Proteomic Characterization of the Greening Process in Rice Seedlings

ARTICLE pubs.acs.org/jpr

Proteomic Characterization of the Greening Process in Rice Seedlings Using the MS Spectral Intensity-based Label Free Method Kentaro Hamamoto,† Toshihiko Aki,† Mikao Shigyo,† Shigeru Sato,†,‡ Tetsuya Ishida,†,‡ Kentaro Yano,§ Tadakatsu Yoneyama,† and Shuichi Yanagisawa*,†,‡ †

Department of Applied Biological Chemistzry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan ‡ Biotechnology Research Center, The University of Tokyo, Tokyo 113-8657, Japan § School of Agriculture, Meiji University, Kanagawa 214-8571, Japan

bS Supporting Information ABSTRACT: Illumination-induced greening in dark-grown plants is one of the most dramatic developmental processes known in plants. In our current study, we characterized the greening process of rice seedlings using comparative proteome analysis. We identified 886 different proteins in both whole cell lysates of illuminated and nonilluminated rice shoots and performed comparative proteome analysis based on the MS spectral intensities obtained for unique peptides from respective proteins. Furthermore, the changes in the levels of individual proteins were then compared with those of the corresponding mRNAs. The results revealed well-coordinated increases in the enzymes involved in the Calvin cycle at both the protein and mRNA levels during greening, and that the changes at the mRNA level precede those at the protein level. Although a much lower effect of illumination was found on the enzymes associated with glycolysis and the TCA cycle, coordinated increases during greening were evident for the enzymes involved in photorespiration and nitrogen assimilation as well as the components of the chloroplastic translational machinery. These results thus define the differential regulation of distinct biological systems during greening in rice and demonstrate the usefulness of comprehensive and comparative proteome analysis for the characterization of biological processes in plant cells. KEYWORDS: comparative proteome analysis, greening process, label-free, light, rice

’ INTRODUCTION Proteome analysis is a powerful research tool that is applicable to a wide range of biological studies as it facilitates the direct identification of proteins in particular samples.1,2 In previous studies, comprehensive analyses of a variety of samples have successfully revealed the characteristics of particular tissues, cells and organelles among others.36 However, comparative proteome analysis is required for a fuller elucidation of biological processes, such as developmental processes and cellular responses to stimuli. This is because such analysis identifies not only existing proteins but also changes in protein composition and fluctuations in protein expression levels. Due to its importance, several methods for comparative proteome analysis have been developed and utilized in past reports. One of the most popular of these protocols is the 2D-electrophoresis-based method,79 which provides reliable quantitative data. However, this method is applicable to relatively abundant proteins only, as they need to be detectable by dye staining. Furthermore, 2Delectrophoresis requires independent analyses of the respective proteins, which are time-consuming. Shotgun analysis that r 2011 American Chemical Society

involves stable isotope labeling methods is therefore a preferable approach for comprehensive and comparative proteome analysis.810 However, these methods also have limitations arising from the difficulty in adequately labeling all proteins in a sample with stable isotopes. There are also alternative label-free methods that can be used for comparative and comprehensive proteome analysis. One of the more popular of these is the spectral counting-based method in which the number of MS/MS spectra of the peptides derived from a protein is utilized as an indicator of its abundance.810 This approach is useful for the qualitative evaluation of protein abundance due to the simplicity of the strategy, but seems not to be suitable for quantitative evaluations. Another label-free method, the MS spectral intensity-based method, is a viable alternative in this regard as it enables quantitative evaluations of protein Special Issue: Microbial and Plant Proteomics Received: August 31, 2011 Published: November 11, 2011 331

