Proteome analysis of wheat leaf under salt stress by two-dimensional difference gel electrophoresis (2D-DIGE)

Phytochemistry 72 (2011) 1180–1191

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Proteome analysis of wheat leaf under salt stress by two-dimensional difference gel electrophoresis (2D-DIGE) L. Gao a,1, X. Yan b,1, X. Li a,1, G. Guo a, Y. Hu a, W. Ma c,⇑, Y. Yan a,⇑ a

College of Life Sciences, Capital Normal University, 100048 Beijing, China College of Resource Environment and Tourism, Capital Normal University, 100048 Beijing, China c Centre for Comparative Genomics, Murdoch University, Western Australia Department of Agriculture & Food, Australia b

a r t i c l e

i n f o

Article history: Available online 21 January 2011 Keywords: 2-D DIGE Proteome Salt-responsive proteins Salt tolerance Wheat leaf

a b s t r a c t Salt stress is a major abiotic stress that limits agricultural productivity in many regions of the world. To understand the molecular basis of the salt stress response in wheat (Triticum aestivum L.), a proteomic approach was used to identify the salt stress-responsive proteins in an elite Chinese wheat cultivar, Zhengmai 9023, which exhibits a high yield, superior gluten quality and better biotic resistance. Three-week-old seedlings were treated with NaCl of four different concentrations (1.0%, 1.5%, 2.0%, and 2.5%). The total proteins from the leaves of untreated and NaCl-treated plants were extracted and separated by two-dimensional difference gel electrophoresis (2D-DIGE). A total of 2358 protein spots were detected on the gels, among which 125 spots showed a significant change in protein abundance, and 83 differentially expressed spots were localised on preparative gels. Using Q-TOF mass spectrometry, 52 salt-responsive spots were identified, which were classified into six functional categories that included transport-associated proteins, detoxifying enzymes, ATP synthase, carbon metabolism, protein folding, and proteins with unknown biological functions. Of the 52 differentially expressed proteins, 26 were up-regulated, 21 were down-regulated, and five spots showed multi-expression patterns. In particular, some important proteins for salt tolerance were found to be up-regulated in Zhengmai 9023 under salt stress, such as H+-ATPases, glutathione S-transferase, ferritin and triosephosphate isomerase. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction Salt stress is a major abiotic stress that limits agricultural productivity in many regions of the world. Excessive Na+ is toxic and imparts both ionic and osmotic stresses. Recently, salinity has become an increasingly widespread problem. It is believed that by year 2050, the salinisation of more than 50% of all arable lands will have occurred (Wang et al., 2003). Wheat is an important cereal crop and a salt-sensitive glycophyte. The growth and grain yield of wheat are significantly affected by soil salinity (Garg and Gupta, 1999). However, little is known about wheat salt-responsive proteins or the molecular mechanisms of salt tolerance in wheat. Past studies on plant salt stresses have been largely in the areas of identifying, cloning and characterising new genes involved in the salt response. In Arabidopsis thaliana, two salt overly sensitive (SOS) genes, SOS1 and SOS2, which are required for intracellular ⇑ Corresponding authors. Address: Western Australia Department of Agriculture & Food Perth, WA 6150, Australia. Tel./fax: +61 8 93606836 (W. Ma); tel./fax: +86 10 68902777 (Y. Yan). E-mail addresses: [email protected] (W. Ma), [email protected] (Y. Yan). 1 These authors contributed equally to this work. 0031-9422/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.phytochem.2010.12.008

Na+ and K+ homeostasis, were cloned (Shi et al., 2000; Liu et al., 2000). These two genes encode proteins involved in salt tolerance, a putative Na+/H+ antiporter and a protein kinase, respectively. Ren et al. (2005) isolated the SKC1 gene by map-based cloning and found that it encodes a member of the HKT-type transporters. The authors suggested that SKC1 is involved in regulating K+/Na+ homeostasis under salt stress. A genetic engineering approach has also been used to increase plant salt tolerance in wheat (Munns et al., 2000; Xue et al., 2004; Colmer et al., 2006). Somatic hybridisation has been used to transfer salt-tolerant genes from phylogenetically related and remote grass species to wheat (Chen et al., 2000; Yue et al., 2001). However, these approaches rely on the availability of salt-tolerant genotypes and the ability to separate undesirable agronomic traits from the salt-tolerant trait. Understanding the gene network involved in wheat salt tolerance and identifying related genes will help to improve the salinity-response traits of wheat. It is well known that, in some cases, there is a poor, or no, correlation between the changes in mRNA levels and the abundance of the cognate proteins, and only direct protein measurements will reveal the real changes that occur at the protein level (Gygi et al., 1999). Consequently, proteomic analysis has become an alternative method to gain insights into the characteristics of the salinity response and to reveal the expression patterns

L. Gao et al. / Phytochemistry 72 (2011) 1180–1191

and functions of the response-associated genes. So far, proteomic methods have been widely used to investigate the different stress responses in various plants, including tobacco, rice, potato, Arabidopsis, and Synechocystis (Abbasi and Komatsu, 2004; Dani et al., 2005; Amme et al., 2006; Fulda et al., 2006; Aghaei et al., 2008). The investigation of salt stress-responsive proteins, with the aim of gaining a better knowledge about the salt stress tolerance in wheat, is of both fundamental and economic importance. Research of wheat salt stress has recently attracted great attention (Majoul et al., 2000; Kerepesi and Galiba, 2000; Saqib et al., 2006; Flagella et al., 2006; Zheng et al., 2008). Huo et al. (2004) has identified five candidate proteins by analysing the proteome of a salt-tolerant wheat mutant (RH8706-49) and a salt-sensitive wheat mutant (H8706-34) after both were treated by 1% NaCl for 72 h. Wang et al. (2008) detected 34 variety-specific and 49 saltresponsive proteins in the roots of wheat with different salt tolerances and functionally classified them into 11 categories. Caruso et al. (2008) identified 38 proteins in wheat leaf whose levels were altered in response to salt stress. In particular, 10 proteins were down-regulated, and 28 were up-regulated. However, in spite of these high quality studies, the information about the dynamics of the proteins in the leaves of wheat during salt stress remains limited. The Chinese winter wheat cultivar, Zhengmai 9023, is a widely used, elite cultivar in China and has many excellent characteristics, including a high yield, superior gluten quality and good biotic resistance. It has been cultivated in more than 7 million hm2 in China since its release in 2001 (Li et al., 2008). However, a detailed investigation of the proteomic changes under salt stress has not yet been carried out for this cultivar; indeed, the molecular basis for the mechanism of salt tolerance is still unclear. In this study, we investigated the Zhengmai 9023 leaf proteome dynamics during salt stress in order to identify the proteins that are important in wheat salt responses by using two-dimensional difference gel electrophoresis (2D-DIGE). 2. Results 2.1. The effects of salt stress on the RWC of wheat leaves Four different NaCl treatments (i.e., 1.0%, 1.5%, 2.0%, and 2.5%) were applied. Compared with the control, a decrease in the leaf relative water content (RWC) was observed in the salt-stress treatments (Fig. 1). The difference in the leaf RWC between the control and salt-treated samples became statistically significant, beginning with the 2.0% salt treatment level, where the RWC of

