Comparative Proteomic Analysis of Proteins in Response to Simulated

ARTICLE pubs.acs.org/jpr

Comparative Proteomic Analysis of Proteins in Response to Simulated Acid Rain in Arabidopsis Ting-Wu Liu,† Bin Fu,† Li Niu,† Juan Chen,† Wen-Hua Wang,† Jun-Xian He,‡ Zhen-Ming Pei,†,§ and Hai-Lei Zheng*,† †

Key Laboratory for Subtropical Wetland Ecosystem Research of Ministry of Education, School of Life Sciences, Xiamen University, Xiamen, Fujian 361005, People's Republic of China ‡ State Key Laboratory of Agrobiotechnology and School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, People's Republic of China § Department of Biology, Duke University, Durham, North Carolina 27708, United States

bS Supporting Information ABSTRACT: A proteomic study using 2-D gel electrophoresis and MALDI-TOF MS was performed to characterize the responses of Arabidopsis thaliana plants to simulated acid rain (SiAR) stress, which is a global environmental problem and has become a serious issue in China in recent years. The emphasis of the present study was to investigate the overall protein expression changes when exposed to SiAR. Out of over 1000 protein spots reproducibly resolved, 50 of them changed their abundance by at least 2-fold. Analysis of protein expression patterns revealed that a set of proteins associated with energy production, metabolism, cell rescue, cell defense and protein folding, etc., could play important roles in mediating plant response to SiAR. In addition to this, some proteins involved in stress responses and jasmonic acid pathway are also involved in plant response to SiAR. More interestingly, the expression of several ubiquitination-related proteins changed dramatically after 32-h SiAR treatment, suggesting that they may act as a molecular marker for the injury phenotype caused by SiAR. Based on our results, we proposed a schematic model to explain the mechanisms associated with the systematic response of Arabidopsis plants to SiAR. KEYWORDS: 2-DE, simulated acid rain, Arabidopsis, plant proteomics, metabolism pathways

’ INTRODUCTION With the rapid economic growth in China, environmental problems such as acid rain become more and more serious. Over the past few decades, the dieback of branches in the upper canopy caused by acid rain has been observed widely in southern China, which resulted in the decline of a large area of forest.1,2 To date, many publications have demonstrated that acid rain causes a series of damages to plants, which includes necrosis, thin crown, premature abscission, branch dieback, etc.35 Furthermore, it destroys the cell membrane system and influences the respiration and photosynthesis,6 which will finally cause disorders in metabolism of glucose, lipids and amino acids.7 At the molecular level, Lee et al.8 found that Arabidopsis leaves treated with simulated acid rain (SiAR) showed similar phenotypes to necrotic lesions caused by biotic stresses like Pseudomonas infiltration. To address the molecular mechanism of the plant response to SiAR, they analyzed the correlations between gene expression and SiAR treatment by Northern blot analysis. They claimed that the up-regulation of some genes induced by SiAR r 2011 American Chemical Society

was related to salicylic acid pathway. In addition, using cDNA chip analysis, Kim et al.9 found that several defense and stress (wounding, pathogen) related genes were also changed after 2 h of acid rain treatment. Obviously, plant response to SiAR stress is a complex set of traits that involves changes in morphological, physiological as well as cellular processes. However, the detailed molecular mechanisms underlying plant tolerance to SiAR remain largely unknown. None of the studies mentioned above offered insights into the quality and quantity of the final gene products, that is, proteins. The recent development of global approaches, such as proteomics, has emerged as a powerful tool for gaining insight into physiological changes at the cellular level, making possible a deeper exploration of the function and regulation of the plant response to environmental changes.10,11 Comparative proteomics has been successfully applied for systematic scrutiny of Received: January 19, 2011 Published: March 07, 2011 2579

dx.doi.org/10.1021/pr200056a | J. Proteome Res. 2011, 10, 2579–2589

Journal of Proteome Research proteins in several plant species under a wide range of abiotic challenges, including salt stress,10,12 drought,1315 high or low temperature,1618 and heavy metals.19 Therefore, in the present study, we used a quantitative proteomics approach to identify global protein expression changes of Arabidopsis involved in SiAR response. Using this powerful tool, we observed extensive changes of protein expressions after SiAR treatment. In addition, we used real-time PCR (RT-PCR) to test the consistency among protein and gene expression profiles. A comprehensive inventory of SiAR responsive proteins was established. These data would provide systemwide information for metabolic responses of plants to acid rain stress.

’ MATERIALS AND METHODS Plant Material

Seeds of Arabidopsis thaliana, ecotype Columbia-0 (Col-0) were planted in the mixture matrix with vermiculite and cover soil (2:1) after vernalization. Plants were grown in an environmentcontrolled growth chamber with a light/dark regime of 16/8 h, temperature of 23/20 °C and a light intensity of 150 μmol m2 s1 photosynthetically active radiation (PAR). After 3 weeks, the plants were sprayed with control solution (pH 5.6) or SiAR (pH 3.0) at the rate of 5 mL per plant. The ion compositions of the control solution were adopted from Fan et al.,5 while the SiAR solution was made from control solution and the pH was adjusted with a mixture of H2SO4 and HNO3 in the ratio of 5 to 1 by chemical equivalents according to Fan et al.5 which represents the average ion compositions of rainfall in South China. The final concentrations of H2SO4 and HNO3 in the spray solution were 0.45 and 0.09 mM, respectively. The leaves that only show the damage phenotype were collected after SiAR treatment at 8, 32, 68, and 116 h. Approximately 1 g leaves from 50 plants were harvested for each sample, and they were immediately frozen in liquid nitrogen (N2) and stored at 70 °C for subsequent protein/RNA extraction and enzyme assays. Each experiment was repeated at least three times. Necrosis Percentage Calculation, Pigment Analysis and Chlorophyll Fluorescence Measurements

For each time point, we randomly selected 25 leaves that emerged the necrosis after SiAR treatment and flattened them on a clean background. Leaves were photographed with a digital camera at an image resolution of 3072 2304, and necrotic spots and total leaf area were quantified using the Adobe Photoshop 7.0 software (Adobe Systems Inc., San Jose, CA). The visible necrosis percentage was expressed as the ratio of necrotic area to total leaf area as described previously.20 Each treatment contains three biological replicates. We used the average of the three replicates as the final necrosis percentage. The chlorophylls in leaves were extracted in 80% v/v acetone. Absorption of the extract was measured as described.21 Ten leaves from each time point were selected for chlorophyll fluorescence measurements using the Portable Chlorophyll Fluorometer (PAM-2100 Walz, Effeltrich, Germany). First, the minimal fluorescence yield (Fo) and the maximal fluorescence yield (Fm) were measured from leaves that were placed in the dark for 30 min. Then, the leaves were exposed to about 150 μmol m2 s1 light intensity at the measured time. Finally, fluorescence yields including minimal fluorescence (Fo0 ) and maximal fluorescence (Fm0 ) were measured. A saturating white

ARTICLE

light pulse of 8000 μmol m2 s1 was applied for 0.8 s when Fm and Fm0 were measured. Measurements were carried out from 08:00 to 09:30. The calculation formulas were as follows: Fv/Fm = (Fm Fo)/Fm; photochemical quenching coefficient (qP) = (Fm0 Fs)/Fv0 . Determination of H2O2 and MDA

