functions.19 Increasing evidence suggest that there is continuous cross-talk between the ECM and the cytoskeletal network.20 The ECM proteins are beset with specific complexities, in addition to the difficulties usually encountered in proteome analyses, such as protein separation and detection of low abundance proteins.21 Nonetheless, proteomics approaches are increasingly being used to identify proteins from this subcellular compartment.22-27,14 While there have been rapid advances in ECM research during the past few years, there is little information pertaining to the role of the ECM in stress tolerance. Therefore, the combination of ECM extraction and mass spectrometry appears to be a powerful strategy for identification of previously unknown protein components involved in stress responses.28,29 The plants of agricultural importance have been potential choices for investigating dehydration tolerance because different cultivars with differing degrees of tolerance are available. This provides correlative evidence for genes that are putatively involved in the dehydration response. Furthermore, the transient and moderate dehydration used in studies of these species probably describes the most common form of dehydration that most plants are likely to encounter. Rice is the most important food cereal, with nearly half of the world’s population relying on its successful harvests. The current annual rice production is 651 million tons,30 and more than 90% of rice is grown and consumed in Asia, which houses 60% of the world’s population. Recently, dehydration has become a major constraint for global rice production particularly in rain-fed environments,31 which highlights the need for a greater understanding of how plants respond to this stress. Previously, we developed a proteome reference map of the chickpea ECM and identified an array of dehydration-responsive components.14,28 In this study, we have developed the ECM-specific comparative proteome of a tolerant rice variety to address the underlying mechanism that enables this variety to withstand water-deficient conditions. The critical analysis of the proteome revealed 192 differentially expressed protein spots, and 94 of those proteins were identified using mass spectrometry. This study represents the first characterization of the repertoire of dehydration-responsive ECM proteins of rice, which may provide new insight into the underlying mechanisms of dehydration tolerance. This may also facilitate the targeted alteration of the extracellular matrix through genetic engineering and generate markers for precision selection of rice breeding programs.

Experimental Section Plant Growth, Maintenance, and Dehydration Treatment. Seeds of rice (Oryza sativa L. var. Rasi) were grown in a mixture of soil and soilrite (2:1, w/w; 10 plants/5.6 L capacity pot) in 3444

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Pandey et al. an environmentally controlled growth room. The seedlings were maintained at 28 ( 2 °C and 70 ( 5% relative humidity under 16 h photoperiod (270 µmol m-2 s-1 light intensity). The pots were provided with 300 mL of water everyday, which maintained the soil moisture content at ∼30%. A gradual dehydration condition was applied to the 4-week-old seedlings by withdrawing water, and aerial tissues were harvested during 48-192 at 24 h intervals. The tissues were harvested, instantly frozen in liquid nitrogen, and stored at -80 °C. Isolation of ECM Fraction and 2-DE. The ECM protein fraction was essentially isolated as described previously.14 Briefly, the tissues were ground to powder in liquid nitrogen with 0.3% (w/w) polyvinylpolypyrrolidone (PVPP) and transferred to an open-mouthed 50 mL centrifuge tube. The tissue powder was immediately homogenized in homogenizing buffer (5 mM K3PO4, pH 6.0, 5 mM DTT, and 1 mM PMSF) for 1-2 min. The ECM fraction was recovered by differential centrifugation at 1000× g for 5 min at 4 °C. The pellet was washed 10 times with excess deionized water. The extent of purification of the ECM fraction was examined as described previoulsy.14 The purified ECM fraction was suspended in three volumes (w/v) of extraction buffer [200 mM CaCl2, 5 mM DTT, 1 mM PMSF, and 0.3% (w/w) PVPP] and extracted on a shaking platform for 45 min at 4 °C. The proteins were separated from the insoluble fraction by centrifugation (10 000× g) for 10 min at 4 °C and filtered through 0.45-µm filter. The filtrate was concentrated using Centricon YM3 and then dialyzed overnight against 1000 volumes of deionized water with one solvent change. The concentration of protein extract was determined using the Bradford assay (Bio-Rad). Isoelectric focusing was carried out with 175 µg of protein in 250 µL of 2-D rehydration buffer for the 13-cm gel strips (Amersham Biosciences). The protein was loaded using the ingel rehydration method onto the IEF strips (pH range 4-7). Electrofocusing was performed using the IPGphor system (Amersham Biosciences) at 20 °C for 30 000 V-h. The focused strips were subjected to reduction with 1% (w/v) DTT in 10 mL of equilibration buffer [6 M urea, 50 mM Tris-HCl (pH 8.8), 30% (v/v) glycerol and 2% (w/v) SDS], followed by alkylation with 2.5% (w/v) iodoacetamide in the same buffer. The strips were then loaded onto the top of 12.5% polyacrylamide gels for SDS-PAGE. The electrophoresed proteins were stained with the Silver Stain Plus kit (Bio-Rad). Image Acquisition and Data Analysis. Gel images were digitized with a Bio-Rad FluorS equipped with a 12-bit camera. The PDQuest version 7.2.0 (Bio-Rad) was used to assemble the first level matchset (master image) from 3 biological replicates that had correlation coefficient values of at least 0.8. The experimental molecular mass and pI values were calculated from the digitized 2-DE images using standard molecular mass marker proteins. Each spot included on the standard gel met several criteria: it was present in all three gels and was qualitatively consistent in size and shape in the replicate gels. Each spot attribute was evaluated and weighted to produce a single numerical value for the spot quality. If a spot fit the Gaussian model perfectly, had no streaking in the x or y directions, did not overlap with any other spot, and had a peak intensity within the linear range of the scanner, PDQuest assigned a value of 100 to the spot. We defined “low-quality” spots as those with a quality score of less than 30; these spots were eliminated from further analyses. The protein spots were also checked manually to ensure that all analyzed spots were true protein spots and that the gel alignment was appropriate.