Fig. 1. The relative water content (RWC,%) of leaves of the control and treated groups with different concentrations of salinity. The RWC was measured at 2 days after the treatment. Four plants in each treatment were used, and the independent experiments were repeated three times. Values are the mean ± SE.

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the salt-treated leaf significantly decreased by 12%. It was also observed that, at the higher salt range, the decrease in the RWC level became smaller (e.g., only a 0.94% decrease when the salt concentration increased from 2.0% to 2.5%). 2.2. The proteomic analysis of the proteins in wheat leaves and the statistical analysis The comparison of the wheat leaf proteins between the control and salt treatments using the DIGE procedure revealed a broad distribution in the pI range, from 4.0 to 7.0, and the mass range, from 10 to more than 100 kDa. As a representative image, Fig. 2 shows the Cy2/Cy3/Cy5 overlay and the separated images. The samples were also separated by preparative gels for the identification of the proteins (Fig. 3). Among the control and the five salt-treated groups, an average of 2358 spots was detected in each gel using DIGE software (v.5.0.1, GE Healthcare). Of these 2358 protein spots, 637 could be matched in all of the gels. A total of 125 differential spots was determined by using the threshold of significance p < 0.05 (oneway ANOVA) and a P2.0-fold increase in protein expression. Only 83 differentially expressed spots were localised in the CBB-stained gels. PCA analysis was used to determine whether there were outliers in the dataset and also to check how well the samples grouped (Fig. 4a). The 125 differential protein spots were centralised into two principal components (PCs), PC1 and PC2, which represent the maximum variation (42.72%) and the next highest variation (17.65%). In the biplot (Fig 4a), the gels for each group (the coloured dots) are close to each other. The spots also appeared to form two distinct groups. Overall, these results indicated that the gels met the quality requirement. Fig. 4b shows the relationship between the replicate number and the power of the experiment. Among the three replicates, 85.6% of the data exhibited a power >0.8. 2.3. The MS/MS identification of the different proteins Among the 83 differentially expressed protein spots, 52 were identified with Q-TOF mass spectrometry (MS) analysis and the Mascot search engine (Table 1 and Supplementary Table 1). Approximately 47% (25) of the identified protein spots contained peptides matching different proteins from unrelated or related families (Supplementary Table 2), indicating that multiple proteins migrated to the same spots. For instance, spot 2 was identified as three proteins, an unnamed protein product (Triticum aestivum), the putative RuBisCo subunit binding-protein alpha subunit precursor (60 kDa chaperonin alpha subunit) (O. sativa) and the chaperonin 60 alpha subunit (C. lineata). Spot 15 was identified as two proteins, the putative Rieske Fe–S precursor protein (T. aestivum) and the cytochrome B6-F complex like-protein (Hordeum vulgare). The proteins shown in Table 1 are the proteins with the highest scores. In contrast, a number of spots, located at different positions on the same gel, were identified as the same proteins, with similar mass or pI values. For example, spots 30, 31, 32, 42, and 78 were all identified as ribulose 1,5-bisphosphate carboxylase activase isoform 1, and spots 80, 81, 82, and 93 were identified as methionine synthase. Similarly, a putative aconitate hydratase was identified in two spots (85 and 86), a 23 kDa polypeptide of photosystem II was identified from two spots (7 and 69), and a chaperonin was identified twice (spots 16 and 17). 2.4. The differential protein regulation under NaCl stress A comparative analysis was conducted on the 2D-DIGE gels using DIGE software (v.5.0.1, GE Healthcare). The average ratios

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Fig. 2. The reference 2D-DIGE electrophoretic patterns of the leaf protein extracts obtained from different NaCl-treated groups. (a) The labelled proteins were visualised for all of the fluorophores. (b) Cy2, mixing equal amounts of all of the proteins as the internal standard. (c) Cy3, for the protein sample of wheat treated with 2.0% NaCl. (d) Cy5, for the protein sample of wheat treated with 1.5% NaCl.

of the 52 identified differential proteins from the five groups are shown in Table 1. Among them, 26 were up-regulated, and 21 were down-regulated. There were five spots (1, 15, 31, 68, and 84) that showed multi-expression patterns and could not be classified simply into the up- or down-regulated group. Among the 47 up- or down-regulated proteins, only seven spots decreased or increased along with the increasing concentration of salt. Ribulose 1,5-bisphosphate carboxylase activase isoform 1 (spot 30 and 32), ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (spot 64) and a 23 kDa oxygen-evolving protein of photosystem II (spot 69) were decreased in the salt-treated samples, while the fructose 1,6-bisphosphate aldolase precursor (spot 29), OSJNBa0091D06.15 (spot 87) and methionine synthase (spot 93) showed a steady increase along with the salt concentration. Five of the identified proteins had quantitative changes that were independent of the salt concentrations. Spots 15, 31, and 84, identified, respectively, as the putative Rieske Fe–S precursor protein, ribulose 1,5-bisphosphate carboxylase activase isoform 1 and EST C74302 (E30840), a sequence that corresponds to a region of the predicted gene that is similar to glyceraldehyde-3-phosphate, were significantly up-regulated at the low salt concentration. However, the expression levels of these proteins decreased to a level that was even lower than the control at the high salt concentrations. In contrast, some proteins were found to be more abundantly expressed at the higher salt concentrations. For example, spot 68, identified as ferritin, was down-regulated at the 1.0% and 1.5% salt treatment levels, but was up-regulated by 2.19- and 3.13-fold at the other two salt concentrations.