Hydrogen peroxide (H2O2) levels were determined according to Sergiev et al.22 with a little modification. Briefly, leaf tissues (1 g) were powdered with liquid N2 and then homogenized in ice bath with 5 mL 0.1% (w/v) trichloroacetic acid (TCA). The homogenate was centrifuged at 12 000 g for 15 min and 0.5 mL of the supernatant was added to 1 mL of potassium phosphate buffer (10 mM, pH 7.0) and 1 mL KI (1 M). The absorbance of supernatant was read at 390 nm. Lipid peroxidation in leaves was estimated by the content of malonaldehyde (MDA) which was measured according to Wang et al.23 Leaf material (1 g) was ground in liquid N2 and then homogenized in 5 mL of 0.1% (w/v) TCA solution. The homogenate was centrifuged at 10 000 g for 20 min and 0.5 mL of the supernatant was added to 1 mL of 0.5% (w/v) thiobarbituric acid (TBA) in 20% TCA. The mixture was incubated in boiling water for 30 min, and the reaction was terminated by placing the reaction tubes in an ice bath. Then the samples were centrifuged at 10 000 g for 5 min and the absorbance of supernatant was read at 532 nm. The value for nonspecific absorption at 600 nm was subtracted. Protein Extraction and 2-DE Anaysis

Total proteins were extracted by the phenol procedure.24 The final washed pellets were air-dried and dissolved with lysis buffer (8 M urea, 2 M thiourea, 4% CHAPS, 1% DTT, 1% IPG Buffer pH 47) at room temperature. Protein concentration was determined according to Gallardo et al.25 Two-dimensional electrophoresis was carried out according to Bjellqvist et al.26 The sample containing 500 μg protein were loaded onto an IPG strip holder with dry IPG strips (18 cm long, pH 47 linear) and rehydrated for 18 h at room temperature. Isoelectric focusing was carried out with an Ettan IPGphor system (GE Healthcare Amersham Bioscience, Little Chalfont, U.K.) as follows: 300 V for 1 h, 600 V for 1 h, 1000 V for 1 h, a gradient to 8000 V for 2 h, and kept at 8000 V for 64000 V 3 h. Focused strips were then equilibrated using an equilibration solution (6 M urea, 30% glycerol, 2% SDS, 50 mM Tris-HCl, pH 8.8, and 1% DTT) for 15 min, followed by the same equilibration solution (2.5% iodoacetamide instead of DTT) for 15 min. The separation of proteins in the second dimension was performed on 12.5% SDS polyacrylamide gels. Each separation was repeated 3 times to ensure the protein pattern reproducibility. Gel Staining, Imaging and Data Analysis

The SDS-PAGE gels were stained by the CBB R250. 2-DE gels were scanned at 600 dots per inch (dpi) resolution with a scanner (Uniscan M3600, China). 2-D gel analysis was performed by PDQuest software (Version 7.0, Bio-Rad). For each gel, a set of three images was generated, corresponding to the original 2-D scan, the filtered image, and the Gaussian image. The Gaussian image containing the three-dimensional Gaussian spots was used for the quantification analysis. The intensity of each protein spot was normalized relative to the total abundance of all valid spots. After normalization and background subtraction, a matchset was created by comparing the control gels. The protein spots that changed more than 2-fold and passed the Student’s t 2580

dx.doi.org/10.1021/pr200056a |J. Proteome Res. 2011, 10, 2579–2589

Journal of Proteome Research test (p < 0.05) were selected and then identified by MALDITOF MS. In-gel Protein Digestion and Protein Identification

Gel slices were destained with a 1:1 v/v solution of methanol and 50 mM NH4HCO3 for at least 3 times until the color of Coomassie was removed and then washed several times with water and completely dried in a vacuum centrifuge. Depending on protein amount, 23 μL of 0.1 mg μL1 modified trypsin (Promega, sequencing grade) in 25 mM NH4HCO3 was added to the dehydrated gel spots. After 30 min incubation, 7 μL of 25 mM NH4HCO3 were added to submerge the gel spots at 37 °C overnight. After digestion, the protein peptides were collected and vacuum-dried. 0.5 μL peptide mixture was mixed with 0.5 μL matrix solution (R-cyano-4-hydroxycinamic acid at saturation in 60% ACN/0.1% TFA v/v). A total of 1 μL of reconstituted in-gel digest sample was spotted initially on Anchorchip target plate. The dried sample on the target plate was washed twice with 1 μL of 0.1% TFA, left for 30 s before solvent removal. MALDI-TOF MS analysis (ReFlexTMIII, Bruker) was used to acquire the peptide mass fingerprint (PMF). Standard peptide mixture was spotted adjacent to all samples for external calibration followed by internal mass correction using peptide ions generated by trypsin autoprotolysis (m/z 842.5, and m/z 2211.10). The spectra were analyzed with the flexAnalysis software (Version 3.2, Bruker-Daltonics). Then, the measured tryptic peptide masses were transferred through MS BioTool program (Bruker-Daltonics) as inputs to search against the taxonomy of Arabidopsis thaliana (thale cress) in NCBI (NCBInr, 12 491 000 entries, downloaded on April 14, 2010) database. The PMF searched parameters were 100 ppm tolerance as the maximum mass error, MHþ monoisotopic mass values, allowance of oxidation (M) modifications, allowed for one missed cleavage, and fixed modification of cysteine by carboxymethyl (Carbamidomethylation, C). The match was considered in terms of a higher Mascot score, the putative functions, and differential expression patterns on 2-DE gels. Some criteria were used to assign a positive match with a known protein. These are as follows: (i) At least four different predicted peptide masses needed to match the observed masses for an identification to be considered valid. (ii) The coverage of protein sequences by the matching peptides must reach a minimum of 10%. (iii) The score that was obtained from the analysis with Mascot software indicates the probability of a true positive identification and must be at least 60. The positive matches were BLAST searched against the UniPort database (http://www. uniprot.org) and/or NCBI protein database (http://www.ncbi. nlm.nih.gov) for updated annotation and identification of homologous proteins. Protein Classification and Hierarchical Cluster Analysis

The identified proteins were searched with the UniProt and TAIR database to find out if their functions are known, then they were further classified using Functional Catalogue software (http://mips.gsf.de/projects/funcat). Hierarchical clustering of the expression profiles was performed on the log transformed fold induction expression values across protein spots affected by SiAR using Cluster software (version 3.0). Input data for preprocessing was calculated by dividing percent volume of each protein spot at SiAR treatment by percent volume of the same protein spot at the control condition.

