Label-Free Quantitative Proteomics Analysis of Cotton Leaf Response

ARTICLE pubs.acs.org/jpr

Label-Free Quantitative Proteomics Analysis of Cotton Leaf Response to Nitric Oxide Yanyan Meng,#,§ Feng Liu,z,§ Chaoyou Pang,# Shuli Fan,# Meizhen Song,# Dan Wang,$ Weihua Li,*,z and Shuxun Yu* ,# #

State Key Laboratory of Cotton Biology, Cotton Research Institute, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan Province, China z National Center of Biomedical Analysis, Beijing, China $ College of Agronomy, Northwest Sci-Tech University of Agriculture and Forest, Yangling, P. R. China

bS Supporting Information ABSTRACT: To better understand nitric oxide (NO) responsive proteins, we investigated the proteomic differences between untreated (control), sodium nitroprusside (SNP) treated, and carboxy-PTIO potassium salt (cPTIO, NO scavenger) followed by SNP treated cotton plants. This is the first study to examine the effect of different concentrations of NO on the leaf proteome in cotton using a label-free approach based on nanoscale ultraperformance liquid chromatography electrospray ionization (ESI)low/high-collision energy MS analysis (MSE). One-hundred and sixty-six differentially expressed proteins were identified. Forty-seven of these proteins were upregulated, 82 were downregulated, and 37 were expressed specifically under different conditions. The 166 proteins were functionally divided into 17 groups and localized to chloroplast, Golgi apparatus, cytoplasm, and so forth. The pathway analysis demonstrated that NO is involved in various physiological activities and has a distinct influence on carbon fixation in photosynthetic organisms and photosynthesis. In addition, this is the first time proteins involved in ethylene synthesis were identified to be regulated by NO. The characterization of these protein networks provides a better understanding of the possible regulation mechanisms of cellular activities occurring in the NO-treated cotton leaves and offers new insights into NO responses in plants. KEYWORDS: nitric oxide, label-free, cotton, leaf, proteomics

’ INTRODUCTION Nitric oxide (NO) is a crucial signaling molecule in mammalian cells that was first discovered as an endothelium-derived relaxing factor in smooth cells.1 It has been shown that the mammaliantype of response to NO is also active in plants.2 Since the first observation of NO regulating plant responses, it has become increasingly evident that NO affects most plant functions, such as promotion of seed germination,3 involvement in stomatal movement,4 regulation of plant senescence,5,6 control of floral transition,7 and involvement in diverse abiotic and biotic stress response.8,9 In plants, the synthesis of NO can be performed by enzymes including nitrate reductase or nitric oxide synthase,10 and by nonenzymatic reduction of apoplastic nitrite under acidic conditions.11 NO may enter the plant cell from the soil or atmosphere,12 and represents an alternative source to endogenous production of NO. The production of NO can be altered under biotic or abiotic stresses.9,13 Beligni and Lamattina have suggested that NO has dual functions, either as a cytotoxin or a cytoprotectant, and whether NO acts as either function depends on the NO concentration and on the status of the environment.14 NO has been shown to promote the elongation of cells in maize and accelerate leaf expansion in peas at low concentrations,15,16 whereas at high concentrations, NO may interfere with normal r 2011 American Chemical Society

metabolism. Previous work has suggested that NO, at a relatively high dose, impaired photosynthetic electron transport17 and decreased the expansion of leaves.16 In addition, an excess of NO production has been shown to lead to the accumulation of H2O2 in mitochondria by inhibition of cytochrome c oxidase in the respiratory chain,18 which is a potential cause of oxidative stress. In the presence of superoxide radicals, NO can be converted to peroxynitrite (ONOO), a compound that has been identified to be a major cytotoxic agent of active nitrogen species derived from NO. Active nitrogen species, including NO and ONOO, are potent oxidants that damage nucleic acids, proteins, and membranes in plant cells.19 Although large-scale microarray analysis has documented a large number of NO responsive genes in Arabidopsis,20 it is important to analyze the response of plants to NO at the proteomics level in an effort to gain systems-level information. The combination of two-dimensional electrophoresis (2-DE) and MS represents the standard approach for proteomic analysis. Georgia and co-workers have identified 40 proteins in response to NO treatment of citrus using this approach.21 However, there are disadvantages of 2-DE, such as low resolution of multiple Received: July 17, 2011 Published: October 27, 2011 5416

dx.doi.org/10.1021/pr200671d | J. Proteome Res. 2011, 10, 5416–5432

Journal of Proteome Research proteins present in a single spot, difficulty in the identification of proteins expressed at very low levels, and small proteins and proteins at the extremes of the pI range.22 As a complementary alternative, various methods based on mass spectrometry have been used including18O labeling, iTRAQ, SILAC, and ICAT.2326 However, these methods have drawbacks, including higher costs, more sample requirements, and complex experimental protocols,27 that limit their application in plant proteomic studies. Recently, label-free methods have been used as promising alternatives.2830 Although there are particular problems with label-free methods such as low reproducibility and low-quality chromatograms because of nano-LC experiments, and the requirement for MS to MS/MS switching,31 with the development of ultraperformance liquid chromatography (UPLC), these problems can be avoided. This is particularly the case if UPLC is combined with low/high-collision energy MS analysis (MSE). By combining these two methods, sufficient sensitivity and reproducibility is possible, in addition to estimating the absolute concentrations of proteins. Consequently, this approach meets the requirements of stability of the intensity, mass measurement, and retention time for label-free quantitative LCMS measurements.3234 In this report, nanoUPLC was combined with MSE-based label-free quantitative shotgun proteomics to obtain proteomic information in response to NO treatment of cotton leaves. This study is the first to characterize functional proteomics information related to NO treatment of cotton using a label-free proteomics method. The investigation of such molecular changes in plants is necessary to aid our understanding of the effects of NO on metabolism and physiological functions. Changes in the expression levels of proteins following treatment with different concentrations of sodium nitroprusside (SNP) and the NO scavenger, carboxy-PTIO potassium salt (cPTIO), were investigated. Onehundred and sixty-six NO-induced and NO-responsive proteins were identified, and the results are discussed in the context of the diverse biological functions of NO in plants. From this study, obtaining information about proteins and signal pathways responding to NO should accelerate research on NO metabolic regulation, and lay a theoretical foundation for further related research.

’ EXPERIMENTAL METHODS Plant Material and Treatments

Seeds of Gossypium hirsutum. ecotype CCRI10 were cultured in a mix of sand and nutritional soil in a culture room under white fluorescent light (14 h light/10 h dark) with day/night temperatures of 30/22 °C. For NO treatment, plants that were 30 days old after sowing were irrigated with 0.1 or 1 mM SNP in distilled water for 6 h. Plants treated with distilled water acted as the control. Plants were treated during the light period and the experiment was performed in triplicate with 30 plants in each group. For the scavenger treatment, plants were irrigated with 0.1 mM cPTIO for 4 h, then transferred to a 0.1 mM SNP solution for 6 h. Fresh, fully expanded leaves were harvested after treatment and immediately frozen in liquid nitrogen and stored at 80 °C. Protein Extraction

Protein extraction was performed as described by Shen and co-workers, according to a modified procedure.29 Samples (about 10 g) were ground in liquid nitrogen and the powders were precipitated in a 10% (w/v) trichloroacetic acid (TCA)/ acetone solution containing 0.07% (v/v) β-mercaptoethanol and

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0.1% (w/v) polyvinyl pyrrolidone at 20 °C overnight. The extracting solutions were centrifuged at 40 000g for 60 min, the supernatant was discarded, and the pellet was rinsed with 20 °C acetone containing 0.07% (v/v) β-mercaptoethanol. The material was centrifuged once more and rinsed, and the final pellet was vacuum-dried and solubilized in 5 mL of 7 M (w/v) urea containing 2 M (w/v) thiourea, 40 mM dithiothreitol (DTT), 600 μL of EDTA-free protease-inhibitor (Roche), and 1 mM NaF on ice for 2 h. The supernatants were used for further assays after centrifugation at 120 000g for 90 min at 4 °C. The 2-D Quant Kit (GE Healthcare) with bovine serum albumin (BSA) as a standard was used to determine the concentration of protein solutions. The supernatants were stored at 80 °C until required. Protein Digestion

Protein digestion was performed as described.35 The pH of the samples was adjusted to 8.5 using 1 M ammonium bicarbonate and ∼50 μg of protein for each sample was used for chemical reduction. Lys-C was added to a final substrate and a modified trypsin (Roche) digest was incubated at 37 °C for 16 h. The peptide mixture was acidified using 1 μL of formic acid for further MS analysis. After digestion, samples that were not immediately analyzed were stored at 80 °C. Analysis by Nano-UPLCMSE Tandem MS