2.5. The functional distribution of proteins affected by salt stress The 52 proteins were categorised into five groups, according to their putative functions, which included detoxification enzymes, ATP synthesis, carbon metabolism, and protein folding (Table 1). Fig. 5 shows the percentages of the protein total that were identified based on their functions; these included carbohydrate metabolism (29 spots, 55.8%), transport (3 spots, 5.8%), ATP synthesis (4 spots, 7.5%), detoxification enzymes (2 spots, 3.8%), and protein folding (2 spots, 3.8%). Some of the identified proteins were annotated either as unknown or hypothetical proteins or as proteins without a specific function in the database (12 spots, 23.1%). Spots 1, 2, and 3 were identified as the same unknown protein in the database, and spots 20, 33, and 79 were also identified as unknown proteins but with different accession numbers. Spots 87 and 88 were both identified as OSJNBa0091D06.15. Spots 22 and 23 were identified as Cp31BHv and the Ps16 protein, respectively. To gain the functional information about these proteins, we searched their homologues with BLASTP (http://www.ncbi.nlm.nih.gov/BLAST/), using their protein sequences as queries. Seven corresponding homologues with the highest homology are shown in Table 2. Most of the spots (except spots 87 and 88) shared more than 90% identity with their homologues at the amino acid level, indicating similar functions. To characterise the global expression trends of proteins involved in different processes, we established composite expression profiles by summing the protein abundance, expressed as standard log abundance, for each protein in each functional class for the four

L. Gao et al. / Phytochemistry 72 (2011) 1180–1191

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Fig. 3. The protein expression patterns in the leaves of wheat subjected to salt stress. Only the gel images of two of the NaCl-treated groups are shown ((a) 1% NaCl and (b) 1.5% NaCl). The proteins were extracted from the leaves after two days of treatment, separated by 2D-PAGE, and stained by CBB. (a) The spots indicated by arrows and numbers were affected by the salt treatment and identified by MS.

salt concentrations and the control (no salt) (Fig. 6). The proteins involved in carbon metabolism were divided into two sub-groups, the photosynthetic and other carbon metabolism proteins. As shown in Fig. 6, the expression of the photosynthetic proteins gradually decreased along with the increasing salt concentrations, while the expression levels of the other carbon metabolism proteins were up-regulated during the salt stress. The 2D montages of three replicates of some differential proteins from each functional class are shown in Fig. 7a. The derived expression profiles are given as graphical views, in which the protein abundances in the individual groups are shown in log scales relative to the internal standard (Fig. 7b). Two proteins involved in protein folding were identified, and spots 16 and 17, identified as chaperonins, were over-expressed during salt stress (Table 1). As shown in Fig. 7, the expression of spot 17 was very low in the control but over-expressed during salt stress, especially in the 1% NaCl treatment, where it was up-regulated by 2.5-fold. Three protein spots (Table 1, spots 21, 24, and 36) were identified as trans-

port-associated proteins, including the putative ATP synthase gamma chain 1, chloroplast H+-transporting two-sector ATPase/ F(1)-ATPase/A, putative H(+)-transporting ATP synthase and vacuolar ATP synthase subunit B isoform 1 (V-ATPase B subunit 1) (vacuolar proton pump B subunit 1). The combined expression level of these three proteins became higher in the leaves of salt-stressed wheat plants (Fig. 6). Spots 37, 38, and 40 were identified as the ATP synthase beta subunit, and spot 39 was identified as the ATP synthase CF1 beta chain. These four ATP synthesis-associated proteins had differential express patterns. Spot 37 was down-regulated in the leaf of wheat exposed to salt, and the other three proteins were all up-regulated under salt stress. Overall these results demonstrate that the abundance of the ATP synthesis group was increased in the NaCl-treated plants (Figs. 6 and 7). Three enzymes were found that function in detoxification to prevent the damage of the plants’ cellular structures: glutathione S-transferase (GST, spot 8), 2-cys peroxiredoxin BAS1, and chloroplast precursor (spot 67). The composite expression profile of this

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Fig. 4. (a) The principal component analysis of the different experimental groups. The grey ellipse in the centre is all of the spot numbers of 125 proteins with statistically significant differences. The coloured dots represent the different images in this experiment (pink, control; blue, 1.0% NaCl; purple, 1.5% NaCl; yellow, 2.0% NaCl; and lightblue, 2.5% NaCl). (b) The power analysis of the experiment, where 80% power is an accepted level.

functional group is shown in Fig. 6, and ferritin (spot 68), as a representative protein, is shown in Fig. 7a – 4 and b – D. Several important enzymes that participate in the carbon metabolism pathways were also identified, including ribulose 1,5-bisphosphate carboxylase large/small subunit, 23 kDa oxygen-evolving protein of photosystem II, triosephosphate isomerase, putative aconitate hydratase. Among these identified proteins, the RuBisCo large/small subunit, RuBisCo activase isoform and 23 kDa oxygen-evolving protein of photosystem II are all photosynthetic proteins. The expression level of these proteins became lower with the increase of the salt concentration (see Figs. 6 and Fig. 7a – 5 and b – E). Two putative aconitate hydratases (spots 85 and 86), which are involved in the tricarboxylic acid (TCA) cycle, were also down-regulated (Table 1). In contrast, triosephosphate-isomerase (spot 70), involved in the glycolysis pathway, was up-regulated (Fig. 7a – 6 and b – F). 3. Discussion Salt in the soil water inhibits plant growth for two reasons (Munns et al., 2006). Firstly, the presence of salt in the soil reduces the ability of the plant to take up water, which leads to a decrease of the leaf RWC. This is known as the osmotic or water-deficit ef-

fect of salinity. Species must have ways to handle the salt as it arrives in the leaves through vascular transport, as well as to manage the gradual accumulation of salt over time to protect the cells from death due to the high salt concentration. A lower RWC can make the leaves more narrow and the cells smaller, so that the chloroplast density is greater and the photosynthesis per unit leaf area is not initially reduced significantly by the salinity (James et al., 2002). Secondly, the excessive amount of salt entering the transpiration stream will eventually injure cells in the transpiring leaves, and this may further reduce growth. The cell injury and further growth reduction is the salt-specific or ion-excess effect of salinity (Munns et al., 2006). The regulation function would be lost by the death of the cells, so the RWC of the leaves would no longer change significantly as the salt concentration passes a threshold (e.g., 2.5%). In the present study, most of the identified proteins had complicated changes during the salt stress (Table 1). The complicated changes may due to the fact that the mechanisms of salt tolerance are complex, involving changes in protein abundance and the production of stress proteins and their compatible osmolytes (Zhu et al., 1997). To achieve salt tolerance, three interconnected aspects of plant processes should take place. First, the damage must be prevented or alleviated. Second, homeostatic conditions must be re-established in the new, stressful environment. Third,

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L. Gao et al. / Phytochemistry 72 (2011) 1180–1191 Table 1 Showing identities of salt responsive protein spots. Spot ID

Accession No.