dx.doi.org/10.1021/pr200852q | J. Proteome Res. 2012, 11, 331–347

Journal of Proteome Research abundance. This method was reported to have problems in terms of a robust reproducibility of MS intensities,9,10 but ion currents extracted with integrated LCMS have later been shown to be generally correlated with peptide abundance.11,12 This method therefore was employed in several studies.13,14 Furthermore, Brabbilla et al. have recently reported that the use of the MS spectral intensity-based method with a microfluidic nano HPLCChip system greatly improves the sensitivity of target protein detection with a high reproducibility.15 When dark-grown plant seedlings are exposed to light, shoot greening initiates. Greening is one of the most dramatic developmental processes in plants, and is accompanied by photomorphogenic development and by a transition from heterotrophic to autotrophic metabolism.1618 Since greening is a phenomenon that has generated much interest, there have been many previous studies of the greening process with a focus on gene expression, particularly photosynthetic genes, and on the modulation of the corresponding protein levels during greening.1719 In addition to the biochemical and molecular biological studies that have focused on particular genes or proteins, comprehensive analyses of transcripts in both Arabidopsis and rice seedlings undergoing greening have been performed. It has been reported that the transcript levels of more than 1000 genes are modulated during greening in both of these plant species.20,21 This suggests that the abundance of many proteins may also be modulated during greening. Furthermore, it has been shown that the greening process is induced by the targeted degradation of photomorphogenesis-promoting transcription factors in the nucleus.22 These previous findings on the greening of plants indicate the potential of comprehensive and comparative proteome analysis to more effectively characterize the mechanisms underlying this process. In a previous proteomics approach to reveal the proteins involved in greening, where whole proteins of Arabidopsis were used, the products of 19 genes were shown to accumulate during 69 h of illumination.23 A similar study of rice lysates revealed that the expression of 52 proteins is modulated by illumination.24 Since a conventional 2D electrophoresis method was employed in these studies however, only small numbers of proteins could be identified. Recently, Shen et al. employed the shotgun method to characterize the greening process in maize.25 They revealed that the abundance of 73 proteins out of approximately 400 that were identified in seedlings of maize, a C4 plant, is altered within 24 h of illumination and that the expression of photosystem II subunits is highly sensitive to light.25 Furthermore, proteome analysis that was focused on light-induced development of chloroplasts from etioplasts in rice plants has revealed that the transition from heterotrophic metabolism to autotrophic metabolism is already initiated within two hours after illumination.26 The reported results of other proteome analyses that focused on chloroplast and thylakoid functions in Arabidopsis have revealed coregulated expression of the thylakoid proteins and new functions and responses of these proteins.2730 In our current study to more comprehensively characterize the greening process in rice, one of the world’s most important crops, we conducted comparative and comprehensive proteome analysis of rice seedlings using the MS intensity based label-free method. A large number of different proteins (886) was identified in cell lysates prepared from both illuminated and nonilluminated rice shoots, and fluctuations in the levels of multiple proteins that function in a particular metabolic pathway or biological process were semiquantitatively evaluated. Taken

ARTICLE

together with the results of transcriptome analysis, our results revealed differences in the modulation of distinct metabolic pathways during greening and provide a wider panel of proteins of which the functions are potentially associated with the greening process.

’ MATERIALS AND METHODS Plant Materials

Rice (Oryza sativa L. Nipponbare) seedlings were germinated for seven days at 25 °C in water in the dark, and then grown with a nutrient solution [0.37 mM CaCl2, 0.17 mM NaH2 PO4, 0.47 mM MgSO4 , 0.27 mM K2SO4, 0.70 mM (NH4 )2 SO4, 45 μM Fe(III)EDTA, 0.40 mM SiO2, 15 μM H3BO3, 4.6 μM MnSO4, 0.1 μM NaMoO4, 0.15 μM ZnSO4, 0.16 μM CuSO4, pH 5.5] for a further six days also in the dark. The seedlings were subsequently cultivated with fresh nutrient solution under continuous illumination (100 μE) for 0, 24, 48, 72, or 96 h. The aerial portions of the seedlings were then frozen in liquid nitrogen and stored at 80 °C until use. Measurement of Chlorophyll Contents

The chlorophyll contents of the aerial parts of the seedlings were determined using the previously described methods of Moran et al.31 Preparation of Sample Proteins