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Dehydration-Responsive ECM Proteome of Rice The remaining high-quality spot quantities were used to calculate the mean value for a given spot, and this value was used as the spot quantity on the standard gel. The first level matchset spot densities were normalized against the total density of the gel image. After obtaining the first level matchsets, a second level matchset was developed that allowed the standard gels from each of the time points to be compared. A second normalization was carried out with a set of three unaltered spots identified from across the time points. Next, the filtered spot quantities from the standard gels were assembled into a data matrix of high-quality spots from the eight time points for further analysis. Protein Identification and Expression Clustering. The protein spots were excised mechanically, destained, and ingel trypsin digested, and peptides were extracted according to standard techniques.32 The peptides were analyzed by electrospray ionization time-of-flight mass spectrometry (LC-MS/ TOF) using an Ultimate 3000 HPLC system (Dionex) coupled to a Q-Trap 4000 mass spectrometer (Applied Biosystems). Tryptic peptides were loaded onto a C18PepMap100 3 m column (LC Packings) and separated with a linear gradient of water, acetonitrile, and 0.1% formic acid (v/v). The MS/MS data was extracted using the Analyst Software version 1.4.1 (Applied Biosystems). The peptides were identified by searching the peak list against the Mass Spectrometry Protein Sequence Database (MSDB) August 09, 2006 (3 239 079 sequences; 1 079 594 700 residues) database using the MASCOT version 2.1 (Matrix Science) search engine. The following database search parameters were employed: taxonomy, Viridiplantae (Green Plants; 247 439 sequences); peptide tolerance, (1.2 Da; fragment mass tolerance, (0.6 Da; maximum allowed missed cleavage, 1; and instrument type, ESI-TRAP. The protein scores were derived from the ion scores on a nonprobabilistic basis for ranking protein hits, and the protein scores were the sum of a series of peptide scores. The score threshold to achieve p < 0.05 was set by the Mascot algorithm and was based on the size of the database used in the search. The details regarding the precursor ion mass, expected molecular weight, theoretical molecular weight, delta, score, rank, charge, number of missed cleavages, p value and the peptide sequence, along with the fragment spectra, for proteins identified with a single peptide are included in Supporting Information Document 1. If there was more than one accession number for the same peptide, the match was considered in terms of the putative function. If the same protein that was identified in multiple spots in which several peptides were shared by the isoforms, the differential expression pattern was observed for each of the candidates, and the proteins were thus listed as independent entities. The function of each of the identified proteins was analyzed in view of the metabolic role of the candidate protein in the extracellular matrix. The protein functions were assigned using the protein function databases Pfam (http://www.sanger.ac.uk/ software/Pfam/) or InterPro (http://www.ebi. ac.uk/interpro/). Because the functional annotation was based on Pfam and InterPro, functional redundancy, if any, was thus greatly minimized. Self-organizing tree algorithm (SOTA) clustering was performed on the log transformed fold-induction expression values across eight time points using the Multi Experiment Viewer software (The Institute for Genomic Research). The data were clustered with the Pearson correlation as the distance and with 10 cycles and a maximum cell diversity of 0.8.33 Immunoblot Analysis. Immunoblotting was performed by resolving the ECM proteins on a uniform 12.5% SDS-PAGE,

which was followed by electrotransfer onto nitrocellulose membrane at 150 mA for 2 h. The membranes were subsequently blocked with 5% (w/v) nonfat milk in TBST buffer (0.1 M Tris pH 7.9, 0.15 M NaCl and 0.1% Tween 20). The resolved proteins were probed with the respective primary polyclonal antibodies [antithioredoxin (sc 31057), anti-2-cys peroxiredoxin (ab 16765), anti-Hsp70 for DnaK (ab 5442) and anti-14-3-3 (sc 12672) from Santa Cruz Biotechnology Inc., USA or Abcam Ltd., U.K.], diluted to various ratios (1:250-1:1000) in TBS for 2 h. The blots were then incubated with alkaline phosphataseconjugated secondary antibody for 1 h, and the signals were detected using the nitro blue tetrazolium/5-bromo-4-chloro3-indolyl phosphate method. Enzyme Assays. The isolated ECM fraction was suspended and homogenized in 100 mM triethanolamine (pH 7.4) for superoxide dismutase (SOD) and 50 mM of KPO4 buffer (pH 7.0) for ascorbate peroxidase (APx). The homogenate was centrifuged at 16 000× g for 20 min at 4 °C, and the supernatant was transferred into a fresh tube and used for the enzymatic assays. SOD activity was determined by spectrophotometric method based on the inhibition of superoxide-driven NADH oxidation.34 The assay mixture contained 100 mM triethanolamine (pH 7.4), 100 mM/50 mM EDTA/MnCl2, 7.5 mM NADH and 10 mM mercaptoethanol in a total volume of 1.0 mL. The oxidation of NADH was monitored at 340 nm (with an absorbance coefficient of 6.2 mM-1 cm-1). The oxidation rates were initially low and then increased progressively (usually 2-4 min after mercaptoethanol addition) to yield linear kinetics (12-15 min), which were used for the calculation. APx was assayed from the decrease in absorbance at 290 nm (with an absorbance coefficient of 2.8 mM-1 cm-1) as ascorbate was oxidized by the enzyme.35 The reaction mixture for the peroxidase contained 50 mM KPO4 (pH 7.0), 0.5 mM ascorbate, and 0.1 mM H2O2 in a total volume of 1.0 mL. The reaction was initiated by addition of H2O2, and the absorbance was recorded 30 s after the addition. The data were corrected for the low amount of nonenzymatic oxidation of ascorbate by H2 O2 .