Scorea

Protein name

Transporting associated proteins 21 gi|50912809 Putative H(+)-transporting ATP synthase 24 gi|50937699 Putative ATP synthase gamma chain 1, chloroplast (H(+)-transporting two-sector ATPase/F(1)-ATPase/A 36 gi|2493131 Vacuolar ATP synthase subunit B isoform 1

SC (%)b

Tmw/Emwc

TpI/EpId

PCe

p-valuef

Average ratiog T1h

T2

T3

T4

120 218

17 17

26.2/25.0 39.7/40.2

4.98/4.12 8.60/5.97

1 6

0.0164 0.0074

2.08 3.21

1.40 2.09

1.18 2.43

1.13 1.69

406

14

53.9/59.8

5.12/5.21

7

0.0005

2.88

2.14

1.92

1.92

461 809 1120 1020

11 25 41 41

48.6/60.2 53.8/62.1 53.8/56.9 53.8/61.4

5.52/5.04 5.17/4.92 5.06/4.87 5.17/4.81

4 9 15 13

0.0019 0.0063 0.0103 0.0217

0.76 2.15 2.09 2.48

0.62 3.12 2.77 1.53

0.21 2.04 1.92 1.87

0.83 1.97 2.00 1.64

ATP Synthase 37 gi|21684927 38 gi|343984 39 gi|14017579 40 gi|343984

ATP ATP ATP ATP

Protein Folding 16 gi|1167858 17 gi|1167858

Chaperonin Chaperonin

299 685

15 31

53.4/67.3 53.4/67.0

4.88/5.01 4.88/5.09

7 12

0.0138 0.0229

1.98 2.47

1.68 1.44

2.57 2.22

1.36 1.35

Detoxifying Enzymes 8 gi|20067415 68 gi|58221595

Glutathione S-transferase Ferritin

491 139

50 13

24.9/25.7 28.1/27.5

6.35/6.42 5.66/5.34

12 3

0.0237 0.0004

4.16 0.75

3.39 0.53

2.35 2.93

1.87 3.13

23 kDa polypeptide of photosystem II Ribulose 1,5-bisphosphate carboxylase large subunit Probable fructose-bisphosphate aldolase Fructose 1,6-bisphosphate aldolase precursor Fructose 1,6-bisphosphate aldolase precursor Ribulose 1,5-bisphosphate carboxylase activase isoform 1 Ribulose 1,5-bisphosphate carboxylase activase isoform 1 Ribulose 1,5-bisphosphate carboxylase activase isoform 1 3-phosphoglycerate kinase Ribulose 1,5-bisphosphate carboxylase activase isoform 1 Ribulose 1,5-bisphosphate carboxylase/ oxygenase large subunit Ribulose 1,5-bisphosphate carboxylase large subunit Ribulosebiphosphate carboxylase Ribulose-1,5-bisphosphate carboxylase/ oxygenase large subunit 23 kDa oxygen-evolving protein of photosystem II Triosephosphat-isomerase Ribulose-1,5-bisphosphate carboxylase large subunit Ribulose-1,5-bisphosphate carboxylase/ oxygenase small subunit Ribulose-1,5-bisphosphate carboxylase/ oxygenase small subunit Ribulose-1,5-bisphosphate carboxylase small subunit Ribulose 1,5-bisphosphate carboxylase activase isoform 1 Methionine synthase Methionine synthase Methionine synthase EST C74302(E30840) corresponds to a region of the predicted gene.similar to glyceraldehyde3-phosp Putative Aconitate hydratase Putative Aconitate hydratase Methionine synthase Ribulose-1,5-bisphosphate carboxylase, large subunit