All experiments were performed on a nanoACQUITY system (Waters, Milford, MA) based on previous work.29 The parameters of the nanoscale LC separation are outlined. An ethylene bridged hybrid C18 1.7 μm, 75 μm 250 mm, analytical reverse-phased column (Waters, Manchester, U.K.) was used. After pre-equilibration of column using mobile phase A (H2O with 0.1% formic acid), the peptides were separated with a linear gradient of 3040% mobile phase B (0.1% formic acid in acetonitrile) for 90 min, then rinsed with 90% phase B for 15 min. The flow rate used was 200 nL/min. The column temperature was maintained at 35 °C. All samples were analyzed four times. For the analysis of tryptic digested peptides, a SYNAPT HD mass spectrometer (Waters Corp., Manchester, U.K.) was used and the MS/MS fragment ions of [Glu1] fibrinopeptide B from m/z 50 to 1600 was used to calibrate the TOF analyzer of the mass spectrometer. The v-mode for mass measurements was used with a typical resolving power of at least 10 000 full-width half-maximum. Accurate mass LCMS data were collected in the MSE mode with 4 eV of low collision energy and high collision energy ramping from 15 to 45 eV. The range of collection was from m/z 300 to 1990. The internal control was 100 fmol of rabbit glycogen phosphorylase trypsin digest.36 Data Processing and Protein Identification

The ProteinLynx GlobalServer version 2.3 (PLGS 2.3) (Waters Corp., Manchester, U.K.) was used to processed the continuum LCMS data. To obtain more accurate results, the raw data were treated with processes such as deconvolution, ion detection, and deisotoping. The principles of the applied data clustering and normalization were similar to previous work.29,36 To avoid the limitations of a single database of G. hirsutum, we also selected additional seven databases of the model plant, or databases that showed a relatively close genetic relationship to G. hirsutum, and one database of G. hirsutum EST sequence. The reference database contained eight databases downloaded from 5417

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Journal of Proteome Research UniProtKB and one CGI (cotton gene index) database from DFCI, the links are: Ricinus communis: http://www.uniprot.org/uniprot/?query=Ricinus+communis& force=yes&format=fasta Populus trichocarpa: http://www.uniprot.org/uniprot/?query=Populus+trichocarpa& force=yes&format=fasta Vitis vinifera: http://www.uniprot.org/uniprot/?query=Vitis+vinifera& force=yes&format=fasta G. hirsutum: http://www.uniprot.org/uniprot/?query=Gossypium+hirsutum& force=yes&format=fasta Arabidopsis thaliana: http://www.uniprot.org/uniprot/?query=Arabidopsis+thaliana& force=yes&format=fasta Glycine max: http://www.uniprot.org/uniprot/?query=Glycine+max& force=yes&format=fasta Oryza sativa: http://www.uniprot.org/uniprot/?query=Oryza+Sativa& force=yes&format=fasta Zea mays: http://www.uniprot.org/uniprot/?query=Zea+mays&force= yes&format=fasta G. hirsutum (CGI.release_11.zip): ftp://occams.dfci.harvard.edu/pub/bio/tgi/data/Gossypium The related parameters of PLGS during the analysis of raw data were set according to the work of Shen.29 Components are clustered together with a < 10-ppm mass precision and a < 0.25-min time tolerance; alignment of elevated energy ions with low-energy precursor peptide ions was conducted with an approximate precision of (0.05 min. Rabbit glycogen phosphorylase was taken as the internal standard. For protein identification, there are at least three fragment ions per peptide with at least two peptides identified per protein. The maximum false positive rate was 4%. Quantitative Analysis

The quantitative changes in protein levels were analyzed using the Waters ExpressionE, according to the measured peptide ion peak intensities with three repeats. The setting parameters of the quantitative analysis have been previously explained in detail.29 For protein quantification, data sets were normalized using the PLGS “auto-normalization” function. The confidence interval of protein identification was set as >95%. With the use of clustering software included in PLGS 2.3 (Waters Corp, Manchester, U.K.), the mass precision and retention time tolerance for identical peptides from each triplicate set per sample were typically ca. 5 ppm and 1.2-fold change and P-value reaching 0.05 level were defined as a threshold of significance. The significant differences of proteins between samples were manually assessed by checking the matched peptide and replication level across samples. Quantitative Real-Time PCR

To understand the relationship between protein accumulation and their encoding gene transcription, we performed a comparison between the mRNA and protein expression levels. The protein sequences not belonging to G. histurm were taken as

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Figure 1. Changes in the morphology (A) and chlorophyll content (B) under different concentrations of NO. Data are means (n = 3) and error bars represent the SD. The treatment of cPTIO indicates treatment with both cPTIO and 0.1 mM SNP. The statistical significance was determined using one-way analysis of variance combined with the Bonferroni test using the SigmaStat software. *P < 0.05, **P < 0.01.

templates to carry out a tblastn (http://blast.ncbi.nlm.nih.gov) and the first nucleotide sequence in the results was selected to do quantitative real time-PCR (qRT-PCR). Total RNA was isolated using the Column Plant RNAout 2.0 software (TIANDZ, China) according to the instructions of the manufacturer. Total RNA (4 μg) was used to synthesize first-strand cDNA with the Superscript III First-Strand Synthesis System (Invitrogen) for qRTPCR. Gene-specific qRT-PCR primers were designed using the Primer Express 3.0 and then synthesized commercially (TaKaRa) as listed in Supplemental Table 1. The cotton actin gene (accession number AY305733) was used as an internal control to standardize the amount of template cDNA in each amplification reaction. The qRT-PCR was performed using the FastStart Universal SYBR Green Master (ROX) (Roche) and an ABI 7500 Sequence detection system (Applied Biosystems). The qRT-PCR cycles were as follows: initiation with a 10-min denaturation period at 95 °C, followed by 40 cycles of amplification with 10 s of denaturation at 95 °C, 35 s of annealing according to the melting temperatures provided in the Supplemental Table 1, 30 s of extension at 72 °C, and the fluorescence data collection at 72 °C. After a final extension at 72 °C for 10 min, a melting curve was then performed from 65 to 95 °C to check the specificity of the amplified product. The relative expression levels were calculated using the comparative CT method.37 All reactions were performed in triplicate.

’ RESULTS AND DISCUSSION Morphological Changes and Chlorophyll Response to SNP Treatment

Cotton seed samples were cultured in various concentrations of SNP. Compared with the control sample, treatment with ddH2O, there were no observable changes to leaves treated with 0.1 mM SNP or 0.1 mM cPTIO; however, there were clearly visible chlorosis and dry areas on leaves treated with 1 mM SNP (Figure 1A). The chlorophyll content under different treatment conditions was carefully assayed. Negligible differences were observed between the control and the 0.1 mM SNP or 0.1 mM cPTIO treated samples. In contrast, the chlorophyll content of 5418


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Figure 2. Assessment of the analytical reproducibility. (A) Error distribution associated with the intensity measurements for replicating the exact mass and retention time (EMRT) clusters detected in the control sample; the median and average intensity errors were 2.3 and 2.28%, respectively. (B) Error distribution associated with the mass accuracy for replicating EMRT clusters (in at least two of the three injections) detected in the control sample. The average retention time and median were 1.2 and 1.1%, respectively. (C) Relative standard deviation of replicating EMRT clusters detected in the control sample. The average mass errors and median were 3.37 and 2.6 ppm, respectively. (D) Log intensity EMRT clusters for injection two versus the log intensity EMRT clusters for the injection of the control sample. (E) The availability of triplicate data sets provides information about the data quality, such as replicate rates between repeated runs (left panel) and the confidence and reproducibility of protein identification (right panel). Under these conditions, 8828 EMRT clusters were detected in all three runs, 7967 were detected in two of the three repeats, and 28 060 were detected in one replicate, representing lowabundance peaks. Under these conditions, 444 proteins were identified in all three runs and are therefore of high confidence, 174 proteins were assigned in two of three repeats and are therefore of intermediate confidence, and 200 proteins were identified in one replicate and represent low-confidence hits.

samples treated with the higher SNP concentration (1 mM) decreased, and the difference reached a notable level when compared with the control sample (Figure 1B). This result indicates that the higher SNP concentration was toxic to the cotton plants with morphological and physiological index changes. Data Quality Evaluation

The relative protein profiling analysis under each condition was repeatedly performed to determine the analytical reproducibility and a series of quality control measures were used. The final results were evaluated with the protein expression software

PLGS2.3 (Waters Corp, Manchester, U.K.) using database search results analysis, and a clustering algorithm for accurate mass and retention time data. With the control as an example, the median and average intensity errors were 2.3 and 2.28%, respectively; the average retention time and median were 1.2 and 1.1%, respectively; and the average mass errors and median were 3.37 and 2.6 ppm, respectively. The latter values are in accordance with the mass precision of the extracted peptide components which were typically within 5 ppm (Figure 2AC). Other samples gave similar results, as shown in the Supplemental Figure 1AC. 5419


Journal of Proteome Research In Figure 2D, there is a diagonal line of data with no variation running throughout the detection range, and represents a binary comparison of the precursor intensity measurements of two injections of the control. As previously shown,34 this result demonstrates that there was no obvious change between the investigated injections and the expected distribution was observed. We also performed analyses of other samples on all injections and conditions, and similar results were found and are presented in the Supplemental Figure 1D. As a quality evaluation parameter, the exact mass and retention time (EMRT) of triplicate data sets were also evaluated for reproducibility between replicate analyses and the confidence of the protein identification. The replication rate plots of the control samples are shown in Figure 2E, which determines how many ions are in common between analytical replicates. The EMRT clusters observed in more than two runs were 37.4, 37.02, 33.94, and 33.87% for the control, cPTIO, 0.1 mM SNP, and 1 mM SNP, respectively, and proteins identified (as a percentage) in more than two runs were 75.56, 65.67, 76.97, and 78.79% for the control, cPTIO, 0.1 mM SNP, and 1 mM SNP, respectively. The numbers of proteins in triplicates were 444 (control), 455 (cPTIO), 423 (0.1 mM SNP), and 418 (1 mM SNP), accounting for 71.84, 59.78, 84.94, and 66.99% of the proteins identified in more than two runs. The analysis results of other samples are presented in the Supplemental Figure 1E. For the label-free method, the two most important elements are the measurement accuracy and the reproducibility of the LC. In our study, the mass accuracy was within 5 ppm and the retention time coefficient variation was within 2%, which is well within the specification of the system and suitable for label-free quantitative analysis. This result indicates that the protein identification data were extracted with high confidence and suitable for label-free quantitative analysis. Leaf Proteome in Response to NO