All protein extractions were conducted at 4 °C. The aerial parts of the rice seedlings were immersed in 5 volumes (w/v) of a grinding buffer [20 mM Hepes-KOH (pH 7.0), 150 mM NaCl 5 mM MgCl2, 1% NP40, 10 mM 2-mercaptoethanol, 0.5 mM PMSF, 10 mM NaF, 1 mM Na3VO4] supplemented with Complete Protease Inhibitor Cocktail Tablets (Roche Diagnostics Japan, Tokyo, Japan). The tissues were then ground using a homogenizer, and the resultant homogenates were centrifuged at 20380 g for 15 min. The obtained supernatants were frozen in liquid nitrogen and stored at 80 °C until use. Protein concentrations were measured using a protein assay reagent (Bio-Rad Japan, Tokyo, Japan). NanoLCESIMS/MS Analysis and Comparative Proteomic Analysis

Aliquots of sample solutions that contained an equivalent amount of protein to that extracted from 5 mg of the tissue sample (50260 μg) were resolved by SDS-PAGE, and the subsequent preparation of trypsin-digested peptides was performed as described previously.32,33 Briefly, the gel was cut into 40 pieces after staining with Coomassie Brilliant Blue R-250. Each gel piece was then subjected to in gel digestion with Sequencing grade Modified Trypsin (Promega, Madison, WI). The peptide samples were analyzed using a nano flow HPLCChip system coupled with ion trap mass spectrometry (Agilent Technologies, Palo Alto, CA) as described previously.32,33 MS and MS/MS spectra were subjected to searches against the rice annotation project database (RAP-DB) using Spectrum Mill software (version A.03.02.060b, Agilent Technologies) as a search engine. For the identification of proteins, the search parameters were set as follows; fixed modification, carbamidomethylation of cysteines; variable modification, none; precursor mass tolerance, 2.5 Da; fragmented mass tolerance, 0.7 Da; digestion enzyme, trypsin; allowed miss cleavage, 2. Only the protein/peptide identifications that passed through the autovalidation filter were considered to be significant. Both global FDRs (false discovery rates) for peptide identification and protein 332

dx.doi.org/10.1021/pr200852q |J. Proteome Res. 2012, 11, 331–347

Journal of Proteome Research

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Figure 1. MS intensity-based quantitative evaluation. (A and B) Correlation between MS spectrum intensities and BSA levels. The mean value and standard deviation (SD) was calculated using the MS spectrum intensities for all identified peptides (A) or the indicated peptides (B). (C) Correlation between MS spectrum intensities and BSA concentrations in the rice cell lysates. The means of the MS spectrum intensities derived from the indicated peptides are shown. (D) Means of the MS spectrum intensities of peptides derived from identified proteins using rice whole cell lysate. The ID of these proteins are Os03g0401300, Os01g0805900, Os08g0113100, Os11g0247300, Os05g0402700, Os10g0478200, Os04g0486600, Os01g0147900, Os03g028500, Os02g0718900, Os05g0489600, AK070556, AK105927, and AK105600.

identification under our search conditions were less than 1%.32,33 For the estimation of the relative abundances of individual proteins, precursor MS ion intensities of each peptide were calculated by creating extracted ion chromatograms within a mass tolerance of (1.4 m/z and a time frame of 15 s. The parameters for the automated software to calculate the precursor MS spectral intensities by creating extracted ion chromatograms were the same as those used in a recent report by Brabbilla et al.15 For comparative analysis, we used only the MS intensities of peptides that were uniquely assigned to a specific protein. We initially calculated the relative abundance using the MS intensities of each unique peptide. The relative abundances of the proteins were calculated from the means of the relative MS intensities of the corresponding unique peptides.

an RNeasy Plant Mini Kit (Qiagen, Dusseldorf, Germany). The preparation was then used for amplification and labeling with a Low RNA Input Linear Amplification/Labeling kit (Agilent Technologies). Subsequent hybridization of Cy3- and Cy5-labeled cDNA probes with Agilent rice 44k arrays (Agilent Technologies) was conducted in accordance with the manufacturer’s instructions. The glass slides were scanned using a microarray scanner (G2565BA, Agilent Technologies) and the data shown are the mean values of triplicate samples.