ARTICLE

RNA Extraction and Gene Expression Analyses by Quantitative Real-time PCR

Total RNA was extracted from Arabidopsis leaves using the TRIZOL Reagents (Invitrogen Inc., Carlsbad, CA) according to the manufacturer’s instructions. Agarose gel electrophoresis was used to confirm RNA integrity and quality. The RNA was reverse transcribed to produce cDNAs using AMV First-Strand cDNA synthesis kit (Invitrogen Inc., Carlsbad, CA) and the cDNA mixture was used as templates for subsequent PCRs. The degenerate oligo nucleotide primers corresponding to the highly conserved amino acid sequence of diverse genes obtained from GenBank were synthesized. The PCR to amplify the core fragment was performed with degenerate primers using Ex Taq HS DNA polymerase (Takara Bio Inc., Japan) and 0.2 mM dNTP in a final volume of 20 μL, according to the manufacturer’s protocol. Gene-specific primers for quantitative real-time PCR are shown in Supplemental Table S1 (Supporting Information). Each run of real-time PCR included standard dilutions and negative reaction controls. The 18S rRNA was used as a housekeeping gene, measured in parallel for each sample. The results of the mRNA expression level of genes were expressed as the normalized ratio using the ΔΔCt method according to Livak and Schmittgen.27 Ct values of each target gene were calculated by Rotor-Gene 6000 Application Software (Version 1.7), and the ΔCt value of the 18S rRNA gene was treated as an arbitrary constant for analyzing the ΔΔCt value of samples. Statistical Analysis

Values in figures and tables were expressed as means ( SE. The statistical significance of the data was analyzed using a univariate analysis of variance (p < 0.05) (one-way ANOVA; SPSS for Windows, version 11.0).

’ RESULTS Effects of SiAR on Physiological Parameters of Arabidopsis

SiAR damaged the cuticles of the epidermis by direct contact with plant surfaces and caused necrosis and chlorosis in leaves, leading to the depressed photosynthesis due to the loss of chlorophyll from the leaf mesophyll. As shown in Figure 1A, the phenotype of leaf damages has changed significantly between 32 and 68 h of SiAR treatment. The area of necrosis had a further increase by 116 h (Figure 1B). Likewise, there was also a gradual decline of total chlorophyll pigments during the SiAR treatment (Figure 1C). Consistently, two chlorophyll fluorescence parameters, including photochemical quantum yield of PSII (Fv/Fm) and photochemical quenching (qP) showed a remarkable decreased (Figure 1D and E), indicating that the photosynthetic efficiency was inhibited by SiAR, leading to decrease in total biomass (Supplement Figure S1, Supporting Information). Moreover, we measured the content of H2O2 as an important composition of reactive oxygen radicals (ROS) and found it was greatly induced by SiAR treatment (Figure 1F), suggesting an excessive ROS enrichment in the cell after exposure to SiAR. In addition, an increase in MDA content was also observed in our experiment (Figure 1G). Differentially Expressed Proteins under SiAR Treatment

To explore the proteomic changes during SiAR treatment, we analyzed the protein expression patterns using 2-DE. The representative images are presented in Figure 2. Proteome was established over the isoelectric point (pI) range from 4 to 7 and molecular weight (MW) range from 12 to 110 kDa (Figure 2 A). Approximately more than 1000 proteins could be resolved 2581


Journal of Proteome Research

ARTICLE

Figure 1. Morphological and physiological changes of Arabidopsis under simulated acid rain (SiAR, pH 3.0) stress. (A) Injury phenotype of Arabidopsis leaves and (B) quantification results using the Adobe Photoshop CS2 software. (C) Total chlorophyll content. (D) Quantum efficiency of open PSII centers in a dark-adapted state (Fv/Fm). (E) Photochemical quenching coefficient (qP) in a light-adapted state. (F) H2O2 content and (G) MDA content of Arabidopsis. The leaves were collected after SiAR treatment for 8, 32, 68, and 116 h, respectively.

Figure 2. 2D gel analysis of proteins extracted from Arabidopsis leaves. Molecular weight (MW) in kilodaltons and pI of proteins are indicated on the left and top of the gel, respectively. (A) Representative 2-DE gels of Arabidopsis in which 50 spots showing at least 2-fold changes (p < 0.05) under SiAR stress were identified by MALDI-TOF MS. (B) Close-up view of some differentially expressed protein spots.

reproducibly on each gel for control and SiAR treatments from 8 to 116 h (Supplemental Figure S2, Supporting Information). While the global pattern of proteins largely remained unaltered,

50 proteins were reproducibly detected as changed more than 2-fold after SiAR treatment (Table 1 and Figure 2A). Close-up views of several protein spots are shown in Figure 2B. Further 2582



ARTICLE

Table 1. Identification of Acid Rain Stress-Responsive Proteins in Arabidopsis thero. spota NCBI accessionb

protein identityc

exper.

kDa/pId kDa/pIe

SCf

MP/TPg

M score

protein function

1

gi|18397283

Carbamoyl phosphate synthetase B (CARB)

131/5.53 138/5.32

12%

12/21

78

Metabolism

2

gi|18086370

Esterases and lipases

107/6.0 105/5.41

26%

16/49

98

Post-translational

3

gi|18407921

Lipoxygenase 1 (LOX1)

103/5.43 102/5.22

37%

26/47

201

4

gi|37785629

Phosphoenolpyruvate carboxylase (PEPc)

110/5.57 100/5.76

18%

14/44

86

5

gi|30696056

Translation elongation factor 2-like protein (LOS1)

95/5.89 95/6.43

34%

20/31

170

Transcription

6

gi|15241102

Heat shock protein 814 (HSP814)

80/4.96 78/5.02

26%

15/29

137

Post-translational modification

7

gi|15241844

Luminar binding protein 1 (BIP1)

74/5.08 76/5.20

24%

15/30

129

modification Defense related Energy


8

gi|30693966

Luminar binding protein (BIP)

68/5.18 78/5.17

14%

9/20

86


9

gi|22326669

GTP-binding protein (LepA)

76/6.30 68/5.54

27%

11/36

77

Transcription

10

gi|18411711

Transketolase

80/5.94 74/5.61

26%

14/52

66

Metabolism

11

gi|18423214

Heat shock protein 93-V (CLPC)

104/6.36 87/5.74

16%

10/12

110

12

gi|15223226

Phosphoglucomutase

63/5.56 65/6.04

41%

18/35

176

13

gi|15242570

Disease resistance protein (NBS-LRR)

117/6.37 61/6.04

11%

12/27

70

Defense related

14

gi|16209658

Beta-catenin-like repeats

64/6.17 64/6.21

23%

8/27

60

Function unknown

15

gi|15238686

Cobalamin-independent methionine synthase (ATCIMS) 85/6.09 70/6.58

31%

11/16

134

Metabolism

16

gi|30686280

AICARFT/IMPCHase bienzyme family protein

65/6.46 57/6.32

31%

12/35

85

Metabolism

17

gi|42563127

U-box domain-containing protein

70/6.61 60/6.33

17%

7/21

64

Post-translational

Post-translational modification Metabolism

18

gi|30698088

Asparagine synthetase 2 (ASN2)

66/6.01 60/6.39

20%

8/22

67

modification Metabolism

19

gi|15228692

tRNA synthetase class II family protein

61/6.09 54/6.39

41%

18/68

112

Transcription

20

gi|13605680

Myrosinase

62/7.11 57/6.71

33%

15/30

180

Metabolism

21

gi|18412104

Identical to Beclin-1-like protein

59/5.64 60/4.69

18%

7/14

73

Function unknown

22

gi|18422193

Variegated 1 (VAR1)