In this study, changes in the expression levels of proteins was optimized and normalized using an internal standard, according to the PLGS autonormalization function. Using the control sample as calibrator, the relative quantification analysis approach yielded the relative fold change of the identified proteins. In this study, the significance level was specified at 20%, so a 1.2-fold ((0.20 natural log scale) change was used as a threshold to evaluate significantly up- or downregulated expression. When proteins showed both up- and downregulation for different treatments, only up- or downregulation was reported with the selection criteria set to the largest observed change. In total, more than 400 proteins were identified. Following the removal the redundant entries and proteins that showed little significant change between the control and treated samples, 166 proteins showed significant changes under the four different conditions (Figure 3). This included 47 proteins that were upregulated (Figure 3A), 82 downregulated (Figure 3B), and 37 appeared only after various treatments (Figure 3C). Specifically, 26 proteins responded to cPTIO treatment positively and 40 responded negatively. Twenty-six and 72 responded positively or negatively to 1 mM SNP, respectively. Thirty and 38 responded positively or negatively to 0.1 mM SNP, respectively (Figure 3A,B). A total of 15 proteins appeared specifically upon cPTIO treatment, 5 appeared after 0.1 mM SNP treatment, and 6 appeared after either cPTIO, 0.1 mM or 1 mM SNP

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Figure 3. Statistics and analyses of the differentially expressed proteins under different treatments. (A) The upregulated proteins; (B) downregulated proteins; (C) the proteins showing specific expression under different conditions: a, control, water only; b, cPTIO; c, 0.1 mM SNP; d, 1 mM SNP + 0.1 mM SNP; e, cPTIO + 0.1 mM SNP; f, cPTIO + 1 mM SNP; g, cPTIO + 0.1 mM SNP + 1 mM SNP.

treatments (Figure 3C). Additionally, five proteins were found to be specifically expressed in the control sample (Figure 3C). The label-free method has specific advantages for analyzing hydrophobic proteins and low-abundance proteins. In our study, 166 proteins were identified and quantified (Figure 3) which is a significantly larger number than most gel-based proteomic studies. In a previous article using 2-DE proteomics method,21 40 proteins responded to NO treatment. Here 72 proteins were newly identified by using label-free method, and these novel proteins were marked with an asterisk (*) in Table 1. For example, ACC synthase (A2IBN7_GOSHI) and S-adenosylmethionine synthetase (B9GSZ3_POPTR) in ethylene biosynthesis, photosystem II CP43 chlorophyll apoprotein (PSBC_POPTR), rust resistance protein Rp1 (Q7XY06_MAIZE), and VM23 (TC275744) involved in photosynthesis, apoptosis/defense response, and transporter activity processes, respectively, showed quantitative differences between the treatments, but have never been identified in previous experiments using 2-DE methods (Table 1). These results suggest that our developed MSE-based label-free method is efficient and sensitive, and is therefore ideally suited to proteomic studies of this type. Classification of Identified Proteins

The 166 differentially expressed proteins were further classified based on: (a) the biological processes of each gene product according to annotations in the UniProtKB and DFCI database at http://www.uniprot.org/uniprot/, and (b) the gene product subcellular localization predicted using the SherLoc2 web server applying the defaulting setting.38 The 166 proteins were classified into 17 functional groups (Figure 4A; Table 1), which covered a wide range of pathways and functions. The identified proteins were mainly involved in photosynthesis, carbohydrate metabolism, amino acid transport and metabolism, and energy production and conversion, accounting for 23.5, 16.3, 11.4, and 13.9% of the 166 proteins, respectively (Figure 4A). For the subcellular localization analysis, these 166 proteins were located 5420



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Table 1. Differentially Expressed Proteins Identified by LCMSE quantitative changes