85 565

7 26

27.0/23.9 49.2/56.2

9.06/6.51 6.75/6.52

2 13

0.0002 0.0009

0.93 0.49

0.31 0.52

0.43 0.87

0.28 0.61

185 193 313 363

7 9 12 30

42.1/42.3 41.9/41.8 41.9/42.1 47.1/48.3

7.60/5.25 9.01/5.43 9.01/5.71 8.62/5.48

3 3 4 11

0.0401 0.0055 0.0416 0.0007

3.41 2.38 1.53 0.98

2.88 1.86 2.38 0.70

1.90 1.87 2.50 0.60

1.76 1.69 2.57 0.28

326

16

47.1/47.5

8.62/5.49

5

0.0154

2.11

1.15

0.73

0.97

613

22

47.1/48.0

8.62/5.18

8

0.0012

0.87

0.68

0.58

0.44

391 418

26 13

31.4/49.2 53.4/47.1

4.89/5.14 8.62/5.21

6 4

0.0128 0.0138

2.46 1.13

1.54 2.63

1.13 2.10

1.38 1.82

389

19

43.7/22.5

6.29/5.31

5

0.0014

0.33

0.42

0.61

0.41

510

22

49.2/35.6

6.75/6.52

10

0.0089

2.63

5.97

6.87

4.22

500 601

27 25

52.5/30.4 46.6/31.2

6.08/6.43 6.18/6.26

15 13

0.0492 0.0063

0.67 0.82

0.43 0.35

0.73 0.29

0.68 0.20

519

31

27.3/28.2

8.84/5.42

6

0.0414

0.75

0.69

0.64

0.35

195 274

19 10

26.8/29.1 48.9/23.9

5.38/5.51 6.94/6.91

4 4

0.0233 0.0341

1.64 0.80

2.42 0.50

3.23 0.67

3.34 0.67

417

61

18.5/20.8

8.83/6.23

10

0.0030

0.81

0.24

0.12

0.42

83

18

33.3/17.3

8.92/6.83

3

0.0021

1.07

0.64

0.21

0.68

88

37

34.2/19.4

8.98/5.76

5

0.0378

0.42

0.78

0.73

0.84

484

13

47.1/53.4

8.62/5.23

4

0.0013

0.32

0.70

0.52

0.52

930 527 1250 96

16 11 42 21

84.5/97.4 84.5/97.4 84.5/97.4 47.1/48.1

5.68/6.12 5.68/6.26 5.68/6.23 6.22/5.79

9 6 22 1

0.0321 0.0436 0.0395 0.0053

2.62 1.16 1.65 2.03

3.75 2.53 3.20 1.52

1.10 1.40 1.89 0.99

2.29 2.30 2.82 0.57

161 193 438 632

11 10 13 19

98.0/98.2 98.0/98.2 84.5/91.3 49.6/49.8

5.67/6.04 5.67/6.17 5.6/6.10 6.43/6.92

8 7 7 8

0.0098 0.0171 0.0321 0.035

0.73 0.52 3.37 1.21

0.48 0.30 3.39 1.43

0.23 0.60 4.69 2.56

0.33 0.57 4.82 2.71

316 732 624 461 357

17 34 16 39 53

57.5/61.2 57.5/61.2 57.5/61.2 23.7/21.2 13.0/16.5

4.83/4.81 4.83/4.87 4.83/4.91 8.47/5.64 5.84/5.53

3 13 7 8 6

0.0098 0.0056 0.0242 0.0256 0.0052

0.89 2.89 0.98 2.65 0.73

1.83 2.64 0.91 0.82 0.44

2.49 3.73 0.43 1.10 0.37

1.27 2.14 0.92 0.75 0.22

Carbon Metabolism 7 gi|2570499 19 gi|33635963 27 28 29 30

gi|7436612 gi|8272480 gi|8272480 gi|167096

31

gi|167096

32

gi|167096

41 42

gi|21396677 gi|167096

43

gi|2734922

45

gi|33635963

46 64

gi|1488525 gi|37361619

69

gi|21837

70 71

gi|11124572 gi|22003630

72

gi|4038719

73

gi|4038699

76

gi|1167948

78

gi|167096

80 81 82 84

gi|50897038 gi|50897038 gi|50897038 gi|50948907

85 86 93 100

gi|50941891 gi|50941891 gi|50897038 gi|1869936

Unknown 1 2 3 15 20

gi|1345582 gi|1345582 gi|1345582 gi|32394644 gi|21866

synthase synthase synthase synthase

beta subunit beta subunit CF1 beta chain beta subunit

Unnamed protein product Unnamed protein product Unnamed protein product putative Rieske Fe–S precursor protein Unnamed protein product

(continued on next page)

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Table 1 (continued) Spot ID

Accession No.

22 23 33 67

gi|3550483 gi|7446356 gi|21833 gi|2829687

79 87 88

gi|755762 gi|50926886 gi|50926886

Scorea

Protein name

Cp31BHv Ps16 protein Unnamed protein product 2-cys peroxiredoxin BAS1, chloroplast precursor Unnamed protein product OSJNBa0091D06.15 OSJNBa0091D06.15

SC (%)b

Tmw/Emwc

TpI/EpId

PCe

p-valuef

Average ratiog T1h

T2

T3

T4

293 121 465 424

20 13 33 17

30.7/30.9 31.8/32.1 49.8/49.1 23.3/24.1

4.76/4.24 4.55/4.18 6.58/5.25 5.71/4.75

6 3 13 3

0.0179 0.0401 0.0004 0.0079

0.23 0.34 0.47 2.43

0.68 0.74 0.43 1.18

0.31 0.72 0.36 1.78

0.38 0.91 0.58 1.06

161 231 239

25 12 27

46.6/47,4 82.2/82.4 82.2/82.4

5.75/4.75 5.69/4.79 5.69/4.83

2 6 13

0.0006 0.0378 0.0096

3.76 2.18 2.23

1.22 2.22 2.47

2.37 2.38 1.69

1.28 2.57 1.52

a Score: statistical probability of true positive identification of the predicted protein calculated by MASCOT with 0.3 peptide tolerance and one allowed missed cleavage (score68 against NCBInr). b SC: percentage of predicated protein sequence covered by matched sequences. c Tmw/Emw: molecular mass of predicted protein/molecular mass of protein on gel. d TpI/EpI: pI of predicted protein/pI of protein on gel. e PC: matched peptide count. f p-value: p-value of the one-way ANOVA for the control/treated, p < 0.05. g Average ratio: average ratio of the protein abundance (treated/control) on different salt concentrations.

Fig. 5. The functional distribution of the identified differentially expressed proteins during salt stress. A pie chart of the six (including the group of ‘‘unknown’’) protein groups, categorised according to their putative functions. The numbers and percentages of each protein group are indicated.

the growth must resume, albeit at a reduced rate (Zhu, 2001). The complex mechanism of salt tolerance may result in the observed complicated changes of proteins that are affected by salinity. In this study, the same proteins were often detected from different spots on the 2D gels. Such a phenomenon has been reported previously (Dani et al., 2005; Yan et al., 2005, 2006; Taylor et al.,

Fig. 6. Composite expression profiles of the various classes of proteins distinguished during salinity stress. The combined expression profiles were calculated as the sum of all of the relative volumes for each protein in the functional category (the ‘‘unknown’’ group was not included).

2005; Wang et al., 2008). It is usually caused by some common and important properties and dynamics of proteins, such as the following: the existence of protein isoforms, post-translational modifications (PTMs), translation from alternatively spliced mRNAs, and protein degradation. PTMs, such as glycosylation and phosphorylation, can change the molecular weight and/or charge of a protein. The reversible phosphorylation of proteins is an important regulatory mechanism that occurs in both prokaryotic and

Table 2 The homologues of the unknown proteins BLASTP (www.ncbi.nlm.nih.gov/BLAST/) was used to search the homologues of the unknown proteins in Table 1. The homologues with the highest homology are shown. Spot No.

Accession No.

a

Homologue NCBI accession No.