Results Dehydration-Responsive Morphological Symptoms and 2-DE of ECM Proteins. Our primary objective was to characterize the global protein expression pattern in the rice ECM under dehydration. The relatively dehydration-tolerant variety Rasi was selected for this study based on our previous screen of several rice genotypes.36 The seedlings did not show any visible symptoms of dehydration until 48 h. The visual symptoms, such as wilting, appeared on the seedlings after 72 h, and the damage was further aggravated at 96-192 h (Figure 1). The first symptom that appeared was the rolling of leaves near the margins, and this could be observed in about 75% of the stressed plants. After 96 h, the leaves showed more severe symptoms, and at this stage, all plants were affected by wilting. Previously, we established the isolation and enrichment of the ECM in the food legume chickpea.14,28 Using a similar approach, the rice ECM fraction was isolated from the aerial part of the seedlings, and the ECM-enriched pellet obtained was washed repeatedly to remove any contaminants from other organelles. The purity of the rice ECM fraction was sequentially examined by transmission electron microscopy and organellespecific marker enzyme assays. The micrographs showed that Journal of Proteome Research • Vol. 9, No. 7, 2010 3445

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Figure 1. Morphological symptoms induced by dehydration in rice seedlings. Four-week-old seedlings were subjected to gradual dehydration by withdrawing water over a period of 192 h. The photographs show the plant morphology at each time point, with the enlarged view of the leaf depicted in the inset. Table 1. Reproducibility of 2-Dimensional Gels time point average no of spotsa high quality spotsb reproducibility (%)

Control 48 h 72 h 96 h 120 h 144 h 168 h 192 h Total

262 285 289 262 291 289 261 315 2254

244 262 274 247 269 275 253 304 2128

93.13 91.93 94.81 94.27 92.44 95.16 96.93 96.50 94.41

a Average number of spots present in three replicate gels of each time point. b Spots having quality score more than 30 assigned by PDQuest (Ver.7.2.0).

Figure 2. Purity assessment of the isolated ECM fraction. (A) Transmission electron microscopy of the purified ECM pellet obtained after extensive washing steps. (B) Determination of vanadate-inhibited H+ ATPase activity in the rice ECM and plasma membrane fractions. The plasma membrane fraction was used as a positive control. (C) Determination of catalase activity in the ECM and cytosolic protein fractions. The cytosolic fraction was used as a positive control for this assay.

the ECM fraction was free from plasma membrane or other ultrastructural cytoplasmic organelles and that there were no intact cells that had escaped breakage during homogenization (Figure 2A). Since the plasma membrane is the most likely contaminant associated with the ECM, the fraction was tested for vanadate-inhibited H+ ATPase activity. The purified fraction displayed negligible vanadate-inhibited H+ ATPase activity 3446


compared with the plasma membrane fraction, which was used as a positive control (Figure 2B). The ECM pellet was then extracted with calcium chloride buffer. The ECM fraction was also checked for any cytosolic contamination using catalase as a marker enzyme. As expected, the ECM proteins did not show significant catalase activity, whereas the cytosolic proteins showed high catalase activity, which was evident from the drastic decrease in absorbance at 240 nm (Figure 2C). Isolated proteins from control and dehydrated seedlings were resolved and analyzed using high-resolution 2-DE followed by silver staining as detailed in the Experimental Section. The control samples consisted of protein prepared from the pooled unstressed seedlings harvested from different time periods during the course of the experiment to correct for the effects of growth and development of plants. Three biological replicate gels were run for each time point, which were then computationally combined into a standard gel (Supporting Information Figure 1). More than 250 protein spots were reproducibly detected on the silver-stained gels. Only spots that survived several stringent criteria (classified as “high-quality” spots) were used to estimate the spot quantities, though otherwise a large number of protein spots were included in the matchset. For example, 262 spots were detected in the control gel, and 244 were classified as high-quality spots (Table 1). The spot densities at the lower level were normalized to the total density present in the respective gels to correct for experimental errors introduced from differential staining. To compare the different time points, a second level matchset was created (Figure 3). The intensities of the spots were normalized to that of landmark proteins used for internal standardization. The

Dehydration-Responsive ECM Proteome of Rice

research articles turonase, phosphogluconate dehydrogenase, enolase, and multiple methyltransferases. Furthermore, proteins associated with wall metabolism and structural modifications are bona fide cell wall residents.38 However, many unexpected proteins were also detected, and these proteins are of interest because they do not contain the predicted signal peptide or they do not have a predicted function in the cell wall. Some of these proteins have previously been reported in the cell walls of other organisms.25,14,27,28 There might be an alternate route, hitherto undiscovered, for the localization of these proteins to the cell wall.39

Figure 3. Higher level matchset of protein spots detected by 2-DE. The matchset was created in silico by combining data from eight standard gels for each of the time points as depicted in Supporting Information Figure 1. The identified spots are circled, and the numbers correspond to the spot IDs mentioned in Table 2.