accession no.a

protein name

PLGS

cPTIO/

scoreb

CK

SNP P-valuec

0.1 mM/CK

SNP P-value

1 mM/CK

P-value

1. Stress Response Q10NA9_ORYSJ*

Heat shock protein, O. sativa

472.78

1.30 ( 0.25

0.98

1.39 ( 0.22

1

1.28 ( 0.22

0.97

Q53NM9_ORYSJ*

DnaK-type molecular chaperone hsp70-rice, O. sativa

407.33

1.25 ( 0.34

0.88

1.27 ( 0.28

0.93

1.19 ( 0.28

0.81

Q9S9N1_ARATH*

At1g16030,A. thaliana

346.21

0.50 ( 0.24

0

0.52 ( 0.30

0

0.51 ( 0.30

0

HSP74_ARATH*

Heat shock cognate 70 kDa protein 4, A. thaliana

454.24

1.28 ( 0.27

0.83

1.34 ( 0.29

0.95

1.25 ( 0.29

0.78

HSP73_ARATH*


308.35

1.16 ( 0.33

0.93

1.34 ( 0.40

0.96

1.19 ( 0.40

0.94

D2D322_GOSHI*

Heat shock protein 70,G. hirsutum

487.69

1.19 ( 0.31

0.83

1.26 ( 0.32

0.96

1.20 ( 0.32

0.85

B9T228_RICCO*

putative Heat shock protein, R. communis

412.77

1.25 ( 0.25

0.94

1.31 ( 0.32

0.96

1.26 ( 0.32

0.94

HSP70_MAIZE*

Heat shock 70 kDa protein, Z. mays

273.11

0.58 ( 0.28

0

0.62 ( 0.28

0

0.62 ( 0.28

0

HSP71_ARATH*


376.16

0.63 ( 0.17

0

0.66 ( 0.23

0

0.65 ( 0.23

0

B6U1E4_MAIZE*

Heat shock cognate 70 kDa protein 2, Z. mays

467.7

1.27 ( 0.25

0.96

1.35 ( 0.25

0.98

1.35 ( 0.25

0.94

DR455842*

Chloroplast HSP70, Cucumis sativus

162.59

cPTIO

Q96438_SOYBN

Actin, G. max

263.15

0.94 ( 0.40

0.39

1.22 ( 0.30

0.97

1.23 ( 0.30

0.88

B8YPL3_GOSHI

Actin 1, G. hirsutum

229.83

0.97 ( 0.20

0.43

1.04 ( 0.24

0.62

1.45 ( 0.24

0.96

B9SXZ5_RICCO

Actin, putative, R. communis

394.72

0.79 ( 0.31

0.08

1.21 ( 0.31

0.9

1.04 ( 0.28

0.63

ACT1_MAIZE

Actin-1, Z. mays

196.16

cPTIO

B4F989_MAIZE

Actin-97, Z. mays

363.05

0.88 ( 0.28

0.13

1.26 ( 0.28

0.95

1.04 ( 0.28

0.63

CYF_ARATH*

Apocytochrome f, A. thaliana

211.27

cPTIO

D4N5G0_SOYBN

Alpha-form rubisco activase, G. max

717.42

0.84 ( 0.09

0

0.84 ( 0.07

0

0.67 ( 0.07

0

B9T6D1_RICCO

Putative ferredoxin--NADP reductase, R. communis

494.66

0.36 ( 0.19

0

0.57 ( 0.32

0

0.52 ( 0.32

0

C1K5D0_GOSHI

Chloroplast chlorophyll A-B binding protein, G. hirsutum

283.7

0.66 ( 0.18

0

0.68 ( 0.14

0

0.62 ( 0.14

0

2. Cytoskeleton

3. Photosynthesis

RCA_ORYSJ

Isoform 2 of Ribulose, O. sativa

793.12

0.92 ( 0.08

0.02

0.85 ( 0.08

0

0.72 ( 0.08

0

Q0DB66_ORYSJ

Os06g0598100 protein, O. sativa

232.65

0.50 ( 0.14

0

0.55 ( 0.16

0

0.61 ( 0.16

0.04

Q8GTK4_ORYSJ


247.18

cPTIO

Q0J6 4_ORYSJ


216.94

0.49 ( 0.23

0

0.53 ( 0.15

0

0.59 ( 0.15

0.02

PSBO_SOLLC

Oxygen-evolving enhancer protein 1, Solanum lycopersicum

475.88

0.36 ( 0.19

0

0.36 ( 0.14

0

0.44 ( 0.14

0

PSBO1_ARATH

Oxygen-evolving enhancer protein 1-1, A. thaliana

387.92

0.51 ( 0.18

0

0.4 0 ( 0.14

0

0.44 ( 0.14

0

PSAD1_ARATH*

Photosystem I reaction center subunit II-1, A. thaliana

307.76

0.34 ( 0.31

0

0.34 ( 0.14

0

0.59 ( 0.14

0

PSBC_POPTR*

Photosystem II CP43 chlorophyll apoprotein, P. trichocarpa

598.01

0.71 ( 0.35

0.03

0.81 ( 0.37

0.39

0.70 ( 0.37

0.02

PSBA_ARATH*

Photosystem Q(B) protein, A. thaliana

316.12

0.84 ( 0.23

0.1

1.06 ( 0.21

0.62

0.75 ( 0.21

0.04

PSBA_SOYBN*

Photosystem Q(B) protein, G. max

321.34

1.05 ( 0.19

0.73

1.25 ( 0.20

0.98

0.97 ( 0.20

0.39

PSBA_GOSHI*

Photosystem Q(B) protein, G. hirsutum

316.12

0.87 ( 0.25

0.14

1.04 ( 0.24

0.64

0.77 ( 0.24

0.04

A5BK28_VITVI

Putative uncharacterized protein, V. vinifera

381.04

0.32 ( 0.14

0

0.34 ( 0.10

0

0.34 ( 0.10

0

O04798_LEPVR

Ribulose 1,5-bisphosphate carboxylase/oxygenase large

766.16

0.42 ( 0.04

0

0.52 ( 0.05

0

0.55 ( 0.05

0

subunit, Lepidium virginicum RBL_GOSHI

Ribulose bisphosphate carboxylase large chain, G. hirsutum

B9S088_RICCO

Putative ribulose large chain, R. communis

1157.68

0.84 ( 0.05

0

1.00 ( 0.04

0.51

0.73 ( 0.04

0

434.72

0.41 ( 0.07

0

0.44 ( 0.12

0

0.33 ( 0.12

0

RBS_GOSHI*

Ribulose bisphosphate carboxylase small chain, G. hirsutum

571.91

0.63 ( 0.07

0

0.67 ( 0.06

0

0.63 ( 0.06

0

B9SDY7_RICCO

Putative ribulose bisphosphate carboxylase/oxygenase

632.76

0.82 ( 0.08

0

0.85 ( 0.09

0

0.69 ( 0.09

0

Q9AXG0_GOSHI

Ribulose activase 2, G. hirsutum

1350.65

0.87 ( 0.07

0

0.85 ( 0.07

0

0.68 ( 0.05

0

D6PAF2_GOSHI

Ribulose large subunit, G. hirsutum

528.7

0.68 ( 0.19

0

0.76 ( 0.09

0

0.52 ( 0.09

0

Q6ZYB1_VITVI

Rubisco large subunit, V. vinifera

786.68

0.43 ( 0.05

0

0.55 ( 0.05

0

0.55 ( 0.55

0

B9S1B6_RICCO

Putative uncharacterized protein, R. communis

856.75

0.44 ( 0.06

0

0.53 ( 0.06

0

0.55 ( 0.06

0

D4N5G2_SOYBN

Rubisco activase, G. max

620.39

0.84 ( 0.07

0

0.82 ( 0.06

0

0.64 ( 0.06

0

D7TDX6_VITVI

Whole genome shotgun sequence of line

396.31

0.31 ( 0.15

0

0.33 ( 0.11

0

0.33 ( 0.11

0

activase 1,R. communis

PN40024, V. vinifera Q0IPF7_ORYSJ


538.64

0.34 ( 0.08

0

0.37 ( 0.07

0

0.26 ( 0.07

0

EX168808*

PSI-D1 precursor, Nicotiana sylvestris

585.51

0.8 ( 0.28

0.08

0.7 ( 0.29

0.03

1.7 ( 0.91

0.72

EX171310

Oxygen-evolving enhancer protein 2, Spinacia oleracea

183.82

cPTIO

5421

SNP0.1 dx.doi.org/10.1021/pr200671d |J. Proteome Res. 2011, 10, 5416–5432


ARTICLE

Table 1. Continued quantitative changes

accession no.a

protein name

PLGS

cPTIO/

scoreb

CK

SNP P-valuec

0.1 mM/CK

SNP P-value

1 mM/CK

P-value

TC229774*

Photosystem II CP43 chlorophyll apoprotein, G. hirsutum

628.53

1.5 ( 0.39

0.92

0.9 ( 0.28

0.44

1.9 ( 0.45

1

TC229854*

Photosystem Q(B) protein precursor, S. lycopersicum

484.08

1.1 ( 0.39

0.73

0.9 ( 0.4

0.35

0.76 ( 0.4

1

TC229855*

Photosystem II protein D2, Lactuca sativa

557.27

CK

TC229981*

Chloroplast pigment-binding protein CP26,

280.78

0.8 ( 0.54

0.33

0.8 ( 0.63

0.26

1.7 ( 0.55

CK

CK 0.98

Nicotiana tabacum TC241433*

Photosystem II CP47 chlorophyll apoprotein, G. hirsutum

642.74

1.22 ( 0.5

0.77

1.6 ( 0.34

0.99

0.95 ( 0.3

0.35

TC235200

Ribulose bisphosphate carboxylase small chain, G. hirsutum

529.08

0.6 ( 0.17

0

1.0 ( 0.14

0.1

0.6 ( 0.16

1

279.76

cPTIO

TC245890*

Apocytochrome f precursor, G. hirsutum

TC248531

Oxygen evolving enhancer protein 1 precursor,

SNP0.1

SNP1

1045.39

0.8 ( 0.17

0.04

0.7 ( 0.16

0

2.2 ( 0.16

1

434.35

0.9 ( 0.23

0.24

0.97 ( 0.2

0.36

0.7 ( 0.22

0.01

0.03

0.89 ( 0.27

0.01

1.36 ( 0.27

0.05

Bruguiera gymnorhiza TC270761*

Plastocyanin A, G. hirsutum

A2IBN7_GOSHI*

ACC synthase, G. hirsutum

B6SVV9_MAIZE

ATP synthase beta chain, Z. mays

373.64

0.90 ( 0.21

0.23

0.90 ( 0.19

0.14

0.69 ( 0.19

0

ATPB_ARATH

ATP synthase subunit beta, A. thaliana

1899.04

0.92 ( 0.08

0.04

0.85 ( 0.09

0

0.66 ( 0.09

0

ATPB_GOSHI

ATP synthase subunit beta, G. hirsutum

2549.32

0.97 ( 0.06

0.17

0.93 ( 0.07

0.02

0.68 ( 0.07

0

ATPBM_MAIZE

ATP synthase subunit beta, Z. mays

362.11

0.81 ( 0.21

0.03

0.9 0 ( 0.18

0.12

0.74 ( 0.18

0.01

A5BSB1_VITVI*

ATPase alpha subunit, V. vinifera

290.85

0.90 ( 0.10

0.04

0.93 ( 0.09

0.08

0.68 ( 0.09

0

Q3ZU94_VITVI

ATPase B subunit, V. vinifera

1866.58

0.93 ( 0.09

0.11

0.89 ( 0.09

0.01

0.68 ( 0.09

0

Q9XQG0_GOSHI

H(+)-transporting ATP synthase, G. hirsutum

2142.56

0.93 ( 0.08

0.08

0.87 ( 0.07

0

0.66 ( 0.07

0

C0J3I3_SOYBN

Membrane-bound ATP synthase subunit B, G. max

1695.24

0.57 ( 0.12

0

0.55 ( 0.16

0

0.38 ( 0.16

0

B9HWA2_POPTR

Mitochondrial beta subunit of F1 ATP synthase,

415.81

0.84 ( 0.20

0.04

0.95 ( 0.16

0.27

0.79 ( 0.16

0

4. Hormone Metabolism 372.98

0.76 ( 0.25

5. Energy Production and Conversion

P. trichocarpa C7IXC8_ORYSJ


581.76

0.42 ( 0.12

0

0.39 ( 0.14

0

0.36 ( 0.14

0

Q0DG48_ORYSJ


367.36

0.68 ( 0.21

0