1, 2, 3 20 22 23 33 79 87, 88 a b

gi|1345582 gi|21866 gi|3550483 gi|7446356 gi|21833 gi|755762 gi|50926886

P08823 BAB19810 NP_001105347 ABR25700 P12782 AAZ30062 XP_002509581

b

Protein name

Species

Identities (%)

Positives (%)

RuBisCO large subunit-binding protein alpha subunit Ribulose-1,5-bisphosphate carboxylase/oxygenase small subunit Nucleic acid binding protein1 Chloroplast 28 kDa ribonucleoprotein Phosphoglycerate kinase Plastid glutamine synthetase isoform GS2c Putative translation elongation factor G

T. aestivum T. aestivum Z. mays O. sativa T. aestivum T. aestivum R. communis

100 99 79 92 100 98 76

100 100 91 97 100 99 84

The accession number of the unknown proteins in Tables 1. The accession number of the homologues.

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Fig. 7. The selected differential protein spots from the functional groups. (a) The 2D montages of the three replicates of the differential proteins from the control and the treated groups (1–6). (b) Graphical views of the selected proteins (A–F).

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eukaryotic organisms. Many enzymes and receptors are switched ‘‘on’’ or ‘‘off’’ by phosphorylation and dephosphorylation. Reversible phosphorylation results in a conformational change in the tertiary or quaternary structure of many enzymes and receptors, causing them to become activated or deactivated (Chang and Stewart, 1998; Cozzone et al., 2004; Cozzone, 2005). Chitteti et al. used the Pro-Q Diamond phosphoprotein stain to study the differential expression of the rice phosphoproteome under salt stress, and the authors identified some putative phosphoproteins that might be related to salt stress (Chitteti and Zhao, 2007). These results indicated that post-translational modifications likely play a key role in mediating the response to salt stress. In order to survive under salt stress, plants have to re-establish homeostasis in stressful environments. An important strategy for achieving this is the regulation of ion transport through a change in the expression of transporting proteins. The H+-ATPases play a significant role in the selective distribution of ions within the plant or cell. Generally, Na+ severely inhibits most enzymes at a concentration above 100 mM, indicating its importance in preventing Na+ from accumulating to a high level in the cytoplasm or in organelles other than the vacuole (Zhu, 2001). The H+-ATPases generate the H+ electrochemical gradient that is the driving force utilised by the tonoplast Na+/H+ antiporter to distribute Na+ and Cl ions into the apoplast, vacuole and also the growth medium (Ashraf and Harris, 2004). Wang et al. (2008) found that a vacuolar proton ATPase (V-ATPase) subunit E was specifically expressed in Shanrong No. 3, a wheat variety with a high salt-tolerance, and up-regulated under salt stress. The authors concluded that the specific synthesis of the V-ATPase subunit E in Shanrong No. 3 may be one of the possible causes for its high tolerance to salt. Caruso et al. (2008) distinguished proteins that were affected by salinity in wheat (Triticum durum ‘Ofanto’), but the H+-ATPases or their isoforms were not identified in their study. In the present study, three protein spots were classified as H+-ATPases (spots 21, 24, and 36), and their expression levels were increased in the salt-treated samples, compared to the control (Table 1). We suggest that these may be key factors for the high salt tolerance of Zhengmai 9023. Apart from the proteins directly related to ion distribution, we also identified proteins that are indirectly related to ion distribution, such as ATP synthesis-associated proteins. In this study, four ATP synthesis-associated proteins were identified, and their abundance was increased in the NaCl-treated plants (Figs. 6 and 7). Previous studies have suggested that the expression of ATP synthase can be enhanced during salt stress (Wang et al., 2008; Parker et al., 2006). The enhanced ATP synthesis in salt-stressed plants may reflect required chemical processes, such as the modulation of ion homeostasis in plant cells. ATP may be transported from the stroma to support the increased activity of the H+-ATPases, which are required for mediating the H+ ion transport from the outer compartment to the inner mitochondrial matrix. The imposition of biotic and abiotic stress conditions can give rise to excess concentrations of reactive oxygen species (ROS), such as singlet oxygen ðO12 Þ, superoxide radical ðO 2 Þ, hydroxyl radical (OH) and hydrogen peroxide (H2O2), resulting in oxidative damage at the cellular level. Therefore, antioxidants and antioxidant enzymes function to interrupt the cascades of uncontrolled oxidation in each organelle (Shigeoka et al., 2002). Glutathione S-transferase is an important enzyme that can scavenge these toxic compounds to regulate the ROS level in the plant cell (Ndimba et al., 2005; Jiang et al., 2007). The over-expression of glutathione S-transferases was observed in the salt-stressed groups in the present study (Table 1). A ferritin protein was also identified, whose abundance increased significantly after salt treatment (Fig. 7a – 4 and b – D). In previous studies, ferritin was also identified as an up-regulated protein in other species, such as salt-stressed Arabidopsis (Ndimba et al., 2005) and rice (Parker et al., 2006). The induc-

tion of ferritin in salt-stressed wheat has not been reported to date. Plant ferritins, as iron-storage proteins, play a protective role against the toxic effects of an iron overload in cells. During salt stress, the reaction between ferrous iron and H2O2 could result in the formation of hydroxyl radicals, the most dangerous type of ROS. Thus, the increased expression of ferritin could help to neutralise the damage resulting from ROS. Triosephosphate isomerase (Spot 70, an increase of 3.34-fold after salt stress) is an important enzyme that catalyses the essential isomerisation reaction between dihydroxyacetone phosphate and D-glyceraldehyde-3-P in the glycolysis pathway. The up-regulation of glucose catabolism could be partly due to a need for extra energy for the detoxification and repair of damages caused by oxidative molecules. In plants, salt stress is known to have significant effects on carbon metabolism and changes in the carbohydrate content (Kerepesi and Galiba, 2000). It has also been reported that salt stress inhibits plant growth. The slower growth is an adaptive feature for the survival of the plant under stress because it allows plants to rely on multiple resources to combat the stress (Zhu, 2001). Indeed, a reduction in the activity of the RuBisCo large/small subunit and RuBisCo activase isoform could result in limited CO2 fixation and lead to a down-regulation of enzymes that are associated with the Calvin cycle. Consequently, the down-regulation of the Calvin cycle may lead to the accumulation of ATP and NADPH in the chloroplasts, and, to protect the plants from the photo-oxidative damage, the decrease of proteins (for example, the 23 kDa oxygen evolving-protein of photosystem II) involved in PS II would be necessary (Ranieri et al., 2001). This hypothesis is supported by our results, as well as other studies (Zhu, 2001; Bohler et al., 2007). Peng et al. (2009) investigated the effect of drought and salinity stress on the seedlings of the somatic hybrid wheat cv., Shanrong No. 3 (SR3), and its parent cv., Jinan 177 (JN177). Among the identified 65 differentially expressed proteins in the leaves, 16 proteins belonged to the functional group of photosynthesis. Most of these 16 proteins were up-regulated in the salinity-stressed SR3 and JN177 plants, with 14 proteins having a lower ratio of increase in SR3, which has a high salt tolerance. This suggests that highly salt-tolerant wheat cultivars may have lower a photosynthetic efficiency. In summary, four different levels of salt treatments were used in the current study, a treatment number that is higher than any previously reported studies on the effects of salt stress in wheat; thus, we suggest that more detailed information was revealed in the present study. Important proteins that responded to salt stress in the elite cultivar, Zhengmai 9023, were identified, including H+ATPases, glutathione S-transferase, ferritin and triosephosphateisomerase, which were up-regulated in the salt-stressed samples. These proteins may constitute the molecular basis of the high salt-tolerance for Zhengmai 9023. These results will be useful for further understanding the biochemical and molecular mechanisms of salt tolerance in wheat.