75/5.37 64/5.11

16%

9/27

70

Energy

23

gi|3962377

Heat shock protein 70 (HSP70)

71/5.15 67/5.24

31%

13/21

135


24

gi|18396719

Chaperonin

59/5.25 60/5.38

26%

12/23

95


25

gi|75171219

Unknown protein

44/4.72 55/4.58

18%

5/8

26

gi|15232408

Fructose-1,6-bisphosphatase (FBP1)

46/5.25 54/4.79

32%

10/16

123

65

Function unknown

27

gi|30685069

Structural constituent of cytoskeleton (ACTIN2)

41/5.44 51/5.03

34%

7/28

61

Defense related

28

gi|18410256

Unknown protein

40/5.12 37/5.22

32%

6/16

75

Function unknown

29

gi|58177602

ANNAT1 membrane-binding proteins (ANNAT1)

36/5.21 36/5.28

50%

14/22

133

30

gi|15240599

Transcription factor (TFIIB)

35/5.08 34/5.21

13%

4/4

65

Transcription

31 32

gi|30678350 gi|15810219

Carbonic anhydrase 1 (CA1) Lactoylglutathione lyase

38/5.74 33/5.32 32/5.11 29/5.08

22% 28%

5/19 8/22

62 87

Energy Metabolism

33

gi|15236768

Fructose-bisphosphate aldolase

39/5.65 39/5.77

24%

7/13

73

Energy

34

gi|61679812

Thiamine biosynthetic enzyme, THI

30/5.88 30/5.72

40%

10/29

84

Metabolism

35

gi|15225839

20S proteasome alpha subunit G1 (PAG1)

28/5.93 27/6.01

29%

7/38

67

Energy

Defense related


36

gi|15219041

Arabidopsis pumilio 20

36/9.68 32/6.01

25%

7/24

60

Transcription

37

gi|30687359

Mutase family protein

37/6.67 33/6.04

34%

9/26

74

Energy

38 39

gi|2373399 gi|30678347

Vegetative storage protein (VSP) Carbonic anhydrase 1 (CA1)

30/6.17 27/6.81 30/5.54 26/6.55

38% 42%

8/21 9/20

90 109

40

gi|15218639

Glutathione S-transferase (ATGSTF7)

24/6.14 24/6.67

51%

8/15

88

41

gi|15223576

Dehydroascorbate reductase (DHAR1)

24/5.56 26/5.57

38%

6/11

73

Antioxidant

42

gi|7525041

Ribulose-1,5-bisphosphate carboxylase/oxygenase large

53/5.88 25/5.88

17%

10/24

88

Energy

43

gi|30690246

Uridine monophosphate (UMP) kinase

23/5.79 23/5.87

35%

5/14

65

Metabolism

Defense related Energy Antioxidant

subunit (Rubsico)

2583



ARTICLE

Table 1. Continued thero. spota NCBI accessionb

protein identityc

exper.

kDa/pId kDa/pIe

SCf

MP/TPg

M score

protein function

44

gi|92090800

Thylakoid lumenal 19 kDa protein

25/6.92 21/5.93

45%

7/19

91

Function unknown

45

gi|15223275

Mlp-like protein 43 (MLP43)

18/5.54 21/5.42

42%

6/12

69

Defense related

46

gi|15221646

Mlp-like protein 423 (MLP423)

17/5.10 14/5.19

46%

5/6

93

Defense related

47

gi|79324722

Nuclear factor Y subunit B1 (ATHAP3)

13/5.19 14/5.28

34%

4/5

69

Transcription

48

gi|30699033

Gibberellin-responsive protein

12/9.00 11/5.02

41%

4/5

64

Defense related

49

gi|15236327

Arabidopsis thioredoxin M-type 2 (ATHM2)

21/9.35 13/4.86

34%

5/6

90

Antioxidant

50

gi|26453304

Zinc finger family protein

29/5.48 12/4.84

18%

13/29

64

Transcription

a Spot No. is the unique differentially expressed protein spot number which refers to the labels in Figure 2. b Database accession numbers according to NCBInr. c Name and functional categories of the proteins identified by MALDI-TOF MS. d Theoretical mass (kDa) and pI of identified proteins. e Experimental mass (kDa) and pI of identified proteins. f Amino acid sequence coverage for the identified proteins. g Number of the matched peptides and the total searched peptides. h Mascot searched score against the database NCBInr.

Figure 3. Outline of functional classification of the identified proteins. Each identified protein listed in Table 1 was functionally classified according to their known and putative functions. The proportion of identities in each functional category was the sum of the proportion of all identities.

inspection of the gel patterns revealed that the MW and/or pI values of the spots differed from their theoretical values. This could be due to post-translational modifications such as glycosylation, phosphorylation and cleavage that can alter protein MW and/or charge. Functional Classification of Differentially Expressed Proteins

Furthermore, a total of 50 proteins were positively identified by MALDI-TOF MS and listed in Table 1 (more details of identified proteins were showed in Supplemental Table S2, Supporting Information). Among them, 45 proteins have assigned functions (Figure 3). These proteins were classified into 7 groups based on their biochemical functions (Table 1, Figure 3). The majority of the protein profile was metabolism-associated proteins, followed by post-translational modification proteins and the energy production and conversion related proteins. In addition, defense mechanism associated proteins also took up a great part of identified proteins and three antioxidant related proteins were also found in our experiment (Figure 3, Table 1). Protein Clustering Revealed the Dynamics of Protein Networks under SiAR Treatment

To gain information on the biological mechanisms associated with the identified proteins. An important method to find the regulatory mechanism for protein interactions is the application

of hierarchical clustering algorithms as similar as that used in cDNA microarray or gene chip experiment. With the use of this method, the proteins that appeared on the same branches were assumed to be involved in some way in related biological functions (Figure 4). According to our results, there were mainly 4 change patterns for these proteins. First, the proteins in the first cluster were first down-regulated but later up-regulated by SiAR treatment (spot: 1, 4, 6, 9 and 34). Second, three proteins related to ubiquitination were first upregulated but then became down-regulated under SiAR (spot: 17, 18 and 35). Third, another cluster that contained most photosynthesis-related proteins were down-regulated under SiAR treatment (spot 12, 33, 37 and 42), suggesting that SiAR reduced photosynthetic efficiency, which subsequently inhibited the plant growth and development (Figure 1 and Supplemental Figure S1, Supporting Information). Fourthly, different from the proteins above, we also found many proteins were up-regulated under SiAR treatment, which were involved in amino acid metabolism, detoxification and antioxidation, defense mechanisms, and proteins related to energy metabolism such as enzymes for glycolysis, sucrose cycle and tricarboxylic acid cycle and so on. Expression Analysis of SiAR-Responsive Genes Using Quantitative Real-time PCR

To further confirm the results of proteome, we used quantitative real-time PCR (RT-PCR) to analyze the transcript expression of 8 genes encoding protein spots 3, 4, 20, 23, 24, 33, 40, and 48 which were lipoxygenase 1, phosphoenolpyruvate carboxylase, myrosinase, heat shock protein 70, chaperonin, fructose-bisphosphate aldolase, glutathione S-transferase 7 and gibberellin-responsive protein, respectively, under SiAR treatment. The detailed lists of protein spots and the corresponding primer sequences used in RT-PCR were summarized in the Supplemental Table S1 (Supporting Information). In this comparison, mRNA levels of 5 out of the 8 genes changed in parallel with protein levels, whereas that of the remaining 3 changed independently (Figure 5). The parallel and independent changes between mRNA and protein levels for these 8 SiAR-responsive genes reflected complex regulatory mechanisms for plant responses to acid rain.