0.86 ( 0.21

0.1

0.70 ( 0.21

0

Q0IPF8_ORYSJ


784.66

0.47 ( 0.13

0

0.49 ( 0.13

0

0.39 ( 0.13

0

A5BY24_VITVI


1792.69

0.94 ( 0.08

0.07

0.87 ( 0.07

0

0.66 ( 0.07

0

D7TSG6_VITVI

Whole genome shotgun sequence of line PN40024,

197.89

1.13 ( 0.18

0.88

1.31 ( 0.20

1

1 0.00 ( 0.20

0.47

V. vinifera B9S0Y9_RICCO*

Putative (S)-2-hydroxy-acid oxidase, R. communis

510.69

0.87 ( 0.12

0.02

0.82 ( 0.15

0.01

0.68 ( 0.15

0

ES842599*

Cytochrome b6-f complex ironsulfur subunit,

144.25

1.0 ( 0.33

0.64

0.8 ( 0.26

0.18

1.4 ( 0.27

1

TC232713

Whole genome shotgun sequence, V. vinifera

196.99

cPTIO

TC233478*

Malate dehydrogenase, V. vinifera

443.74

1.65 ( 0.35

0.98

1.49 ( 0.29

0.21

0.88 ( 0.34

1

TC235261

ATP synthase subunit beta, V. vinifera

343.34

1.32 ( 0.38

1

1.03 ( 0.52

0.49

1.16 ( 0.49

0.68

TC235622


642.17

1.46 ( 0.26

0.99

0.75 ( 0.27

0.03

1.09 ( 0.3

0.72 0.96

Solanum tuberosum

TC259440

ATP synthase subunit alpha, G. hirsutum

889.36

1.46 ( 0.26

1

1.23 ( 0.22

0.05

0.86 ( 0.19

TC259522*

Malate dehydrogenase, O sativa

414.2

2.1 ( 0.63

0.99

0.81 ( 0.72

0.26

0.65 ( 0.92

0.17

TC277800

H+-transporting two-sector ATPase,

1443.15

3.29 ( 0.08

1

1.01 ( 0.09

0.6

2.27 ( 0.12

1

Medicago truncatula 6. Carbon Utilization/Fixation Q9ZUC2_ARATH

F5O8.28 protein, A. thaliana

365.01

1.49 ( 0.37

0.94

0.88 ( 0.16

0.04

1.32 ( 0.16

1

TC255943

Carbonic anhydrase, G. hirsutum

794.37

0.99 ( 0.3

0.44

0.93 ( 0.25

0.26

2.39 ( 0.29

1

B9T4D7_RICCO

Putative histone h2b, R. communis

235.59

0.68 ( 0.19

DT048644*

Histone H2B, G. hirsutum

250.98

CK

Q7XRT0_ORYSJ

OSJNBa0042F21.13 protein, O. sativa

307.17

1.05 ( 0.14

B9RHD4_RICCO

Putative fructose-bisphosphate aldolase, R. communis

423.62

0.84 ( 0.17

B9SE47_RICCO*

Putative malate dehydrogenase, R. communis

199.09

B6STH5_MAIZE

Phosphoglycerate kinase, Z. mays

406.72

7. Nucleosome Assembly 0.90 ( 0.38

0.3

0.65 ( 0.38

0

0.67 ( 0.65

0.11

1.31 ( 0.59

0.76

0.72

1.55 ( 0.34

1

0.73 ( 0.34

0

0.03

0.79 ( 0.16

0

0.75 ( 0.16

0

0

8. Carbohydrate Metabolism

5422

SNP0.1 0.91 ( 0.17

0.13

0.94 ( 0.16

SNP1 0.31

0.72 ( 0.16

0



ARTICLE


accession no.a

protein name

PLGS

cPTIO/

scoreb

CK

SNP P-valuec

0.1 mM/CK

SNP P-value

1 mM/CK

P-value

Q9C7J4_ARATH

Putative phosphoglycerate kinase, A. thaliana

374.03

0.49 ( 0.14

0

0.49 ( 0.24

0

0.38 ( 0.24

0

ALFC1_ARATH

Probable fructose-bisphosphate aldolase 1, A. thaliana

287.59

0.9 0 ( 0.21

0.2

0.76 ( 0.25

0.04

0.77 ( 0.25

0.03

D7TG04_VITVI


191.25

cPTIO

B9GJB1_POPTR

Predicted protein, P. trichocarpa

331.72

cPTIO

A9P807_POPTR


297.97

cPTIO

SNP1

V. vinifera SNP 0.1

SNP1

Q0WL92_ARATH*

Putative GAPDH, A. thaliana

460.37

0.79 ( 0.20

0

0.99 ( 0.17

0.43

0.64 ( 0.17

0

D7UDC9_VITVI


538.51

0.82 ( 0.16

0.01

1.00 ( 0.14

0.43

0.63 ( 0.14

0

V. vinifera Q9SNK3_ORYSJ

H(+)-transporting ATP synthase, G. hirsutum

2142.56

0.93 ( 0.08

0.01

0.87 ( 0.07

0.68

0.66 ( 0.07

0

Q6LBU9_MAIZE*

GADPH (383 AA), Z. mays

567.13

0.89 ( 0.17

0.12

0.98 ( 0.16

0.4

0.68 ( 0.16

0

Q56Z86_ARATH*

Glyceraldehyde 3-phosphate dehydrogenase A,

194.01

cPTIO

B3H4P2_ARATH*

Uncharacterized protein At1g12900.3, A. thaliana

644.6

0.92 ( 0.15

0.2

1 0.00 ( 0.13

0.54

0.7 ( 0.13

0

B9RBN8_RICCO*

Putative GAPDH, R. communis

253.26

cPTIO

B4F8L7_MAIZE*

GAPDH B, Z. mays

456.96

0.82 ( 0.16

0.01

1.07 ( 0.17

0.76

0.66 ( 0.17

0

Q38IX0_SOYBN*

GAPDH B subunit, G. max

602.4

0.88 ( 0.17

0.1

1.07 ( 0.16

0.83

0.69 ( 0.16

0

G3PB_ARATH*

GAPDH B, A. thaliana

460.37

0.84 ( 0.15

0.02

1.09 ( 0.15

0.88

0.70 ( 0.15

0

G3PC_ORYSJ*

GAPDH, O. sativa

224.51

cPTIO

A. thaliana

SNP0.1

SNP1

TC231206

OJ000223_09.15 protein, O. sativa

359.37

1.31 ( 0.6

0.79

1.05 ( 0.45

0.59

1.63 ( 0.43

0.99

TC233697

Phosphoglycerate kinase, V. vinifera

628.01

1.4 ( 0.28

1

0.6 ( 0.29

0.03

0.78 ( 0.27

1

TC232277

whole genome shotgun sequence, V. vinifera

345.76

0.74 ( 0.43

1

1.46 ( 0.45

0.58

1.05 ( 0.44

0.97

TC232639

Glyceraldehyde-3-phosphate dehydrogenase B,

924.51

2.32 ( 0.2

1

0.94 ( 0.21

0.24

1.55 ( 0.2

1

TC240069*

Transketolase 1, Capsicum annuum

750.94

1.3 ( 0.21

1

0.8 ( 0.21

0.12

0.8 ( 0.27

0.1

TC247653*

Triosephosphate isomerase, V. vinifera

467.36

cPTIO

TC262493

Fructose-bisphosphate aldolase, Hevea brasiliensis

548.75

1.3 ( 0.32

0.98

1.3 ( 0.48

0.91

0.6 ( 0.27

0.01

E0YA22_ZEAMP*

Rp1-like protein, Z. mays

501.79

CK

CK

CK

Q7XY06_MAIZE*

Rust resistance protein Rp1, Z. mays

416.08

CK

CK

CK

D4N5G0_SOYBN

Alpha-form rubisco activase, G. max

717.42

0.84 ( 0.09

0

0.84 ( 0.07

0

0.67 ( 0.07

0

C6T859_SOYBN

Beta-form rubisco activase, G. max

883.09

0.84 ( 0.07

0

0.84 ( 0.06

0

0.69 ( 0.06

0

D2D326_GOSHI*

Luminal binding protein, G. hirsutum

326.02

cPTIO

B9HT80_POPTR


798.41

0.82 ( 0.09

0

0.85 ( 0.07

0

0.69 ( 0.07

0

B9NBF4_POPTR


455.95

1.22 ( 0.31

0.87

1.27 ( 0.29

0.93

1.25 ( 0.29

0.91

B9HMG8_POPTR


416.83

1.42 ( 0.23

1

1.46 ( 0.28

0.99

1.39 ( 0.28

1

B9N0E2_POPTR


391.3

0.73 ( 0.17

0

0.83 ( 0.21

0.05

0.66 ( 0.21

0

B9GL18_POPTR


305.02

B7ZZ42_MAIZE

Putative uncharacterized protein, Z. mays

442.6

1.27 ( 0.31

0.96

1.23 ( 0.31

0.91

E0CV73_VITVI


358.83

cPTIO

SNP0.1

TC232648

Chromosome chr6 scaffold_3, whole genome

190.45

CK

CK

A. thaliana

9. Apoptosis/Defense Response

10. Post-translational Modification, Protein Turnover, Chaperones

SNP0.1

SNP1

SNP0.1 0.93

1.34 ( 0.31

V. vinifera CK

shotgun sequence, V. vinifera TC241786

Ribulose-1,5-bisphosphate carboxylase/oxygenase

1273.2

0.71 ( 0.15

1

0.7 ( 0.13

0

0.7 ( 0.25

0

0.45

1.2 0 ( 0.21

0.92

0.86 ( 0.21

0.11

0.38

0.6 ( 0.57

0.96

activase 1, G. hirsutum 11. Redox Regulation CATA1_GOSHI*

Catalase isozyme 1, G. hirsutum

398.28

0.98 ( 0.24

CATA1_ARATH*

CAT-1, A. thaliana

222.57

cPTIO

B2LYS0_GOSHI*

Extracellular Cu/Zn SOD, G. hirsutum

148.09

cPTIO

Q0WUH6_ARATH*

Putative uncharacterized protein At1g20630,

222.57

cPTIO

TC229969*

Superoxide dismutase [CuZn], G. hirsutum

225.43

0.9 ( 0.65

SNP0.1

A. thaliana

5423

0.41

0.8 ( 0.55



ARTICLE


accession no.a TC232171*

protein name Catalase isozyme 2, G. hirsutum

PLGS

cPTIO/

scoreb

CK

373.75

0.84 ( 0.3

SNP P-valuec

0.1 mM/CK

SNP P-value

1 mM/CK

P-value

0.17

1.4 ( 0.29

1

1.5 ( 0.29

1

0.90 ( 0.18

0.13

0.78 ( 0.18

0.02

0.68 ( 0.16

0

12. Amino Acid Transport and Metabolism B9HK13_POPTR*

Precursor of transferase serine

261.67

1.03 ( 0.23

0.62

hydroxymethyltransferase 2, P. trichocarpa C6ZJZ0_SOYBN*

Serine hydroxymethyltransferase 5, G. max

328.81

0.89 ( 0.15

0.07

0.77 ( 0.16

0

METK1_POPTR*

S-adenosylmethionine synthase 1, P. trichocarpa

360.32

0.66 ( 0.24

0

0.88 ( 0.29

0.15

METK2_ARATH*

S-adenosylmethionine synthase 2, A. thaliana

341.8

0.64 ( 0.27

0

0.83 ( 0.26

0.11

CK

B6T681_MAIZE*

S-adenosylmethionine synthetase 1, Z. mays

348.2

0.66 ( 0.25

0

0.87 ( 0.25

0.18

CK

B9GSZ3_POPTR*

S-adenosylmethionine synthetase 5, P. trichocarpa

338.54

CK

0.84 ( 0.23

0.12

0.85 ( 0.25

0.21

C6TG31_SOYBN

Putative uncharacterized protein, G. max

609.35

0.89 ( 0.15

0.05

1.04 ( 0.15

0.73

0.68 ( 0.05

0

B9DGD1_ARATH*

AT5G35630 protein, A. thaliana

263.53

1.12 ( 0.29

0.74

1.38 ( 0.31

0.97

1.25 ( 0.31

0.87

Q5D185_SOYBN*

Chloroplast glutamine synthetase, G. max

297.63

cPTIO

B6TE43_MAIZE*

Glutamine synthetase, Z. mays

262.07

0.49 ( 0.23

0

0.72 ( 0.24

0

0.45 ( 0.24

0

B9RST2_RICCO*