’ RESULTS AND DISCUSSION Evaluation of the MS Intensity-based Label-free Method Using Increasing Amounts of BSA

A recent report has examined the feasibility of using the MS intensity-based label-free method with a nano flow HPLC-Chip system coupled with MS for comparative proteome analysis.15 We independently evaluated the feasibility of this method in our present study using the same system to assay increasing amounts of BSA. In consecutive analyses, the MS spectral intensities of peptides derived from BSA and their means mainly increased in parallel with the analyzed amounts of BSA (Figure 1A, B). The standard curves that were obtained using the MS intensities of 12 different peptides were similar to each other, although the basal levels of the respective peptides differed (Figure 1B). They were mostly linear between 1 107 and 1 1010. When five independent assays of increasing amounts of BSA were conducted, the MS spectral intensities were also found to be in parallel with the analyzed amounts of BSA in each assay. However, the basal levels varied in the five independent assays, probably due to changes in the sensitivity of the MS (data not shown). To further assess the feasibility of the MS intensity-based label-free method, we investigated the correlations between the MS spectral intensities and the amounts of BSA present in a complex matrix by analyzing mixtures of spiked amounts of BSA

Quantitative Reverse Transcription (RT)-PCR Analysis

Preparation of total RNA, cDNA synthesis using random primers and quantitative RT-PCR analysis were performed as described previously.34,35 The following primers were used for PCR amplification; 50 -GAAGGAGGAGGAAATCGAAC-30 and 50 -CTTCACAGAGGTGATGCTAAGG-30 (for OsUBQ5); 50 GGAAGATGGGTTTAGTGCG-30 and 50 -GCTAATCAGAATAACACCACGG-30 (for Cab). Immunoblotting Analysis

Immunoblotting was performed with antichlamydomonas sedoheptulose 1,7-bisphosphatase (SBPase)36 and antirice chloroplastic glutamine synthase (GS2)37 antibodies, as described previously.38 Detection of antigens was carried out using the Super Signal West Pico Chemiluminescence signal detection system (Pierce, Rockford, IL). As a control, α-tubulin was also detected with specific antibodies (DM1A; Sigma, St. Louis, MO). DNA Microarray Analysis

Total RNA was extracted using TRIzol reagent (Life Technologies Japan Ltd., Tokyo, Japan) and was further purified using 333



ARTICLE

Figure 3. Comparative analysis of protein expression levels. (A) Proteins that were compared were classified into seven functional categories including metabolism, translation, protein fate, defense system, signal transduction/gene expression, RNA/DNA metabolism, and other/unknown. (B) Venn diagram analysis of numbers of proteins that showed a greater than 3-fold change in expression. (C) Immunoblotting analysis of proteins extracted from an equivalent amount of tissues from nonilluminated, and 24 or 96 h illuminated rice shoots using anti-SBPase, anti-GS2 and anti-α-tubulin antibodies. (D) Scatter plots comparing the protein levels with the corresponding mRNA levels after 24 and 96 h illumination. (E) Heat map analysis of the relative protein (P) and mRNA (R) levels for 569 different proteins in 24- or 96-h illuminated rice seedlings. Greater than 3-fold decreases and increases are indicated by navy and red bars, respectively. Decreased and increased expression of between 2 and 3 fold is denoted by sky blue and pink bars, respectively.

Figure 2. The greening process in rice seedlings. (A) Photographs of rice seedlings that were illuminated for the indicated periods to enable greening. (B) Changes in the total chlorophyll content per fresh weight during greening. (C) Quantitative RT-PCR analysis of the Cab expression during greening. Values are the means ( SD of three replicates relative to the transcript levels of the OsUBQ5. The expression level of Cab gene in the seedling shoots illuminated for 24 h was assigned a reference value of 1 unit. (D) Changes in the total cellular protein content per fresh weight. (E) SDS-PAGE analysis of proteins extracted from the same weight of shoots illuminated for 0, 24, or 96 h. Examples of proteins down-regulated during greening are indicated by “d”, and examples of proteins showing no expression changes after 24 h of illumination but that were altered after 96 h of illumination are indicated by “96”.