4. Experimental procedures 4.1. The plant material and treatments A common wheat (T. aestivum L.) cultivar, Zhengmai-9023, was used in this study. A total of 400 hand-selected seeds of uniform size were surface sterilised with 15% (v/v) HgCl for 15 min (min) and washed four times with sterilised distilled water. The seeds were submerged in water for 12 h (h) at room temperature and then transferred to wet filter paper to germinate at room temperature (20–25 °C) for 24 h. The germinated seeds were grown in a plastic tank with water. When the first leaves had emerged, the plants were cultured in Hoagland solution containing 5 mM

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KNO3, 2 mM MgSO4, 1 mM KH2PO4, 5 mM Ca(NO3)2, 50 lM FeNa2(EDTA)2, 50 lM H3BO3, 10 lM MnC12, 0.8 lM ZnSO4, 0.4 lM CuSO4, and 0.02 lM (NH4)6MoO24. At the three-leaf stage, the experimental plants were divided into two groups. The first group was treated with NaCl of different concentrations (1.0%, 1.5%, 2.0%, and 2.5%), and the second group was regularly irrigated to serve as control plants. After two days of treatment, the second leaf was harvested for the analysis: a portion of the fresh leaves was used to measure the relative water content immediately after harvesting, and the remaining leaves were frozen at 80 °C. Each sample had three replicates to ascertain the reproducibility of the treatments. 4.2. The measurement of the relative water content (RWC) of the leaves The leaf relative water content (RWC) was evaluated immediately after the leaves were collected (Bajji et al., 2001). The RWC was estimated using the following formula: RWC = (FW  DW)/ (TW  DW)  100, where FW is the weight of the freshly collected material, TW is the weight after rehydration for 24 h at 4 °C in the dark, and DW is the weight after drying in an oven at 60 °C for 48 h. The final RWC was the mean value taken from four individual plants. 4.3. The protein extraction Approximately 400 mg of leaves were ground in liquid nitrogen, suspended in 3 mL extraction buffer containing 5 M/L Urea, 2 M/L thiourea, 2% (w/v) SDS, 2% (v/v) Triton-114, and 2 lg/lL DTT (freshly added), incubated at 4 °C for 2 h, and centrifuged for 15 min at 13,000 rpm at 4 °C. The supernatants were transferred to new tubes. This step was repeated twice, and the proteins in the supernatant were precipitated by adding four volumes of cold (20 °C) acetone at incubating at 20 °C overnight, followed by centrifugation for 15 min at 13,000 rpm at 4 °C. The resulting precipitate was washed twice with 1 mL ethanol and then three times with chilled (20 °C) acetone containing 0.07% b-mercaptoethanol (ME); centrifugation, at 8000 rpm at 4 °C for 5 min, was performed between the washes. The supernatant was removed, and the pellet was dried at room temperature. After drying, the pellet was resuspended in 100 lL lysis buffer (7 M urea, 2 M thiourea, 4% CHAPS and 40 mM/L Tris) overnight. Prior to quantification, the pH of the protein sample was adjusted to 8.5 by using 1 M Tris-base, as monitored by a pH indicator strip. Lastly, the protein concentration was determined with a 2-D Quant Kit (Amersham Bioscience, USA) using BSA (2 mg/mL) as the standard. The optimal concentration of the protein sample was between 5 and 10 mg/mL. 4.4. Two-dimensional polyacrylamide gel electrophoresis Each protein sample extracted from the control or salt-treated materials was labelled with a ratio of 250 pmol Cy3 or Cy5 protein minimal labelling dye (GE Healthcare) for each 50 lg of protein, according to the manufacturer’s directions. For the gel normalisation, an internal standard was prepared by pooling an equal protein quantity from each of the samples. The pooled mixture of all the samples was mixed with 6 lL of Cy2 and then kept on ice for 30 min in the dark. The reactions were quenched by the addition of 1 lL (Cy3 or Cy5 reactions) or 6 lL (Cy2 reactions) of 10 mM lysine, vortexed, and incubated on ice for 10 min in the dark. The three labelled and quenched samples were combined, the total 150 lg of proteins was mixed and added to the Rehydration buffer (7 M urea, 2 M thiourea, 2% w/v CHAPS, and a trace amount of bromophenol blue) containing 0.4% DTT and 0.5% IPG buffer (GE Healthcare/Amersham Biosciences) to bring the final volume to