’ DISCUSSION SiAR Causes Disorders in Plant Metabolisms Related to Energy Production and Amino Acid Biosynthesis

The substance and energy metabolism are the basic life activities and are vulnerable to the environmental stresses. Large amounts of 2584



ARTICLE

Figure 4. Hierarchical clustering analyses of the expression profiles of the 50 SiAR-responsive proteins. The 4 columns represent protein expression changes under different treatments, including SiAR treatment for 8, 32, 68, and 116 h, respectively. The rows represent the individual proteins identified. On the left is the protein clustering, and on the top is the treatment scheme. The up- or down-regulated proteins are indicated in red or green, respectively. The intensity of the colors increases with the increase of expression differences between the control and treatments.

ATP are needed for plant to provide sufficient energy for growth, development and response to stress.11,28 Consistent with the fact that ATP is mainly produced by the carbohydrate metabolism,29 we found that a large number of proteins related to the carbohydrate metabolism and energy production were up-regulated in our study. For example, phosphoglucomutase (spot 12) and lactoylglutathione lyase (spot 32) are enzymes involved in pentose-phosphate pathway and tricarboxylic acid cycle in carbohydrate metabolism and they were up-regulated by SiAR treatment. It is also well-known that SiAR leads to a remarkable decrease in the efficiency of photosynthesis in plants. The proteomic data from our study confirmed that ribulose-1,5-bisphosphate carboxylase/oxygenase (spot 42, Rubisco) was decreased under SiAR. The expression of carbonic anhydrase (spot 31 and 39, CA) also changed significantly under SiAR stress, implying that photosynthesis was very sensitive to SiAR stress. On the other hand, change in phosphoenolpyruvate carboxylase (spot 4, PEPc) was also identified in the present study. PEPc plays a key role in the interaction between C and N metabolism as well as in plant

responses to low pH.30,31 Therefore, it is not surprising that the expression of PEPc was also up-regulated under SiAR. Fructosebisphosphate aldolase is a very important enzyme in Calvin cycle and plays a crucial role in plant responses to salt stress and other abiotic stimuli.32 In our study, the protein expression of fructosebisphosphate aldolase (spot 33) was down-regulated under SiAR, which is consistent with the mRNA change resolved by microarray studies.9 On the basis of these results, we believed that the Calvin cycle could have been repressed by SiAR stress and that Arabidopsis plants might reduce the efficiency of photosynthesis through altering protein expression of photosynthetic enzymes to cope with the stress caused by SiAR. This notion was supported by our physiological measurements (Figure 1D and E). It is well-known that there are high levels of N- and S-containing compounds in acid rain and that N and S metabolisms can be disturbed by the high N and S input.33 Several important proteins related to amino acid metabolism, such as carbamoyl phosphate synthetase B (spot 1, CARB), Cobalamin-independent methionine synthase (spot 15), asparagine synthetase 2 (spot 18, 2585



Figure 5. Comparison of changes in protein and mRNA levels for selected protein spots. (A) mRNA change patterns. (B) Protein change patterns. For each gene or protein, the expression change that with a 2 was calculated and normalized to 1 via the fit of log2, and we used the log2 values as the relative expression of mRNA or protein in plants treated with SiAR for 8, 32, 68, and 116 h. Spot 3, Lipoxygenase 1; Spot 4, Phosphoenolpyruvate carboxylase; Spot 20, Myrosinase; Spot 23, Heat shock protein 70; Spot 24, Chaperonin; Spot 33, Fructose-bisphosphate aldolase; Spot 40, Glutathione S-transferase; Spot 48, Gibberellin-responsive protein.

ASN2), and uridine monophosphate kinase (spot 43) were identified in our study, suggesting that SiAR caused disorders in amino acid metabolism.34 Moreover, we identified a protein of myrosinase (Spot 20) that was up-regulated under SiAR. Myrosinase is a defense-related enzyme and is capable of hydrolyzing glucosinolates. The glucosinolate/myrosinase system is a major defense system involved in a range of environmental factors in plants.35 The identification of myrosinase protein in our study indicated that the glucosinolate/myrosinase system is also involved in plant responses to SiAR stress. Stress and Antioxidation Related Proteins

In this study, several proteins that were known to be involved in plant responses to biotic and abiotic stresses were identified under SiAR. These include 8 defense-related proteins, 3 coldrelated proteins and 3 antioxidation related proteins. Among the 8 defense-related proteins is the vegetative storage protein (spot 38, VSP), which was previously reported to respond to jasmonic acid (JA) stimulus36,37 and showed dramatically high expression under SiAR stress. This suggests that the JA pathway may play a role in mediating plant responses to SiAR. The 3 cold-related proteins include a lipoxygenase (spot 3), a AICARFT/IMPCHase bienzyme family protein (spot 16), and an unknown protein (spot 25).38,39 The identification of these proteins under both SiAR and cold stress implies that they may function as common regulators of both SiAR and cold responses in plants. In

ARTICLE

fact, a range of studies in Canada have certified that the cold tolerance of red spruce (Picea ruben) is reduced by acid rain and increase the risk of winter injury and crown deterioration.4042 They considered the membrane-bound Ca2þ leaching is one of the key factors in reduction of cold tolerance. In our presented results, a Ca2þ binding protein annexins1 (spot 29) was upregulated by SiAR in Arabidopsis. Annexins act as targets of calcium signals in eukaryotic cells, and recent results suggested that it plays an important role in plant stress responses.43,44 On the basis of the results from red spruce and our results from Arabidopsis, it is reasonable to speculate that Ca2þ and the Ca2þ binding proteins have a good correlation with the acid rain response; however, their roles need to be further studied. The expression of some enzymes related to antioxidation and ROS scavenging in plants was changed by SiAR stress in our study (Figure 1F and G). Dehydroascorbate reductase (spot 41, DHAR) is an important antioxidant that serves as an electron donor and reacts with ROS.45 There is growing evidence that overexpression of DHAR enhances tolerance to environmental stresses in some species.46 Our data showed that the DHAR was up-regulated under SiAR, which could be favorable for plants to cope with SiAR stress. A member of glutathione S-transferase (GST) family proteins (spot 40, GST-7) was up-regulated considerably after exposure to SiAR, indicating that GST functioned in the response of Arabidopsis to SiAR stress. We also identified a member of the thioredoxin family, thioredoxin M-type 2 (spot 49). Thioredoxins (TRXs) are involved in a wide range of cellular redox processes.47 Interestingly, a recent study observed that the thioredoxin M regulates the activity of some enzymes involved in photosynthetic carbon metabolism,48 such as fructose-1,6-bisphosphatase, which was also identified in our experiments. Consistent with our result, Ahsan et al.49 found that the expression of M-type thioredoxin increased upon heat stress and they claimed that thioredoxin-M may be involved in the scavenging of heat-induced ROS and protection of the chloroplast structure from thermal stress. Taken together, these results suggest that under SiAR there is inevitable generation of ROS induced by oxidative damage and that plants have employed the antioxidant defense system to scavenge ROS. Transcription and Translation Related Proteins