filtered spot quantities from the higher-level matchset were assembled into a data matrix that consisted of 330 unique spots and indicated changes in the intensity for each spot during dehydration. The data revealed that nearly 94% of the spots on the standard gels were of high quality, which reflects the reproducibility of the replicates (Table 1). The quantitative image analysis showed that a total of 192 protein spots had changes in abundance (p e 0.5) of more than 2.5-fold, at least at 1 time point. While most of the spots showed quantitative changes, some spots also showed qualitative changes. For these spots, the background value averaged across the replicates for that time point was used to calculate the fold change. Of the 192 dehydration-responsive proteins (DRPs), 94 proteins were clearly upregulated, 40 proteins were downregulated, and 58 proteins showed a mixed pattern of timedependent expression. Six typical gel regions depicting differential expression are enlarged and shown in Figure 4. Identification of the Differentially Expressed Proteins. In total, 94 DRPs that showed differential expression across 2 or more time points were identified by mass spectrometry. Since multiple proteins were represented by multiple spots with different pIs and/or molecular weights, the 94 spots accounted for 60 nonredundant proteins. These data suggest that 36% of the identified spots corresponded to either post-translationally modified forms or members of multigene families. Figure 5 illustrates some of these spot variations. It should be noted that some of the identified proteins (e.g., OsE-128, -265, -330, -100, and -267) showed discrepancies between the theoretical and observed molecular weights, which may be due to protein degradation, an incomplete protein database, or alternative splicing products.37,27 Out of the 94 differentially expressed proteins identified, 87 proteins were assigned functions that were classified into 9 functional classes, whereas 7 DRPs were designated as unknown proteins (Figure 6, Table 2). The carbohydrate metabolism class emerged as the most abundant (26%), with the cell defense and rescue class (17%) a close second. Cell wall modifying proteins and chaperones, each with 10% of the proteins, were the other major classes. A large number of the detected proteins have previously been shown to be cell wall localized proteins, including polygalac-

Dynamics of the Dehydration-Responsive Protein Network. One major goal for systems biology is to understand the interdependence of proteins in terms of expression profiles in a tissue or other biological samples. An important method to find the regulatory mechanism for protein interdependence is the application of hierarchical clustering algorithms similar to that used in DNA microarray experiments. Using this method, the proteins that clustered together are assumed to be involved in a related biological function. To summarize the coordinately regulated proteins contained in Table 2 and to cluster the proteins that showed similar expression profiles during dehydration, SOTA clustering was applied to the 94 identified proteins. The data were analyzed in terms of fold expression with respect to the control expression value. Furthermore, the data sets were log-transformed to base two to normalize the scale of expression and to reduce noise. The analysis yielded 11 expression clusters, and only clusters with n g 6 were used for the study of coexpression patterns (Figure 7). The most abundant group was Cluster 3 with 42 proteins showing upregulated expression at all time points. All the functional classes were represented in this cluster, although, proteins related to carbohydrate metabolism formed the major proportion (33%), followed by cell wall modifying proteins (12%). On the other hand, the downregulated proteins and proteins that showed a mixed pattern of expression were clustered in smaller groups and made up the remaining 10 clusters. Clusters 1 and 6, which contained 7 and 11 proteins, respectively, also had carbohydrate metabolism as the most abundant class, even though these clusters displayed different time kinetics. Cluster 2 with 10 proteins had more proteins related to cell defense and rescue (40%), followed by proteins involved in degradation (30%). The analysis of the clusterogram revealed an unbiased distribution of proteins involved in cell defense and rescue, suggesting a critical role of this class in dehydration tolerance. Immunoblot Analysis. A number of proteins were detected in multiple spots with different pIs. For example, thioredoxin, 2-cys peroxiredoxin, Hsp70/DnaK and 14-3-3 were detected in 2-5 protein spots (Table 2). It is widely recognized that proteins can resolve into multiple spots due to proteolytic processing, multiple isoforms, or changes in the charge state resulting from post-translational modifications. The differential expression of the multiple spots as detected in the 2-D gel (Figure 5) may provide new insight into the roles of these proteins in the dehydration response and cellular adaptation. Hence, we verified the identification of these proteins using immunoblot analysis (Figure 8) with the same protein extract used for 2-DE. The proteins followed similar trends of up-regulation, indicating their possible roles in dehydration tolerance; however, there were differences in fold induction and time specificity of expression compared with the 2-D profile. This highlights the Journal of Proteome Research • Vol. 9, No. 7, 2010 3447

research articles complexity of the plant response to stress, where different isoforms/members of a multigene family may play compensatory roles. Time Kinetics of ROS Metabolism. Water-deficit or dehydration causes an increase in oxidation,40 and the cellular defenses against or repair of oxidative damage are greatly

Pandey et al. compromised by excessive generation of ROS. Increasing evidence suggest that ROS scavenger molecules and antioxidant enzymes play an important role in protecting cells against oxidative damage. Because we identified several proteins involved in the oxidative pathway, we investigated the role of the antioxidant enzymes SOD and APx in the acquisition of

Table 2. List of Identified Dehydration-Responsive Cell Wall Proteins of Rice by MS/MS Analysis

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a Functional classification of proteins is only tentative since the biological function of many proteins identified has not yet been established experimentally. b Spot number as given on the 2-D gel images. The first letters (Os) signify the source plant, Oryza sativa, followed by the subcellular fraction, extracellular matrix (E). The numerals indicate the spot numbers corresponding to Figure 3. c Protein scores are derived from ion scores as a nonprobabilistic basis for ranking protein hits. d Gene identification number as in GenBank. e Protein expression profile represents the average change in spot density at various time points C (control), 48, 72, 96, 120, 144, 168, and 192 h. The data were taken in terms of fold expression with respect to the control value and were log transformed to the base two to level the scale of expression and to reduce the noise.

resistance to dehydration. Both enzymes showed maximum activities at 96 h of dehydration, which gradually decreased under severe stress conditions. While APx showed a 1.6-fold increase in activity, SOD displayed a 2.4-fold increase (Figure 9). The increased activities of the antioxidant enzymes indicate their possible role against dehydration-induced oxidative damage in the extracellular matrix.