Putative glutamine synthetase plant,

350.88

1.01 ( 0.18

0.5

1.15 ( 0.21

0.87

1.25 ( 0.21

0.98

GLNA2_ARATH*

Glutamine synthetase, A. thaliana

263.53

1.08 ( 0.29

0.71

1.34 ( 0.33

0.96

1.32 ( 0.33

0.94

Q0J9E0_ORYSJ*


216.68

cPTIO

C6TA91_SOYBN


299.82

1.03 ( 0.24

0.58

1.06 ( 0.33

0.66

CK

D7T6P4_VITVI


355.85

1.12 ( 0.15

0.85

1.14 ( 0.18

0.91

1.23 ( 0.18

R. communis SNP0.1

SNP1 1

V. vinifera D7SVD7_VITVI


275.23

SNP0.1

SNP1

V. vinifera TC231194

Glutamine synthetase, Nicotiana attenuata

699.29

1.73 ( 0.3

1

0.8 ( 0.31

0.28

1.1 ( 0.44

0.73

TC234776*

Mitochondrial serine hydroxymethyltransferase,

424.15

0.7 ( 0.34

1

1.5 ( 0.26

0.06

1.0 ( 0.26

0.71

TC238214*

Mitochondrial glycine decarboxylase

346.5

1.9 ( 0.36

1

1.2 ( 0.46

0.79

1.4 ( 0.41

0.99

0.9 ( 0.56

0.5

1.9 ( 0.53

0.99

Populus tremuloides complex P-protein, P. tremuloides 13. RNA-Dependent DNA Replication A5BS29_VITVI


TC231244*

Elongation factor Tu, G. max

TC263883*

Vicilin C72 precursor, G. hirsutum

TC275744*

VM23, Raphanus sativus

C6TCJ4_SOYBN


203.46

A9PGC7_POPTR

Putative uncharacterized protein, P. trichocarpa

282.71

0.67 ( 0.61

0.18

0.74 ( 0.63

0.37

0.75 ( 0.63

A9PHE2_POPTR

Putative uncharacterized protein, P. trichocarpa

816.14

1.52 ( 0.11

1

0.94 ( 0.11

0.11

0.84 ( 0.11

0

A9PJ06_9ROSI

Putative uncharacterized protein, Populus

745.89

0.87 ( 0.08

0

0.86 ( 0.070

0

0.69 ( 0.07

0

B9SPT7_RICCO

Putative uncharacterized protein, R. communis

612.8

D7UAB3_VITVI


541.29

cPTIO

D7THJ7_VITVI


705.22

0.88 ( 0.07

0

0.88 ( 0.08

0

0.69 ( 0.08

0

1

0.9 ( 0.69

0.49

1.3 ( 0.68

0.81

0.29

1.4 ( 0.41

0.94

544.27

cPTIO

14. Translation, Ribosomal Structure and Biogenesis 335.59

1.7 ( 0.53

0.98

15. Nutrient Reservoir Activity 370.19

cPTIO

SNP0.1

SNP1

16. Transporter Activity 167.81

SNP0.1

17. Unknown SNP0.1 0.38

trichocarpa x Populus deltoides SNP0.1

V. vinifera V. vinifera TC235626


310.49

1.9 ( 0.52

DR459665

Chromosome chr16 scaffold_10, whole genome

135.33

cPTIO

ES830242

Predicted protein, Monosiga brevicollis MX1

205.26

TC234871

Uncharacterized protein At1g09340

257.92

shotgun sequence, V. vinifera

5424

SNP0.1 1.7 ( 0.32

1

0.8 ( 0.49



ARTICLE


accession no.a TC230728

protein name Chromosome chr6 scaffold_28, whole genome

PLGS

cPTIO/

scoreb

CK

406.99

0.7 ( 0.43

172.6

CK

SNP P-valuec 0.07

0.1 mM/CK 1.4 ( 0.44

SNP P-value 0.96

1 mM/CK 0.9 ( 0.44

P-value 0.42

shotgun sequence, V. vinifera TC277548*

Proline-rich protein-1, G. hirsutum

CK

CK

a

Accession No. for UniProtKB and DFCI. b PLGS score is calculated by the Protein Lynx Global Server (PLGS 2.3) software using Monte Carlo algorithm to available mass spec. data and is a statistical measure of accuracy of assignation. c P-values between 0 and 0.05 represent a 95% likelihood of downregulation, while a value between 0.95 and 1 indicates a 95% likelihood of upregulation. It is best seen as an advanced statistical test, where multiple components, i.e., peptides, contribute to the probability. Proteins marked with the asterisk (*) represent new proteins identified in this paper.

Figure 4. Functional classification and distribution (A) and protein subcellular locations (B) of all 166 identified and quantified proteins.

in the chloroplast (16.3%), cytoplasm (33.1%), mitochondrion (7.8%), and so forth, and the proteins lacking exact localization annotations accounted for 38.0% (Figure 4B).

To understand which physiological action was regulated by NO, the identified 166 proteins were analyzed by placing them in appropriate signaling pathways. The analysis of the signaling 5425



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Figure 5. The quantitative expression of proteins showed significant changes to proteins involved in carbon fixation (A) and qRT-PCR analysis of the genes encoding these proteins (B). 1, control, water only; 2, cPTIO + 0.1 mM SNP; 3, 0.1 mM SNP; 4, 1 mM SNP. The Y-axis denotes relative protein levels or relative transcript levels, with values from the control set to 1.0 arbitrarily. The numbers represent the EC numbers of proteins: 4.1.1.39 (D6PAF2_GOSHI), 2.7.2.3 (B6STH5_MAIZE), 1.2.1.13 (B4F8L7_MAIZE), 4.1.2.13 (B9RHD4_RICCO), 2.2.1.1 (A9PHE2_POPTR), 3.1.3.37 (Q7XRT0_ORYSJ), 5.3.1.1 (TC247653), and 1.1.1.37 (B9SE47_RICCO). In panel A, *P < 0.05, **P < 0.01; the P-values were shown in the Table 1. ***, proteins only expressed in their corresponding treatment groups. For qRT-PCR, the NCBI/DFCI database accession numbers correspond to those listed in the Supplemental Table 1. The statistical significance was determined using one-way analysis of variance combined with the Bonferroni test using the SigmaStat software. *P < 0.05, **P < 0.01.

pathways was carried out using the KEGG database (http:// www.genome.jp/kegg/pathway.html) with an E-value of 1 105. The Expressions of Key Enzymes Involved in Carbon Fixation in Photosynthetic Organisms Were Affected by NO

Carbon dioxide fixation is an essential process of photosynthesis, and this pathway involves many enzymes that catalyze and regulate energy generation. In this study, we identified eight proteins whose expression changed significantly (marked by red boxes) and are involved in carbon fixation (Supplemental Figure 2). Rubisco is the first key enzyme of carbon fixation and we observed that the levels of the rubisco larger subunit (EC, 4.1.1.39: D6PAF2_GOSHI, Figure 5A) changed when the plant was treated with different SNP concentrations or treated with cPTIO followed by the addition of SNP. When treated with 1 mM SNP, the expressions of three proteins reduced significantly. These proteins were phosphoglycerate kinase (EC, 2.7.2.3: B6STH5_MAIZE), glyceraldehyde-3-phosphate dehydrogenase (EC, 1.2.1.13: B4F8L7_MAIZE), and fructose-bisphosphate aldolase (EC, 4.1.2.13: B9RHD4_RICCO). These results confirm previous observations that these proteins are regulated by NO in plants,21,39 thereby lending support to the results presented herein. In addition, we also found four proteins with different expression profiles. The putative uncharacterized protein (EC, 2.2.1.1: A9PHE2_POPTR) was upregulated upon

application of cPTIO but no change was observed when SNP was used. There were no changes in the level of OSJNBa0042F21.13 protein (EC, 3.1.3.37: Q7XRT0_ORYSJ) when the plant was treated with cPTIO; however, this protein was upregulated using the lower concentration of SNP (0.1 mM) and downregulated when a higher concentration of SNP (1 mM) was used. The putative malate dehydrogenase (EC, 1.1.1.37: B9SE47_RICCO) and triosephosphate isomerase (EC, 5.3.1.1: TC247653) were only expressed in the presence of SNP and cPTIO, respectively. The changes in the relative mRNA expression levels of each protein were analyzed separately (Figure 5B). Besides the putative malate dehydrogenase (B9SE47_RICCO), triosephosphate isomerase (TC247653), and putative fructose-bisphosphate aldolase (B9RHD4_RICCO), the results showed that changes in the pattern of transcription of genes encoding proteins matched the observed changes at the protein level. The putative malate dehydrogenase (B9SE47_RICCO) could not be detected in the control and cPTIO plants, whereas its mRNA level showed no significant changes when the plants were treated with 0.1 mM SNP. The mRNA expression level of putative fructosebisphosphate aldolase (B9RHD4_RICCO) showed no changes among the four groups tested, which was not consistent with enzyme activity. Meanwhile, the mRNA level of triosephosphate isomerase (TC247653) was also investigated, showing significant 5426