Seven proteins that related with gene transcription and protein translation were identified in our experiments, which include a translation elongation factor 2-like protein (spot 5), GTP-binding protein LepA (spot 9), tRNA synthetase class II family protein (spot 19), a transcription factor (spot 30), Arabidopsis pumilio 20 (spot 36), nuclear factor Y subunit B1 (spot 47) and a zinc finger family protein (spot 50). Transcriptional and translational control of the expression of stress responsive genes is a crucial part of the plant response to various abiotic and biotic stresses.17 For instance, GTP-binding proteins were reported to play an important role in signal transduction evoked by environmental stress.18 SiAR stress seems to increase the synthesis of GTP-binding proteins leading to increase in kinases activities. Using gene chip analysis, Kim et al.9 identified 4 transcription factor-related genes that were up-regulated by acid rain treatment in Arabidopsis. The induction of transcription factor-related proteins in our study suggests that plants induced transcription of some genes in response to SiAR. Post-translational Modification Related Proteins

Heat shock proteins (HSPs) are known as a big family that are induced by a wide variety of stresses and play important roles in 2586


Journal of Proteome Research helping protein folding and stabilization by preventing protein aggregation and protecting cells against a lot of stresses.50,51 We observed that SiAR caused a marked increase in the expression of several HSPs including HSP81-4 (spot 6), HSP70 (spot 23), chaperonin (spot 24), and chloroplast HSP93-V (spot 11). We also found that the expression of two luminal-binding protein precursors (spots 7 and 8, Bip), which are the molecular chaperones of HSP family, were increased dramatically under SiAR, suggesting that HSPs play an important role in the response of Arabidopsis to SiAR. U-box protein, as well as other ubiquitination related proteins involved in the regulation of protein degradation by the 26 S proteasome.52 Here, we found two ubiquitination related proteins including spot 17 and 35 which were U-box protein and component of the 20 S proteasome subunit, respectively, were first upregulated but then down-regulated between 32 and 68 h of SiAR treatment (Figure 4). On the other hand, acid rain caused the leaf necrosis has been well documented.5,8 Considering that a dramatic leaf injury phenotype also occurred in 3268 h SiAR treatment (Figure 1), we assumed that the control of protein degradation by the ubiquitin/proteasome pathway is likely to play an important role in response to SiAR in Arabidopsis. Comparative Analysis of Transcriptome Data and Proteome Data

Comparative genomics study of Arabidopsis under acid stress has been carried out using an Arabidopsis cDNA microarray.9

ARTICLE

The study identified 54 genes in Arabidopsis induced by SiAR treatment for 2 and 12 h. Compared to the microarray data, we only detected 2 out of the 54 genes whose mRNA expression changed consistently with the protein changes from our study. In view of the discrepancy of the treatments and growth conditions of plants, we used real-time PCR to further check the mRNA expression of selected proteins. The poor correlation was also found between the RT-PCR and proteomics results (Figure 5). These results support the conclusion from other researchers that, in statistical terms, measurements of mRNA are not always correlated with protein abundance.28,53,54 The fact that parallel and independent relations exist between mRNA and protein levels among each stress-responsive genes implies the existence of a fairly complex regulatory network such as mRNA stability, splicing, translational regulation, post-translational processing control of protein turnover, protein degradation or a combination of effect in plant cell should be considered.

’ CONCLUDING REMARKS By using a comparative proteomic strategy, we provided for the first time an overview of the systematic mechanism by which Arabidopsis plants respond to SiAR stress. Quantitative analysis of more than 1000 highly reproducible proteins on 2-DE profiles identified 50 proteins with significant change in response to SiAR stress. These proteins were classified into 7 functional groups

Figure 6. Schematic model of response mechanism in Arabidopsis under SiAR stress. The abbreviations used in the figure were consistent with Table 1. The proteins were analyzed by WoLF PSORT (http://wolfpsort.org/) to predict their subcellular location. 2587


Journal of Proteome Research including metabolism and post-translational modification related proteins followed by energy production and conversion and defense mechanisms associated proteins. In addition, the proteins involving antioxidation were also an important part in the identified proteins. The above-mentioned results reveal that a set of proteins associated with energy production, metabolism, cell rescue, cell defense and protein folding, etc. play important roles in plant response to SiAR. In particular, some stress responsive proteins that are associated with heat shock, oxidative stress and other stress response, especially cold stress, changed dramatically under SiAR treatment. Furthermore, we found that some ubiquitination related proteins, which mediated the ubiquitin/ proteasome pathway, also play important roles in the response of Arabidopsis to SiAR treatment. On the basis of previous reports and our results, we put forward a schematic model of mechanism associated with the systematic response to SiAR in Arabidopsis (Figure 6).

’ ASSOCIATED CONTENT

bS

Supporting Information Figure S1. (A) The representative photos of intact plants treated with SiAR for 8, 32, 68, and 116 h. (B) Biomass changes of Arabidopsis after SiAR treatment for 116 h. Figure S2. 2-D gel maps of total leaf proteome of Arabidopsis leaves treated with SiAR for 8, 32, 68, and 116 h, respectively. Table S1. Primer pairs used in this study for Real-time PCR experiment. Table S2. Details of identified proteins and peptide list of each protein in Arabidopsis under acid rain stress. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Hai-Lei Zheng, School of Life Sciences, Xiamen University, Xiamen, Fujian 361005, P.R. China. Tel: þ86 592 218 1005. Fax: þ86 592 218 1015. E-mail: [email protected].

’ ACKNOWLEDGMENT We are grateful to Mr. Sieh Sorie Kargbo and Xiao-Qian Yi for critically editing the manuscript. This work was supported by the National Natural Science Foundation of China (NSFC No 30930076, 30770192, 30670317), the Foundation of the Chinese Ministry of Education, and Program for New Century Excellent Talents in Xiamen University (NCETXMU No X07115) to H.-L. Zheng and the Changjiang Scholarship to Z.-M. Pei, and the Research Grant Council of the Hong Kong Special Administrative Region, China (465009, 465410) to J.-X. He. ’ REFERENCES (1) Likens, G. E.; Bormann, F. H.; Johnson, N. M. Acid rain. Environment 1972, 14, 33–40. (2) Likens, G. E.; Driscoll, C. T.; Buso, D. C. Long-term effects of acid rain: response and recovery of a forest ecosystem. Science 1996, 272, 244–246. (3) Larssen, T.; Lydersen, E.; Tang, D. G.; He, Y.; Gao, J. X.; Liu, H. Y.; Duan, L.; Seip, H. M.; Vogt, R. D.; Mulder, J.; Shao, M.; Wang, Y. H.; Shang, H.; Zhang, X. S.; Solberg, S.; Aas, W.; Okland, T.; Eilertsen, O.; Angell, V.; Liu, Q. R.; Zhao, D. W.; Xiang, R. J.; Xiao, J. S.; Luo, J. H. Acid rain in china. Environ. Sci. Technol. 2006, 40, 418–425.