Discussion This study represents the first effort to delineate the molecular basis of acquisition of tolerance to water-deficit conditions in the rice extracellular matrix. Proteins that are involved in imparting tolerance fell into two categories: the first group is involved in signaling cascades, an efficient mechanism for sensing dehydration, whereas the second group participates in altering cellular metabolism to withstand the deleterious effects of dehydration. The results obtained from the present study are illustrated in a representative model to summarize the events that occur in the ECM under dehydration (Figure 10). Dehydration-Responsive Differential Protein Network in ECM. The communication between the cytoskeleton and the ECM is one of the most characteristic feature of cellular

mechanism that allows cells to respond effectively to various extracellular signals,41 possibly through regulation of ROS levels. Nucleoside diphosphate kinase (NDK; OsE-365) is a ubiquitous enzyme in eukaryotes and prokaryotes that catalyzes the transfer of the γ-phosphate from ATP to NDP through autophosphorylation,42 thus contributing to downstream signaling by producing GTP for the activation of GTP-binding proteins. We observed an initial down-regulation of this protein under dehydration; however, the protein expression returned to close to the basal level at 192 h. Overexpression of NDK was previously reported to reduce the accumulation of ROS and provide tolerance against different abiotic stresses.43 Guanine nucleotide dissociation inhibitor (GDI; OsE-198 and -260) has been shown to interact with Rho-like small GTPases (ROPs), and recent results have suggested that ROPs have an essential function in the regulation of superoxide producing NADPH oxidases.44 The 14-3-3 family proteins (OsE-21 and -29) were first reported in the cell wall in Chlamydomonas45 and have been identified in the cell wall proteomes of several other organisms. Recently, it has been shown that tobacco cells transformed with an antisense construct of 14-3-3 no longer accumulate ROS,46 suggesting their key role in the stressinduced signaling pathways. Protein phosphorylation and Journal of Proteome Research • Vol. 9, No. 7, 2010 3457

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Figure 4. Representative zoomed-in gel sections for the differentially expressed proteins across eight time points during dehydration. The boxed areas (A-F) show the time-dependent expression patterns for a few of the dehydration-responsive protein spots.

dephosphorylation regulate numerous biological processes and are catalyzed by protein kinases and phosphatases. Inorganic pyrophosphatase (OsE-60) and inositol phosphatase (OsE-240) are other likely candidates that might be involved in the signal transduction network that operates in the ECM of rice seedlings. The apoplast is known to be a source of ROS production.29,47 In this study, a significant fraction of the identified ECM proteins were found to be linked to antioxidative/detoxifying reactions and mostly displayed inductions during dehydration. It is likely that dehydration increases ROS levels, particularly O2- and H2O2.48,49 SOD (OsE-96) catalyzes the dismutation of O2- to molecular oxygen and H2O2, thus playing a key role in cell defense and rescue.50 Subsequently, H2O2 can be decomposed by catalase or through a series of oxidoreduction reactions that involve APx (OsE-237) and glutathione peroxidase (GPx) and use ascorbate or reduced glutathione, respectively. While the accumulation of SOD decreased under progressive dehydration (Table 2), the activity of SOD increased (Figure 9). Monodehydroascorbate reductase (MDAR; OsE-185) and GSH-dependent dehydroascorbate reductase (DHAR; OsE-328) constitute a mechanism for the regeneration of antioxidants in the cell. In fact, DHAR is expressed in rate-limiting amounts and contributes significantly to establish the cellular ascorbate redox state.51 APx is highly sensitive to inactivation by ROS and is often insufficient to protect proteins during severe drought stress.52 The thioredoxin system is an important conserved system for protection against oxidative stress by reducing peroxides to harmless products and may mark an alternative pathway for detoxification of H2O2 during dehydration. The system is composed of three proteins: thioredoxin peroxidase/ 2-cys peroxiredoxin, thioredoxin, and thioredoxin reductase.53 3458


We identified several spots as 2-cys peroxiredoxin (OsE-9, -13, -14, -15, and -16) that showed up-regulation during dehydration. This protein is known to be regulated by drought in rice seedlings.54 Thioredoxins (OsE-3, -39, and -46) are involved in oxidative damage avoidance by supplying reducing power to reductases for detoxification of lipid hydroperoxides or repair of oxidized proteins.55 Furthermore, other lines of evidence have indicated that thioredoxins act as regulators of scavenging mechanisms and as components of signaling pathways in the plant antioxidant network. Methylglyoxal (MG) has a binding site on GPx, and high levels of MG inhibit the enzyme activity.56 Since GPx catalyzes the detoxification of H2O2, its inactivation leads to the accumulation of ROS. Glyoxalase (OsE-116) metabolizes MG and is indirectly involved in scavenging ROS.57 However, this protein was downregulated during dehydration unlike other members of this class. Glutamine synthetase or glutamate-ammonia ligase (OsE-180) activity is primarily responsible for scavenging ammonia, a highly reactive and cytotoxic metabolite that is maintained in a dynamic equilibrium with the conjugate ammonium cation and produced by the photorespiratory phosphoglycolate (C2) cycle.58 Oryzacystatin (OsE-2) is a cysteine proteinase inhibitor from rice in the phytocystatin family of proteinase inhibitors. Phytocystatins are known to have a role in regulation of proteinases during seed development and germination, in addition to pathogen attack and oxidative stress.59 Legumin (OsE-248) is a storage protein but has been shown to be differentially regulated during dehydration. It was also identified as a water-deficit responsive protein in the cell wall proteome of maize primary roots.29 It has been suggested that generation of hydroxyl radicals from H2O2 (by either the Fenton reaction or peroxidase activity)