plant greening, we prepared whole cell lysates from rice seedlings and generated a dilution series, and then performed consecutive MS assays using the samples that included 1, 3, or 9 μg of protein. Because the protein samples were separated by standard SDSPAGE, and the proteins in their denatured form were recovered from 10 gel pieces, there was the possibility that the same proteins would be separated into different gel pieces. We therefore initially confirmed using a BSA standard that the MS intensity of the protein in a single gel piece equaled the sum of the MS intensities obtained with the same amount of protein in two gel pieces in a consecutive assay (data not shown). To avoid possible reductions in the protein identification by overlapping of the MS peaks from BSA and sample proteins, we did not add BSA as an internal standard into each sample but assayed it at the beginning and end of each consecutive assay to confirm that there had been no reduction in sensitivity. To calculate the relative abundance of a given protein in the samples, we initially calculated its relative abundance using the MS intensities of identical peptides that were detected with different samples, and then the relative abundance of each protein as an average of relative abundance for each peptide, given that the basal levels of distinct peptides from the same protein were different. Moreover, we used only unique peptides in this calculation and never

and rice whole cell lysates containing a fixed amount of rice protein (10 μg). The results were similar to those obtained with pure BSA alone. The slopes of the standard curves using MS intensities for 14 different peptides were similar, but the slope of the standard curves for one of the peptides differed from the others (Figure 1C). As the MS intensity for this peptide was below 1 107 and those of the other peptides were above 1 107, we speculated that there may be a lower threshold for maintaining the proportional relationship between the MS intensity values in our assay system. Taken together, these results suggest that comparative proteomic analysis using the MS intensities for each peptide is possible if all assays are consecutively performed and MS peaks of sufficient strength are obtained. This is in agreement with the previous findings of Brambilla et al.15 Evaluation of the MS Intensity-based Label-free Method Using Increasing Amounts of Rice Proteins

To further verify MS intensity-based comparative proteome analysis as a feasible approach to the molecular elucidation of 334


335

Os02g0595700

Os02g0603800

Os02g0634900

M

PF

Os02g0537700

D

T

AK066564 40S ribosomal protein S10

Os02g0503400

T

Os02g0549600

Os02g0304800

M

Os02g0589400

Os02g0302400

M

T

Os02g0285300

O

M

AK068919 2-cys peroxiredoxin BAS1, chloroplast precursor

Os02g0259600

protein

Protein prenyltransferase domain containing

Tyrosine aminotransferase-like protein

family protein

UDP-glucuronosyl/UDP-glucosyltransferase

proteasome subunit alpha-2)

AK072855 Proteasome subunit alpha type 2 (20S

AK120769 Isoprenoid biosynthesis-like protein

(EF-TuA) precursor

AK119587 Chloroplast elongation factor TuA

()

AK119704 Ribosomal protein L35

()

()

protein

AK058613 DREPP plasma membrane polypeptide family

precursor

AK064863 50S ribosomal protein L21, chloroplast

AK120755 Citrate synthase, glyoxysomal precursor

AK072105 Hydroxypyruvate reductase

AK065652 Cysteine synthase, mitochondrial precursor

AK067623 Hypothetical protein

T

AK110782 Apocytochrome f precursor

AK058284 Photosystem II subunit PsbS

Os02g0232400

Os01g0869800

M

AK065795 Conserved hypothetical protein

M

Os01g0805300

O

AK059820 Minor allergen

Os02g0101500

Os01g0784800

O

AY346327 Victorin binding protein

M

Os01g0711400

M

AK122067 Ubiquitin-conjugating enzyme E2 N

Os01g0978100

Os01g0673600

PF

AF010581 Chloroplast 50S ribosomal protein L12

M

Os01g0662200

T

chloroplast precursor

AK060890 Carbonic anhydrase, chloroplast precursor

Os01g0881700

Os01g0639900

M

AK067715 Oxygen-evolving enhancer protein 1,

Os01g0894700

Os01g0501800

M

AK120842 60S ribosomal protein L23a (L25)

M

Os01g0348700

T

AK066632 Ferric leghemoglobin reductase

O

Os01g0328700

M

ester (oxidative) cyclasea

AK059435 Magnesium-protoporphyrin IX monomethyl

3

17

21

5

5

13

3

1

2

8

2

7

21

16

3

6

9

6

4

28

5

5

15

14

7

18

45.89

248.84

340.47

86.44

77.95

185.22

36.19

12.48

29.96

121.78

25.68

103.5

322.31

265.63

46.78

94.33

157.82

90.88

58.75

423.14

71.68

67.93

242.61

201.8

97.72

264.31

45.56 224.59

3

217.44

162.38

16

13

11

19

31

60

23

33

66

20

1

8

56

15

19

77

59

14

85

52

37

31

36

46

29

63

55

34

51

42

19

76

60

AK068329 SOUL-like protein

Os01g0210500

Os01g0279100

O

M

AK109339 Isoflavone reductase-like protein

AK060920 Triosephosphate isomerase

Os01g0106400

Os01g0147900

M

M

sequence

unique annotation

peptides score coverage (%)