ARTICLE

(4) Pawlowski, L. Acidification: its impact on the environment and mitigation strategies. Ecol. Eng. 1997, 8, 271–288. (5) Fan, H. B.; Wang, Y. H. Effects of simulated acid rain on germination, foliar damage, chlorophyll contents and seedling growth of five hardwood species growing in China. For. Ecol. Manage. 2000, 132, 285–285. (6) Kong, F. X.; Liu, Y.; Hu, W.; Shen, P. P.; Zhou, C. L.; Wang, L. S. Biochemical responses of the mycorrhizae in Pinus massoniana to combined effects of Al, Ca and low pH. Chemosphere 2000, 40, 311–318. (7) Kovacik, J.; Klejdus, B.; Backor, M.; Stork, F.; Hedbavny, J. Physiological responses of root-less epiphytic plants to acid rain. Ecotoxicology 2011, 20, 348–357. (8) Lee, Y.; Park, J.; Im, K.; Kim, K.; Lee, J.; Lee, K.; Park, J. A.; Lee, T. K.; Park, D. S.; Yang, J. S.; Kim, D.; Lee, S. Arabidopsis leaf necrosis caused by simulated acid rain is related to the salicylic acid signaling pathway. Plant Physiol. Biochem. 2006, 44, 38–42. (9) Kim, J. K.; Baek, S. A.; Yoon, S.; Park, H. J.; Lee, S. C.; Lee, T. S.; Im, K. H. Global analysis of gene expression upon acid treatment in Arabidopsis thaliana. Plant Pathol. J. 2009, 25, 172–178. (10) Wang, X. C.; Fan, P. X.; Song, H. M.; Chen, X. Y.; Lil, X. F.; Li, Y. X. Comparative proteomic analysis of differentially expressed proteins in shoots of Salicornia europaea under different salinity. J. Proteome Res. 2009, 8, 3331–3345. (11) Ndimba, B. K.; Chivasa, S.; Simon, W. J.; Slabas, A. R. Identification of Arabidopsis salt and osmotic stress responsive proteins using two-dimensional difference gel electrophoresis and mass spectrometry. Proteomics 2005, 5, 4185–4196. (12) Pang, Q. Y.; Chen, S. X.; Dai, S. J.; Chen, Y. Z.; Wang, Y.; Yan, X. F. Comparative proteomics of salt tolerance in Arabidopsis thaliana and Thellungiella halophila. J. Proteome Res. 2010, 9, 2584–2599. (13) Salekdeh, G. H.; Siopongco, J.; Wade, L. J.; Ghareyazie, B.; Bennett, J. Proteomic analysis of rice leaves during drought stress and recovery. Proteomics 2002, 2, 1131–1145. (14) Zhang, M. H.; Li, G. W.; Huang, W.; Bi, T.; Chen, G. Y.; Tang, Z. C.; Su, W. A.; Sun, W. N. Proteomic study of Carissa spinarum in response to combined heat and drought stress. Proteomics 2010, 10, 3117–3129. (15) Ali, G. M.; Komatsu, S. Proteomic analysis of rice leaf sheath during drought stress. J. Proteome Res. 2006, 5, 396–403. (16) Cui, S. X.; Huang, F.; Wang, J.; Ma, X.; Cheng, Y. S.; Liu, J. Y. A proteomic analysis of cold stress responses in rice seedlings. Proteomics 2005, 5, 3162–3172. (17) Amme, S.; Matros, A.; Schlesier, B.; Mock, H. P. Proteome analysis of cold stress response in Arabidopsis thaliana using DIGEtechnology. J. Exp. Bot. 2006, 57, 1537–1546. (18) Majoul, T.; Bancel, E.; Triboi, E.; Ben Hamida, J.; Branlard, G. Proteomic analysis of the effect of heat stress on hexaploid wheat grain: Characterization of heat-responsive proteins from non-prolamins fraction. Proteomics 2004, 4, 505–513. (19) Sarry, J. E.; Kuhn, L.; Ducruix, C.; Lafaye, A.; Junot, C.; Hugouvieux, V.; Jourdain, A.; Bastien, O.; Fievet, J. B.; Vailhen, D.; Amekraz, B.; Moulin, C.; Ezan, E.; Garin, J.; Bourguignon, J. The early responses of Arabidopsis thaliana cells to cadmium exposure explored by protein and metabolite profiling analyses. Proteomics 2006, 6, 2180–2198. (20) Xiao, Q.; Ye, W. J.; Zhu, Z.; Chen, Y.; Zheng, H. L. A simple non-destructive method to measure leaf area using digital camera and Photoshop software. Chin. J. Ecol. 2005, 24, 711–714. (21) Arnon, D. I. Copper enzymes in isolated chloroplasts. Polyphenoloxidase in Beta vulgaris. Plant Physiol. 1949, 24, 1–15. (22) Alexieva, V.; Sergiev, I.; Mapelli, S.; Karanov, E. The effect of drought and ultraviolet radiation on growth and stress markers in pea and wheat. Plant Cell Environ. 2001, 24, 1337–1344. (23) Wang, H. H.; Huang, J. J.; Bi, Y. R. Nitrate reductase-dependent nitric oxide production is involved in aluminum tolerance in red kidney bean roots. Plant Sci. 2010, 179, 281–288. (24) Carpentier, S. C.; Witters, E.; Laukens, K.; Deckers, P.; Swennen, R.; Panis, B. Preparation of protein extracts from recalcitrant 2588