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Figure 5. Relative abundance of a few of the redundant DRPs. Some spots were identified as isoforms/members of multigenic families, reflecting a change in pI and/or molecular weight. The expression profiles for the following proteins are represented in the form of a histogram: (A) thioredoxin, (B) 2-cys peroxiredoxin, (C) 14-3-3, (D) fructose-1,6-bisphosphatase, (E) DnaK, (F) GDI, (G) transaldolase, and (H) transketolase. The y-axis represents the fold change in expression of a particular protein across the eight time points under dehydration; the time zero bars are all 1 unit. While most spots showed quantitative changes, some spots showed qualitative changes, that is, they were either new spots or were missing at one time point. For these spots, the background value averaged across the replicates for a time point was used to calculate the fold changes.

plays a direct role in cell wall loosening via polysaccharide cleavage.60,61 Genes upregulated by dehydration have been shown to cause alterations in the chemical composition and physical properties of the cell wall (e.g., wall extensibility), and these changes may involve the genes encoding S-adenosylmethionine synthetase or methyltransferase (OsE-97, -101, -128, -302, -303, and -402). In the absence of stress, increased expression of methyltransferases has been correlated with lignification.62 Thus, the increased expression in dehydrated tissues could also be due to lignification in the cell wall. Adenosine kinase (ADK; OsE-70) is a key player in the Sadenosyl-L-Met (AdoMet) cycle, which provides methyl groups for a variety of transmethylation reactions.63 S-Adenosyl-Lhomocysteine hydrolase (AdoHcyase; OsE-57) is responsible for the reversible hydrolysis of S-adenosyl-L-homocysteine (AdoHcy) to adenosine and homocysteine. AdoHcy is formed as a direct

product of transmethylation reactions involving AdoMet and is known to be a potent inhibitor of most AdoMet-mediated methyl-transfer reactions.64 On the other hand, polygalacturonase (OsE-265) is a cell wall-degrading enzyme specifically associated with the processes of cell expansion as well as fruit ripening, abscission, and pathogen defense.65 Most of the metabolism-related DRPs identified in this study have been reported in the cell walls of different organisms. Enolase (OsE-136, -138, and -194) was detected in Candida albicans,66 Arabidopsis,23 and Medicago.25 Aldolase (OsE-246), triose phosphate isomerase (OsE-52 and -111) and GAPDH were reported in the secondary cell wall of developing xylem tracheary elements.67 Interestingly, fructose-1,6-bisphosphatase (OsE-35, -36, and -37) was also identified as a dehydrationresponsive ECM protein in this study. The actual functions of these proteins during dehydration remain to be investigated. Journal of Proteome Research • Vol. 9, No. 7, 2010 3459

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Figure 6. Functional classification of the dehydration-responsive proteins. The putative functions were assigned to each of the candidates using the protein function database and grouped as represented in the histogram.

Pandey et al. However, other members in this class are known to protect plants by changing osmolyte concentrations. Fructose bisphosphate aldolase has been hypothesized to play a vital role in dehydration tolerance by increasing the sucrose content, a potent osmolyte.68 Phosphoglucomutase (OsE-267), a phosphoenzyme, catalyzes an important trafficking step in carbohydrate metabolism. The conversion of Glc-6-P to Glc-1-P provides a substrate for the synthesis of UDP-Glc, which is required for synthesis of a variety of cellular constituents, including cell wall polymers and glycoproteins. These results suggest that the DRPs associated with metabolism may utilize the cell wall polysaccharides as a reservoir to produce sugar monomers, which maintain the osmotic adjustment in plants under dehydration. Surprisingly, many enzymes of the pentose phosphate pathway (PPP), including phosphogluconate dehydrogenase (OsE-330), ribose-5-phosphate isomerase (OsE-17 and -20), phosphoribulokinase (OsE-42, -50, -75, -76, and

Figure 7. Clustering analysis of the differentially expressed proteins based on their expression profiles. The 94 differentially expressed proteins were grouped into 11 clusters. The SOTA cluster tree is shown on the top, and the expression profiles are shown below. The expression profile of each individual protein in the cluster is depicted by gray lines, whereas the mean expression profile is marked in pink for each cluster. The number of proteins in each cluster is given in the left upper corner and the cluster number in the right lower corner. The markings on the x-axis reflect the time points in this study, while that on the y-axis show the log-transformed value of expression. 3460



research articles panoid biosynthesis pathway. The phenylpropanoid compounds have multiple functions, such as structural support, pigmentation, defense and signaling.69 3-β-hydroxysteroid dehydrogenase (OsE-243 and -291), another protein from this class, was also identified in the rice ECM.

Figure 8. Immunoblot analysis of a few of the DRPs with multiple isoforms/members of the same protein family. The expression profiles for thioredoxin, 2-cys peroxiredoxin, DnaK-type molecular chaperone and 14-3-3 were investigated using one-dimensional Western blot analysis. In each lane, 100 µg of protein was loaded, resolved on 12.5% SDS-PAGE and electrotransferred onto nitrocellulose membrane. The membranes were probed with the respective primary antibodies, and the proteins were detected by incubation with alkaline phosphatase-conjugated secondary antibody. The representative Coomassie-stained gel shows uniform protein loading.

Figure 9. Determination of the activities of ROS-metabolizing enzymes under dehydration. (A) SOD and (B) APx were extracted, and their activities were assayed as described in the Experimental Section. Enzyme activities are expressed in terms of fold change over the eight dehydration time points studied.