Journal of Proteome Research downregulation both in control and 1 mM SNP group, although no protein were detected under these treatments (Figure 5B). This indicates that the mRNAs levels coding these proteins do not correlate with the changes observed at the protein level. From Figure 1, we know that the higher NO concentrations were toxic to cotton plants, and it is plausible that the higher NO concentration inhibited the normal expression of key enzymes involved in carbon fixation and blocked photosynthesis. Expression of Components of the Photosynthetic Apparatus Changes Most Remarkably in Response to NO

A multitude of functional complexes and enzymatic reactions are involved in photosynthesis, where electron transfer is achieved according to the different arrangements of redox potentials and thus often dominate the photosynthetic chain. The photosynthetic chain is primarily composed of PSII, the cytochrome complex, PSI, and ATP synthase. In this study, 12 differential proteins involved in photosynthesis were detected (Supplemental Figure 3), of which one belonged to PSI (Photosystem I reaction center subunit II-1, PsaD_ARATH), six to PSII (Photosystem Q(B) protein, PsbA_ARATH; Photosystem II protein D2, TC229855; Os06g0598100 protein, PsbB_ORYSJ; Photosystem II CP43 chlorophyll apoprotein, PsbC_POPTR; Oxygenevolving enhancer protein 1, PSBO1_ARATH and Os07g0141400 protein, PsbP: Q8GTK4_ORYSJ), two to the cytochrome complex (Apocytochrome f, PetA: CYF_ARATH; Cytochrome b6-f complex ironsulfur subunit, PetC: ES842599), one to the electron transport chain (Putative ferredoxin-NADP reductase, PetH: B9T6D1_RICCO) and two to the F-type ATPase (H(+)-transporting ATP synthase, beta: Q9XQG0_GOSHI; ATPase alpha subunit, alpha: A5BSB1_VITVI). The analysis revealed that the expression level of PsbA (PsbA_ARATH) in PSII was significantly downregulated under high concentrations of SNP (Figure 6A). PsbD could only be detected in the control group but not others. PetA (CYF_ARATH), composed of the cytochrome complex b6/f and PsbP (Q8GTK4_ORYSJ) in PSII, only showed a differential level of expression when the NO scavenger was present. On the other hand, another protein (PetC) of the cytochrome complex b6/f was significantly upregulated under higher concentration of SNP. The other seven proteins all decreased in expression levels only when NO was present. The relative levels of α and β subunits of ATP synthase were significantly altered when the plants were treated with 1 mM SNP, with expression ratios of 0.68 and 0.66, respectively (Table 1). A close scrutiny revealed that several photosystem Q(B) proteins, ribulose bisphosphate carboxylase large chain proteins, ribulose bisphosphate carboxylase/oxygenase activases, and GAPDH proteins identified in “Carbohydrate metabolism” and “Photosynthesis” subsections (Table 1) were significantly inhibited only by 1 mM SNP treatment. We suggest that the inhibition of these proteins by 1 mM SNP may be responsible for the visible chlorotic phenotype observed in Figure 1A. QRT-PCR analysis revealed that the gene expression levels displayed very similar patterns to the protein expression profiles (Figure 6B). However, while PetH (B9T6D1_RICCO) showed a decrease in protein levels when plants were treated with SNP, the mRNA expression level exhibited no change among the four groups examined. This indicates that the expression of this protein was regulated by post-translational modifications. In the photosynthetic system, a heterodimer consisting of PsbA and PsbB from PSII combines with the P680 center and an electron acceptor,40 and provides an Mn-ion cluster of the

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oxidized excited complex with ligand. The stability of the Mn-ion cluster is because of PsbO;41,42 the CP43 chlorophyll apoprotein represents a key component of the antenna complex from PSII, which can bind chlorophyll and aid in catalyzing the initially light-induced chemical reaction.43 The ATP synthase is composed of α, β and other subunits and catalyzes the synthesis of ATP.44,45 These proteins all showed a decrease in expression when NO was present, especially under the higher concentration of NO, indicating that the application of NO generates a negative effect on light capture in photosynthesis. Previous work showed that the PsbP may be involved in the regulation of photosystem II.46 In this study, the PsbP (Q8GTK4_ORYSJ) was expressed only when the NO scavenger was present. Moreover, exogenous application of NO led to a reduction in the expression levels of PsaD (PsaD_ARATH) in PSI and PetH (B9T6D1_RICCO) in the electron transport chain. PetA (CYF_ARATH) in the cytochrome complex b6/f showed a similar trend to that observed for the PsbP (Q8GTK4_ORYSJ). The exogenous application of SNP had an influence on different components of the photosynthetic chain, and most of the proteins were downregulated, which coincided with morphological variations. Since high NO concentrations are toxic to cotton plants, we hypothesize that the application of NO undermines the competency of photosynthesis. Here, higher concentrations of NO inhibit the normal expression of ATP synthase and this subsequently leads to a lack of required energy which retards the development and growth of the cotton plants. In addition, certain components in the photosynthetic system are specifically expressed when NO is absent, which is possibly because of induced compensation or a stress effect resulting from the inhibition of the expression of other proteins. Further studies, however, remain to be completed to unravel the mechanism underlying the repressive effect of NO on photosynthetic components. The Expression of Key Enzymes Involved in Protein Processing Are Regulated by NO

The endoplasmic reticulum is the source of protein synthesis in the cell, where all kinds of protein modifications occur, such as glycosylation, hydroxylation and acylation, and improperly folded proteins will be refolded or assembled.46 We have found that protein modifications and processing are also affected and regulated by NO (Supplemental Figure 4). Among these, Bip (Luminal binding protein, D2D326_GOSHI) can be specifically induced by SNP, that is, only when NO is present is this protein specifically expressed, regardless of whether the inhibitor is applied in advance (Figure 7A). The expression level of Hsp70 (Heat shock cognate 70 kDa protein 4, HSP74_ARATH) is also highly increased with the exogenous application of NO in three treatments. Moreover, the expression level of p97 (Ribulose activase 2, Q9AXG0_GOSHI) remains unchanged under the low level of NO treatment, whereas its expression is severely inhibited when 1 mM SNP was applied to the plants. The genes encoding the Bip (D2D326_GOSHI) and Hsp70 (HSP74_ ARATH) increased significantly with SNP treatment and the gene of Ribulose activase 2 (Q9AXG0_GOSHI) decreased when SNP was applied (Figure 7B). Not only is endoplasmic reticulum the source of protein synthesis, but this organelle is the base of protein modification and processing where improperly folded or assembled proteins will be recognized and transferred from the ER lumen to the cytoplasmic matrix, culminating in degradation by the proteasome.47,48 The binding protein, Bip, in the endoplasmic reticulum can 5427



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Figure 6. The quantitative analysis identified significant changes to protein levels that are components of the photosynthetic apparatus (A) and qRT-PCR analysis of the genes encoding these proteins (B). 1, control, water only; 2, cPTIO + 0.1 mM SNP; 3, 0.1 mM SNP; 4, 1 mM SNP. The Y-axis denotes relative protein levels or relative transcript levels, with values from the control set to 1.0 arbitrarily. The proteins were as follow: PsbA (PSBA_GOSHI), PsbD (TC229855), PsbB (Q0DB66_ORYSJ), PsbC (PSBC_POPTR), PsaD (PSAD1_ARATH), PsbO (PSBO1_ARATH), PsbP (Q8GTK4_ORYSJ), PetA (CYF_ARATH), PetC (ES842599), PetH (B9T6D1_RICCO), beta (Q9XQG0_GOSHI) and alpha (A5BSB1_VITVI). In panel A, *P < 0.05, **P < 0.01, the P-values were shown in the Table 1. ***, proteins only expressed in their corresponding treatment groups. For qRT-PCR, the NCBI/DFCI database accession numbers correspond to those listed in the Supplemental Table 1. The statistical significance was determined using one-way analysis of variance combined with the Bonferroni test using the SigmaStat software. *P < 0.05, **P < 0.01.