Journal of Proteome Research plant tissues: An evaluation of different methods for two-dimensional gel electrophoresis analysis. Proteomics 2005, 5, 2497–2507. (25) Gallardo, K.; Firnhaber, C.; Zuber, H.; Hericher, D.; Belghazi, M.; Henry, C.; Kuster, H.; Thompson, R. A combined proteome and transcriptome analysis of developing Medicago truncatula seeds. Mol. Cell. Proteomics 2007, 6, 2165–2179. (26) Bjellqvist, B; E., K.; Righetti, P. G.; Gianazza, E; Gorg, A; Westermeier, R; Postel, W Isoelectric focusing in immobilized pH gradients: principle, methodology and some applications. J. Biochem. Biophys. Methods 1982, 6, 317–339. (27) Livak, K. J.; Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(T)(-Delta Delta C) method. Methods 2001, 25, 402–408. (28) Jiang, Y.; Yang, B.; Harris, N. S.; Deyholos, M. K. Comparative proteomic analysis of NaCl stress-responsive proteins in Arabidopsis roots. J. Exp. Bot. 2007, 58, 3591–3607. (29) Agarie, S.; Shimoda, T.; Shimizu, Y.; Baumann, K.; Sunagawa, H.; Kondo, A.; Ueno, O.; Nakahara, T.; Nose, A.; Cushman, J. C. Salt tolerance, salt accumulation, and ionic homeostasis in an epidermal bladder-cell-less mutant of the common ice plant Mesembryanthemum crystallinum. J. Exp. Bot. 2007, 58, 1957–1967. (30) Scheible, W. R.; GonzalezFontes, A.; Lauerer, M.; MullerRober, B.; Caboche, M.; Stitt, M. Nitrate acts as a signal to induce organic acid metabolism and repress starch metabolism in tobacco. Plant Cell 1997, 9, 783–798. (31) Pasqualini, S.; Ederli, L.; Piccioni, C.; Batini, P.; Bellucci, M.; Arcioni, S.; Antonielli, M. Metabolic regulation and gene expression of root phosphoenolpyruvate carboxylase by different nitrogen sources. Plant Cell Environ. 2001, 24, 439–447. (32) Fan, W.; Zhang, Z. L.; Zhang, Y. L. Cloning and molecular characterization of fructose-1,6-bisphosphate aldolase gene regulated by high-salinity and drought in Sesuvium portulacastrum. Plant Cell Rep. 2009, 28, 975–984. (33) Bowman, W. D.; Cleveland, C. C.; Halada, L.; Hresko, J.; Baron, J. S. Negative impact of nitrogen deposition on soil buffering capacity. Nat. Geosci. 2008, 1, 767–770. (34) Gallardo, K.; Job, C.; Groot, S. P. C.; Puype, M.; Demol, H.; Vandekerckhove, J.; Job, D. Importance of methionine biosynthesis for Arabidopsis seed germination and seedling growth. Physiol. Plant 2002, 116, 238–247. (35) Andreasson, E.; Jorgensen, L. B.; Hoglund, A. S.; Rask, L.; Meijer, J. Different myrosinase and idioblast distribution in Arabidopsis and Brassica napus. Plant Physiol. 2001, 127, 1750–1763. (36) Porta, H.; Rocha-Sosa, M. Plant lipoxygenases. Physiological and molecular features. Plant Physiol. 2002, 130, 15–21. (37) Matthes, M. C.; Pickett, J. A.; Napier, J. A. Natural variation in responsiveness of Arabidopsis thaliana to methyl jasmonate is developmentally regulated. Planta 2008, 228, 1021–1028. (38) Bae, M. S.; Cho, E. J.; Choi, E. Y.; Park, O. K. Analysis of the Arabidopsis nuclear proteome and its response to cold stress. Plant J. 2003, 36, 652–663. (39) Goulas, E.; Schubert, M.; Kieselbach, T.; Kleczkowski, L. A.; Gardestrom, P.; Schroder, W.; Hurry, V. The chloroplast lumen and stromal proteomes of Arabidopsis thaliana show differential sensitivity to short- and long-term exposure to low temperature. Plant J. 2006, 47, 720–734. (40) Hawley, G. J.; Schaberg, P. G.; Eagar, C.; Borer, C. H. Calcium addition at the Hubbard Brook Experimental Forest reduced winter injury to red spruce in a high-injury year. Can. J. For. Res. 2006, 2544–2549. (41) DeHayes, D. H.; Schaberg, P. G.; Hawley, G. J.; Strimbeck, G. R. Acid rain impacts on calcium nutrition and forest health-alteration of membrane-associated calcium leads to membrane destabilization and foliar injury in red spruce. Bioscience 1999, 49, 789–800. (42) Halman, J. M.; Schaberg, P. G.; Hawley, G. J.; Eagar, C. Calcium addition at the Hubbard Brook Experimental Forest increases sugar storage, antioxidant activity and cold tolerance in native red spruce (Picea rubens). Tree Physiol. 2008, 28, 855–862. (43) Lee, S.; Lee, E. J.; Yang, E. J.; Lee, J. E.; Park, A. R.; Song, W. H.; Park, O. K. Proteomic identification of annexins, calcium-dependent

ARTICLE

membrane binding proteins that mediate osmotic stress and abscisic acid signal transduction in Arabidopsis. Plant Cell 2004, 16, 1378–1391. (44) Mortimer, J. C.; Laohavisit, A.; Macpherson, N.; Webb, A.; Brownlee, C.; Battey, N. H.; Davies, J. M. Annexins: multifunctional components of growth and adaptation. J. Exp. Bot. 2008, 59, 533–544. (45) Colville, L.; Smirnoff, N. Antioxidant status, peroxidase activity, and PR protein transcript levels in ascorbate-deficient Arabidopsis thaliana vtc mutants. J. Exp. Bot. 2008, 59, 3857–3868. (46) Yoshida, S.; Tamaoki, M.; Shikano, T.; Nakajima, N.; Ogawa, D.; Ioki, M.; Aono, M.; Kubo, A.; Kamada, H.; Inoue, Y.; Saji, H. Cytosolic dehydroascorbate reductase is important for ozone tolerance in Arabidopsis thaliana. Plant Cell Physiol. 2006, 47, 304–308. (47) Gelhaye, E.; Rouhier, N.; Navrot, N.; Jacquot, J. P. The plant thioredoxin system. Cell. Mol. Life Sci. 2005, 62, 24–35. (48) Marchand, C. H.; Vanacker, H.; Collin, V.; Issakidis-Bourguet, E.; Le Marechall, P.; Decottignies, P. Thioredoxin targets in Arabidopsis roots. Proteomics 2010, 10, 2418–2428. (49) Ahsan, N.; Donnart, T.; Nouri, M. Z.; Komatsu, S. Tissuespecific defense and thermo-adaptive mechanisms of soybean seedlings under heat Stress revealed by proteomic approach. J. Proteome Res. 2010, 9, 4189–4204. (50) Wang, W. X.; Vinocur, B.; Shoseyov, O.; Altman, A. Role of plant heat-shock proteins and molecular chaperones in the abiotic stress response. Trends Plant Sci. 2004, 9, 244–252. (51) Timperio, A. M.; Egidi, M. G.; Zolla, L. Proteomics applied on plant abiotic stresses: Role of heat shock proteins (HSP). J. Proteomics 2008, 71, 391–411. (52) Agetsuma, M.; Furumoto, T.; Yanagisawa, S.; Izui, K. The ubiquitin-proteasome pathway is involved in rapid degradation of phosphoenolpyruvate carboxylase kinase for C4 photosynthesis. Plant Cell Physiol. 2005, 46, 389–398. (53) Tian, Q.; Stepaniants, S. B.; Mao, M.; Weng, L.; Feetham, M. C.; Doyle, M. J.; Yi, E. C.; Dai, H. Y.; Thorsson, V.; Eng, J.; Goodlett, D.; Berger, J. P.; Gunter, B.; Linseley, P. S.; Stoughton, R. B.; Aebersold, R.; Collins, S. J.; Hanlon, W. A.; Hood, L. E. Integrated genomic and proteomic analyses of gene expression in mammalian cells. Mol. Cell. Proteomics 2004, 3, 960–969. (54) Yan, S. P.; Zhang, Q. Y.; Tang, Z. C.; Su, W. A.; Sun, W. N. Comparative proteomic analysis provides new insights into chilling stress responses in rice. Mol. Cell. Proteomics 2006, 5, 484–496.

2589


Comparative Proteomic Analysis of Proteins in Response to Simulated

Recommend Documents