-100), sedoheptulose-1,7-bisphosphatase (OsE-32), transketolase (OsE-268 and -311) and transaldolase (OsE-77, -130, and -131), were detected in the dehydration-responsive ECM proteome. One of the functions of the PPP in cells is the generation of NADPH for reductive biosynthetic reactions and control of oxidative stress. Since apoplast acts as a reservoir for ROS, this might explain the presence of these enzymes in this compartment. The enzymes related to secondary metabolism were also notable among the list of dehydration-responsive proteins. Phenylalanine ammonia-lyase (PAL; OsE-431) and chalcone isomerase (OsE-53) were two enzymes from the phenylpro-

During stress adaptation, protein degradation is necessary for the removal of abnormal or damaged proteins and for altering the balance of proteins.70 In this study, a significant fraction of the proteins identified were involved in the degradation pathway. The R-20S proteasome (OsE-411) has been reported to be involved in ubiquitin-mediated turnover of misfolded proteins and in signal transduction.71 Oryzain (OsE24), a cysteine proteinase, is known to be induced under stress conditions.72 Oligopeptidase (OsE-150, -151, and -214), which was represented by three isoforms, might compensate for the down-regulation of the 20S proteasome subunit and perform essential protein degradation processes. Another protein with a similar function was the silverleaf whitefly induced protein 1 (OsE-132). Since intact proteins are less sensitive to oxidation than misfolded proteins, protein chaperones are usually upregulated in response to various stresses. Different chaperones have been documented to play complementary, and sometimes overlapping, roles in protection of proteins. Many proteins from this class were also identified in the differential proteome of the rice ECM, namely DnaK (OsE-87, -90, and -213), Hsp20 (OsE163), peptidyl-prolyl cis-trans isomerase (OsE-74 and -309), Hsp90 (OsE-30) and chaperonin 60 (OsE-140 and -141). Protein translation may represent another method to combat dehydration, which explains the differential profile of this protein class. Under normal conditions, the protein synthesis elongation factor EF-Tu (OsE-19 and -372) catalyzes the GTP-dependent binding of the aminoacyl-tRNA to the A-site of the ribosome during the elongation phase of protein synthesis. However, EFTu can act as a molecular chaperone during stress and might be involved in protein folding and protection.73 Ribosome recycling factor (OsE-238) was also identified as a differential protein in rice under dehydration, though its presence in the ECM remains debatable. Furthermore, cysteine synthase (OsE115), which is involved in the final step of cysteine biosynthesis, was also identified as a differential spot. Cysteine is an important amino acid required for biosynthesis of proteins like glutathione and thioredoxin, which play a major role in protection against abiotic and biotic stresses.74 The miscellaneous class of proteins with five members made up rest of the dehydration-responsive ECM proteome. The proteins in this class were ATP synthase (OsE-169), mago nashi-like protein (OsE-225) ferredoxin-NADP(H) oxidoreductase (OsE-342 and -412) and ferredoxin-nitrite reductase (OsE-398). Presence of Nonclassical Cell Wall Proteins. Many proteins identified and described in this study of the rice ECM, are otherwise known as intracellular proteins. Nevertheless, they have been reported in the cell wall proteomes of higher plants,14,23,25-29 green alga,75 and fungi.66 The presence of these proteins is puzzling because they do not have consensus signal sequences for targeting to the secretory pathway or any known function in the wall. These candidates may be part of an increasing number of proteins that are now known to defy the ‘rules’ of protein targeting. It is evident that some proteins are secreted into the extracellular matrix even though they lack the classical secretory signal peptide,76 but the nonclassical export mechanism is poorly understood. One such protein is elongation factor-1R, which was localized to tobacco cell walls,77 even Journal of Proteome Research • Vol. 9, No. 7, 2010 3461

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Pandey et al.

Figure 10. Dehydration-regulated network in the rice ECM. Proteins identified in this study are indicated in boxes and are displayed on the corresponding metabolic pathways. Graphs are representative expression profiles of individual protein and are expressed in terms of fold change, and the numeral below each graph indicates the protein identification number. Abbreviations: 6-Pg D, 6-phosphogluconate dehydrogenase; R-5-PI, ribose-5-phosphate isomerase; TK, transketolase; TA, transaldolase; Ald, aldolase; R-20S, R-20S proteasome; Ory, oryzain; SwiP, silverleaf whitefly induced protein; OP-A, oligopeptidase-A like; RRF, ribosome recycling factor; CS, cysteine synthase; Trx, thioredoxin; Tpx, thioredoxin peroxidase/2-cys peroxiredoxin; PPI, peptidyl-prolyl cis-trans isomerase; CPN60, chaperonin 60; MT, methyltransferase; PG, polygalacturonase; Gly, glyoxalase; Gln-syn, glutamine synthetase; OC, oryzacystatin; Leg, legumin; IPP, inorganic pyrophosphatase; InP, inositol phosphatase.