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350 lL. To increase the protein quantity needed for the ensuing protein identification, the preparative gels (IEF/SDS–PAGE) were performed using approximately 1 mg of unlabelled protein. Immobiline DryStrips (GE Healthcare, pH 4–7L, 18 cm) were rehydrated passively with 350 lL of the protein solution (as prepared above) for 12 h in a PROTEAN IEF cell (Bio-Rad, USA) and focused for the first-dimension IEF. The electrophoresis conditions were set at 18 °C, with a maximum current setting of 50 mA/strip. The electrophoresis consists of four steps, as follows: step 1–250 V for 30 min, linear; step 2–1000 V for 1 h, rapid; step 3–10,000 V for 5 h, linear; and step 4–10,000 V for 60,000 V-h. After the IEF, the IPG strips were equilibrated for SDS–PAGE in 5 mL equilibration buffer (0.05 M Tris–HCl (pH 8.8), 6 M urea, 30% v/v glycerol, 2% w/v SDS and a trace amount of bromophenol blue) containing 1% DTT for 15 min, followed by a second equilibration step of 15 min with the same equilibration buffer containing 2.5% w/v iodoacetamide. The equilibrated strips were loaded on the top of 12% SDS–polyacrylamide gels and sealed with 0.5% w/v agarose. The SDS–PAGE step was performed at 15 °C in an Ettan Dalt Twelve (Amersham Biosciences) electrophoresis system at 2.5 W/gel for 18 h. 4.5. Image acquisition and data analysis The CyDye-labelled gels were visualised using a Typhoon™ 9400 imager (GE Healthcare/Amersham Biosciences) at a resolution of 100 lm, using the appropriate filters for the excitation and emission wavelengths of each dye (i.e., Cy2-488/520 nm; Cy3-532/580 nm; and Cy5-633/670 nm), according to the manufacturer’s recommendations. The gels were scanned individually. The voltages of the Photo Multiplier Tube (PMT) were adjusted for a maximum image quality with minimal signal saturation and clipping. The images were checked for saturation during the acquisition process using ImageQuant TL software (GE Healthcare/ Amersham Biosciences). Prior to the image analysis, all images were processed using ImageQuant (v.5.2, Amersham Biosciences) and then analysed with the DeCyder software v.6.05 (GE Healthcare/Amersham Biosciences). Using the Batch Processor to perform the spot detection and the inter-gel matching of multiple gel images, the estimated number of spots for each co-detection procedure was set to 2500. The eighteen images obtained from the six gels were divided and designated as Standard, Control, Treat 1 (1.0% NaCl), Treat 2 (1.5% NaCl), Treat 3 (2.0% NaCl) and Treat 4 (2.5% NaCl). The intra-gel analysis was performed using the DeCyder Difference In-gel Analysis (DIA) system, and the inter-gel matching was performed using the DeCyder Biological Variance Analysis (BVA); statistical analyses were carried out for each sample. The spot volume ratios that showed a statistically significant (abundance variation at least 2.0-fold, p < 0.05) difference were processed for further analysis. The spots of interested were excised from the preparative gels. 4.6. Statistical analysis Statistical analyses were carried out on three biological replicates. When analysing the protein abundance, only statistically significant results were considered (one-way analysis of variance (ANOVA), p < 0.05, with control/treated as a factor) and differentially expressed proteins with an absolute ratio of at least 2.0-fold were selected. A principal component analysis (PCA) was performed to separate the gel samples according to their expression variation. The normalised volumes of the 126 proteins with statistically significant differences between the control and treated samples were used for the PCA. A power analysis was also conducted on the 126 proteins to reflect the confidence in the ability of the experimental data to find the differences that actually exist. The

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power is expressed as a percentage, where 80% power is an accepted level. There must be adequate replicates performed to achieve at least 80% of the spots with a power > 0.8. These analyses were performed using Progenesis SameSpots v2.0 (Nonlinear Dynamics). 4.7. The protein identification using LC–MS/MS Selected protein spots were excised from the preparative gels. Each small gel plug was destained with 100 lL of 50% ACN in 50 mM ammonium hydrogen carbonate for approximately 1 h at room temperature. This step was repeated until the gel was colourless. After evaporation of the solvent by vacuum centrifugation, each of the gel plugs was rehydrated with 20 lL of 0.01 mg/mL sequencing-grade modified trypsin (Promega, Madison, WI, USA), and the mixture was agitated at 37 °C overnight (16 h). The supernatants were collected, and the gel pieces were rinsed once with 5% TFA in 50% ACN and then twice with 2.5% TFA in 50% ACN. The supernatants were combined and lyophilised. The lyophilised peptides were dissolved in 5 mg/mL CHCA (a-cynao-4-hydroxycinnamic-acid, Sigma, Germany) in 50% ACN and 0.1% TFA. All of the MS/MS experiments for peptide identification were performed using a nano-LC–MS system, consisting of an ultimate HPLC system and a Q-TOF mass spectrometer (Waters, Milford, MA) equipped with a nano-ESI source. The peptides were subsequently eluted onto an analytical Atlantis C18 column (Waters Corporation) and separated at 1 lL/min with an increasing ACN gradient from 4% to 95% over 50 min. The mobile phase A consisted of 0.1% formic acid in water, and the mobile phase B consisted of 0.1% formic acid in ACN. The mass spectrometer was operated in a positive ion mode with a source temperature of 80 °C and a cone gas flow of 10 L/h. The MS/MS data were processed with MassLynx version 4.0 software (Waters Corporation) to produce a PKL file and analysed with the NCBInr protein sequence database using the Mascot search engine. The following search parameters were used in all of the Mascot searches: tolerance of one missed cleavage; and carbamidomethylation (Cys) and oxidation (Met) as the fixed and variable modifications, respectively. A maximum error tolerance of 100 ppm and a 0.3 Da fragment tolerance were allowed. Acknowledgements This research was financially supported by grants from the National Key Natural Science Foundation of China (30830072), the Chinese Ministry of Science and Technology (2009CB118300) and the National Key Project for Transgenic Crops (2008ZX08002-004 and 2009ZX08002-017B). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.phytochem.2010.12.008. References Abbasi, F.M., Komatsu, S., 2004. A proteomic approach to analyse salt-responsive proteins in rice leaf sheath. Proteomics 4, 2072–2081. Aghaei, K., Ehsanpour, A.A., Komatsu, S., 2008. Proteome analysis of potato under salt stress. J. Proteome Res. 7, 4858–4868. Amme, S., Matros, A., Schlesier, B., Mock, H.P., 2006. Proteome analysis of cold stress response in Arabidopsis thaliana using DIGE-technology. J Exp Bot 57, 1537– 1546. Ashraf, M., Harris, P.J.C., 2004. Potential biochemical indicators of salinity tolerance in plants. Plant Sci. 166, 3–16. Bajji, M., Lutts, S., Kinet, J.M., 2001. Water deficit effects on solute contribution to osmotic adjustment as a function of leaf ageing in three durum wheat (Triticum durum Desf.) cultivars performing differently in arid conditions. Plant Sci. 160, 669–681.

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