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Figure 7. The quantitative analysis of protein levels showed significant changes to proteins involved in protein processing (A) and qRT-PCR analysis of the genes encoding these proteins (B). 1: control, water only, 2: cPTIO + 0.1 mM SNP, 3: 0.1 mM SNP, 4: 1 mM SNP. The Y-axis denotes relative protein levels or relative transcript levels, with values from the control set to 1.0 arbitrarily. The proteins were as follow: Bip (D2D326_GOSHI), Hsp70 (B6U1E4_MAIZE) and p97 (Q9AXG0_GOSHI). In the A part, *P < 0.05, **P < 0.01, the P values were shown in the Table 1. ***, proteins only expressed in their corresponding treatment groups. For qRT-PCR, the NCBI/DFCI database accession numbers correspond to those listed in the Supplemental Table 1. The statistical significance was determined using one-way analysis of variance combined with the Bonferroni test using the SigmaStat software. *P < 0.05, **P < 0.01.

recognize improperly folded or assembled proteins and facilitates their refold and reassembly.49,50 In our study, luminal binding protein (D2D326_GOSHI) in the endoplasmic reticulum was specifically expressed when plants were treated with NO, where in the control group it is not expressed. Thus, we postulate that the application of NO affected the folding and assemble of proteins, and hence induces high levels of Bip (D2D326_GOSHI) expression. On the other hand, previous work also showed that the luminal binding protein, D2D326_GOSHI, showed significantly higher expression during cotton fiber development,51 suggesting that NO maybe involved in the regulation of fiber biosynthesis. Hsp70, a molecular chaperone, is widely distributed in the endoplasmic reticulum, and can respond to different stresses.52 HSP74 is a type of ATP-dependent chaperone in A. thaliana. HSP74 contains the following functions: (i) aids in the refolding of unfolded or misfolded proteins under stress conditions; (ii) involved in ubiquitin degradation of targeted proteins with E3 ubiquitin ligase; (iii) identifying the specific site of transit peptides and subsequently degrading the precursor via the ubiquitinproteasome; and (iv) a potent role in embryogenesis. Our investigation demonstrated that Hsp70 (HSP74_ARATH) in the endoplasmic reticulum is highly expressed under three treatments. It was revealed that its function is induced by NO treatment in terms of protein folding and reassembly, which is consistent with the specific expression of the Bip protein. Simultaneously, the expression level of Rubisco kinase (Q9AXG0_GOSHI) is also quite low under high concentrations of NO. In addition to its role in photosynthesis, it also can perceive external stimuli, such as cold stress and attacks from pests, and binds to particular proteins.53 Our results suggest that Rubisco kinase may be involved in related physiological activities like NO-regulated protein folding, transport, or degradation. NO Involved in the Regulation of the Ethylene Synthesis Pathway

Ethylene is a plant hormone that is produced from methionine; the latter is converted into S-adenosyl methionine (SAM)

by methionine adenosyl transferase. 1-Aminocyclopropane-1carboxylic acid (ACC) is formed from SAM by ACC synthase, which is a limiting velocity enzyme of ethylene synthesis. In this study, we identified two proteins, B9GSZ3_POPTR (methionine adenosyl transferase) and A2IBN7_GOSHI (ACC synthase), that are part of the ethylene synthetic pathway (Supplemental Figure 5). In Arabidopsis, NO remarkably reduced ethylene production by downregulating methionine adenosyl transferease1 (MAT1) activity through post-translational S-nitrosylation regulation, thus, affecting the overall turnover of ethylene biosynthesis.54 Our results showed that there was no expression of methionine adenosyl transferase (B9GSZ3_POPTR) in the presence of cPTIO and no significant change in either high or low concentrations of SNP (Figure 8A). The relative expression level of the gene showed a similar pattern to the protein expression levels (Figure 8B). This observation suggests that the expression of methionine adenosyl transferase must be associated with NO appearance and this enzyme probably mediates the cross-talk between ethylene and NO signaling. Zhu and co-workers have reported that the application of NO at lower concentrations could decrease ethylene output, through inhibition of ACC synthase activity, thereby reducing ACC content.55 Moreover, there are a number of studies showing that NO effectively prevents ethylene biosynthesis and inhibits the activities of ACC synthase or ACC oxidase.5658 Luisa and coworkers found the ethylene evolution was very high after treatment with high concentrations of SNP, and SNP also enhanced mRNA accumulation of the ethylene biosynthetic gene ACS2, indicating that NO potentiates ethylene production by inducing a gene for its biosynthesis.59 Here, we identified and quantified ACC synthase (A2IBN7_GOSHI). The level of this protein decreased drastically when cPTIO was presented to the plants, increased significantly under the treatment of high NO concentration, and showed no obvious change with the application of the low concentration of NO. Changes in the level of 5429



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carbon fixation, photosynthesis, protein processing, and cysteine and methionine metabolism (Table 1, Supplemental Figures 25) responded most significantly to NO. Pathway analysis also revealed that NO is involved in various physiological activities, such as apoptosis, carbohydrate, and cytoskeleton (Table 1). This is the first time that functional proteomics information related to NO treatment has been systematically analyzed in cotton using a label-free proteomics method. The label-free approach represents a powerful means to identify and quantify protein levels. The method also revealed detailed information that could not be obtained with other proteomic and genomic approaches. In addition, our experiment also provided important indicators for further studies on NO-responsive proteins and signaling pathways that are regulated by NO. This research provides a broad spectrum of information which can be used in other plant studies examining the response of a plant to NO. Figure 8. The quantitative analysis of protein levels identified significant changes to proteins involved in ethylene synthesis (A) and qRTPCR analysis of the genes encoding these proteins (B). 1, control, water only; 2, cPTIO + 0.1 mM SNP; 3, 0.1 mM SNP; 4, 1 mM SNP. The Y-axis denotes relative protein levels or relative transcript levels, with values from the control set to 1.0 arbitrarily. The numbers represent EC numbers of the proteins: 2.5.1.6 (B9GSZ3_POPTR) and 4.4.1.14 (A2IBN7_GOSHI). In panel A, *P < 0.05, **P < 0.01, the P-values were shown in the Table 1. ***, proteins only expressed in their corresponding treatment groups. For qRT-PCR, the NCBI/DFCI database accession numbers correspond to those listed in the Supplemental Table 1. The statistical significance was determined using oneway analysis of variance combined with the Bonferroni test using the SigmaStat software. *P < 0.05, **P < 0.01.

transcription of the gene were consistent with the protein expression pattern (Figure 8B). Our result suggest that ethylene biosynthesis is positively regulated by NO, and high concentrations of NO could elevate the level of ethylene production, which is in accordance with the results of previous studies.60,61 It should be noted that these proteins were not detected in previous proteomics data sets.21,39 Other Signal Pathway Responses to NO

Besides the signaling pathways discussed above, other physiological activities were found to be regulated by NO. In this study, the levels of identified proteins involved in amino acid metabolism (Chloroplast glutamine synthetase, Q5D185_SOYBN; G. max precursor of transferase serine hydroxymethyltransferase 2, B9HK13_POPTR; Glutamine synthetase, B6TE43_MAIZE), the calcium signaling pathway (Putative uncharacterized protein, A5BS29_VITVI), nitrogen reduction and fixation (F5O8.28 protein, Q9ZUC2_ARATH), peroxisome (Extracellular Cu/Zn SOD, B2LYS0_GOSHI; Putative (S)-2-hydroxy-acid oxidase, B9S0Y9_RICCO; Catalase isozyme 1, CATA1_GOSHI) and RNA degradation (Predicted protein, B9N0E2_POPTR) were significantly affected following the application of SNP to the cotton plants. Consequently, NO regulates various kinds of signaling pathways, and our study provides new insights into the NO action mechanisms in plants.

’ CONCLUSIONS In conclusion, the present study reproducibly identified and quantified 166 proteins from cotton leaves whose expression levels changed noticeably in response to NO treatment to the plant. KEGG database analysis demonstrated that proteins in

’ ASSOCIATED CONTENT

bS

Supporting Information Supplemental Table 1, primer sequences used for quantitative real-time PCR; Supplemental Table 2, cellular localization of differentially expressed proteins; Supplemental Figure 1, assessment of the analytical reproducibility; Supplemental Figure 2, the pathway of carbon fixation derived from KEGG; Supplemental Figure 3, the pathway of photosynthesis derived from KEGG; Supplemental Figure 4, the pathway of protein processing derived from KEGG; Supplemental Figure 5, the pathway of ethylene synthesis derived from KEGG. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*S.Y.: State Key Laboratory of Cotton Biology, Cotton Research Institute, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan Province, China. Tel.: +86 372 2525365. Fax: +86 372 2525363. E-mail: [email protected]. W.L: National Center of Biomedical Analysis, Beijing 100850, China. E-mail: [email protected]. Author Contributions §

These authors have contributed equally to this work.

’ ACKNOWLEDGMENT This research is funded by the National Basic Research Program of China (2010CB126006) and Supported by the Earmarked Fund for China Agriculture Research System (CARS-18). The research was performed at the State Key Laboratory of Cotton Biology in the Cotton Research Institute of the Chinese Academy of Agricultural Sciences, and portions of this research were carried out at the National Center of Biomedical Analysis. ’ REFERENCES (1) Ignarro, L. J.; Byrns, R. E.; Buga, G. M.; Wood, K. S. Endotheliumderived relaxing factor from pulmonary artery and vein possesses pharmacologic and chemical properties identical to those of nitric oxide radical. Circ. Res. 1987, 61 (6), 866–879. 5430


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Label-Free Quantitative Proteomics Analysis of Cotton Leaf Response

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