though it lacks a secretory signal peptide. Furthermore, elongation factor-1R is a bona fide cell wall protein in yeast.78 In a recent study of the Arabidopsis secretome, about 40% of the proteins identified lacked a putative N-terminal secretory signal peptide.79 These results suggest that certain cytoplasmic proteins without obvious targeting signals are secreted to perform specific extracellular functions.39,79 Comparison of Dehydration-Responsive ECM Proteomes. The focus of proteomic studies has only recently shifted to the organellar level. In the past decade, there have been a number of reports on the cell wall/ECM-associated proteomes in a variety of plants, such as Arabidopsis,23,24,26,39,80 Medicago,25 chickpea,14,28 maize,27,29 and rice.81 Nevertheless, dehydrationresponsive cell wall proteomics is still in its infancy. A proteomic approach was used to examine the water-soluble and loosely ionically bound cell wall proteins in the maize primary root elongation zone.29 The protein populations from this study and our other study in chickpeas28 were compared with the dehydration-responsive cell wall proteome of rice (Table 3). This comparison involves two distinct taxonomic groups; chickpea is a legume, whereas rice and maize are cereals. In addition, the fact that the dehydration-responsive proteomes of chickpea and rice display attributes of shoot tissue and the study in maize focused on cell wall proteins of root tissue adds to the value of this comparison. Many proteins were found to be unique to each of the crops studied. Despite the variation, it was observed that the differential cell wall proteome across the three crops showed some commonalities in the essential functional protein classes. Proteins associated with cell defense and rescue, namely APx and glyoxalase, were common in all three crops, apart from fructose-bisphosphate aldolase. It has been proposed that functionally important proteins are more evolutionarily conserved than less vital proteins.82,83 Nevertheless, the preponderance of differential protein networks in 3462


different crop species could be attributed to the evolutionary species-specific dynamics of the cell wall proteome because the protein expression profile is a reflection of the cellular environment and the ecological niche of the corresponding organism. A closer look at the results revealed that many more proteins are common between chickpea and rice, both studies having shoot tissue as the experimental material. Methyltransferases and the chaperone class of proteins were absent in the maize proteome, suggesting the importance of the sampled tissue. Interestingly, rice and maize showed a higher degree of commonality possibly because both species belong to the Poaceae family. These data altogether provide evidence for molecular diversity as opposed to the commonality of the differential protein profiles at the organismal level and/or at the tissue level. Nevertheless, the higher percentage of cropspecific DRPs signifies the necessity for studying the cell wall proteomes of different crops, especially the major lineages of higher plants, at different tissue levels to better understand the critical role of this organelle in stress tolerance.

Concluding Remarks In summary, the extracellular matrix in higher plants plays critical roles in a wide range of cellular functions, including structural integrity and biogenesis. Considering the importance of the ECM, we have analyzed the dehydration-induced changes in the ECM proteome of rice to further our understanding of the mechanism of dehydration tolerance in rice and compared it to the possible pathway established in our previous studies in chickpea.28 In this study, we identified and catalogued about 100 proteins that were differentially expressed, which may be involved in the establishment of dehydration tolerance in plants. The biological functions of most of these proteins have not yet been experimentally

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Dehydration-Responsive ECM Proteome of Rice Table 3. List of Common Dehydration-Responsive Cell Wall Proteins in Three Different Crops

spot ID functional classification

Signaling Cell defense and rescue

identification

Oryza sativa

Carbohydrate metabolism

OsE-365 OsE-96

CaE-10

Ascorbate peroxidases

OsE-237

CaE-54b CaE-73

Thioredoxin

OsE-3 OsE-39 OsE-46 OsE-116

CaE-82 CaE-97

Cysteine proteinase inhibitor Putative legumin

OsE-2 (Oryzacystatin) OsE-248

Methyltransferases

OsE-97 OsE-101 OsE-128 OsE-302 OsE-303 OsE-402 OsE-17

Adenosine kinase β-N-acetylhexosaminidase Sedoheptulose bisphosphatase Fructose-bisphosphate aldolase

OsE-32 OsE-246

Cytosolic 6-phosphogluconate dehydrogenase Enolase

Chaperones

Peptidyl-prolyl cis-trans isomerase Chaperonin 60

Miscellaneous

Cicer arietinumb

Nucleoside diphosphate kinase Superoxide dismutase

Glyoxalase I Chitinase

Cell wall modifying

a

ATP synthase Ferrodoxin-NADP reductase

OsE-330 OsE-136 OsE-138 OsE-194 OsE-74 OsE-309 OsE-140 OsE-141 OsE-169 OsE-342 OsE-412

Leucine aminopeptidase

CaE-192 CaE-206

Zea maysc

381 1149 385 1849 918 921

820 358 1663 840 2669 3464 3507

CaE-26 CaE-140 CaE-491 CaE-93 CaE-267 CaE-34 CaE-360 CaE-95 CaE-35 CaE-59

688 98 3228 116 479

CaE-215 CaE-315 CaE-151 CaE-239 CaE-58 CaE-5 CaE-49

1518

a Current study on dehydration-responsive ECM proteome of rice. b Study on dehydration-responsive proteins in chickpea ECM.28 c Differential cell wall proteome in the maize primary root elongation zone under water-deficit.29

studied. Our future efforts will focus on the characterization of the dehydration-induced novel proteins that are putatively involved in key regulatory cellular processes.

Acknowledgment. This work was supported by grants from the Council of Scientific and Industrial Research (CSIR), Govt. of India and the National Institute of Plant Genome Research, New Delhi, India. We thank Dr. H.E. Shashidhar, University of Agricultural Sciences, Bangalore, India for providing the rice seeds. We also thank Mr. Jasbeer Singh for illustrations and graphical representation in the manuscript. Supporting Information Available: Supporting Information Figure 1. Dehydration-responsive comparative proteome of rice ECM and the representative 2-DE gels. Supporting Information Document 1. The details regarding the precursor ion mass, expected molecular weight, theoretical molecular weight, delta, score, rank, charge, number of missed cleavages,

p-value and the peptide sequence, along with the fragment spectra, for the protein identified with a single peptide. This material is available free of charge via the Internet at http:// pubs.acs.org.

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