EST

Os number

category

mean

total

81 24 9

1.20 108 6.03 107 5.06 107

18

60 10

4.93 107 7.41 107

228

50

1.27 108 8.03 107 1.37 108

14 149

2.33 108

9.72 108 1.06 108

6 12

5.99 107

34

142

1.20 108

6.29 107

13

2.73 108 34

20 177

4.14 107 6.79 107

17

4.79 107

2.33 107

15 197

1.26 108

17

4.49 107

198

132

1.24 108 2.31 108

31

1.09 108

3.11 107

96

1.50 108

20 109

2.08 107 9.76 107

61 135

1.13 108

7

intensity peptides 9.58 10

identification

functional

Table 1. Proteins Identified to Be Differentially Expressed during Greening

2

6

26

4

6

19

3

2

0

7

2

3

16

21

3

6

12

0

2

5

2

2

9

17

7

19

17

3

18

3

peptides

compared

0.26

1.54

1.90

1.27

0.74

1.35

0.57

5.88

ND

0.46

1.78

1.70

3.11

1.42

0.78

0.72

2.35

ND

0.86

5.03

0.22

0.26

3.93

1.10

1.05

1.49

3.44

3.79

0.84

0.79

ratio

mean

0.03

0.56

0.37

0.51

0.25

0.40

0.09

1.16

ND

0.13

0.44

1.01

0.72

0.21

0.46

0.20

0.50

ND

0.17

2.62

0.01

0.24

1.06

0.12

0.25

0.15

1.98

2.34

0.13

0.24

SD

24 h/0 h

0.90

2.83

5.12

3.48

1.41

3.68

0.65

0.53

ND

0.11

4.73

0.75

17.75

5.17

2.49

1.23

15.95

6.80

0.89

6.21

1.02

0.90

18.82

1.60

0.79

3.71

9.15

15.11

1.45

0.20

transcript

2

8

22

2

5

16

2

2

2

3

2

2

6

18

2

4

12

2

4

4

2

1

8

16

2

16

11

3

21

4

peptides

compared

1.24

0.15

0.87

1.25

6.16

0.79

3.04

SD

1.40

4.24

6.05

5.45

0.30

4.28

0.30

5.66

4.69

0.13

4.73

3.72

5.72

3.56

3.37

6.13

14.30

3.14

3.23

5.77

0.35

0.46

1.13

0.77

21.07

2.72

0.72

0.59

11.41

2.10

1.02

5.23

1.42

0.75

9.08

1.25

0.48

1.99

3.71

7.23

1.08

1.42

transcript

0.11

2.21

3.98

2.92

0.10

0.89

0.22

1.27

1.12

4.34

2.33

3.21

1.18

1.82

0.29

0.61

3.08 ND

0.05

2.04

2.49

0.76

0.86

1.50

3.55

4.17

1.74

2.20

3.29

0.06

0.05 ND

45.53 21.47

3.73

0.29

3.16

2.46

15.19

3.14

7.22

ratio

mean

96 h/0 h

Journal of Proteome Research ARTICLE


336

Os03g0171900

Os03g0219900

Os03g0284400

Os03g0308100

Os03g0332700

Os03g0655700

M

T

T

PF

M

M

Os04g0233400

Os04g0234600

Os04g0235600

O

M

M

Os03g0829000

Os03g0131200

D

M

Os03g0129300

M

Os03g0827700

Os03g0117100

O

RD

Os03g0100200

O

Os03g0786100

Os02g0830100

PF

M

Os02g0813500

D

Os03g0773800

Os02g0797500

M

M

Os02g0774300

PF

Os03g0738400

Os02g0749500

PF

Os03g0712700

Os02g0744900

M

M

Os02g0698000

M

M

Os02g0662100

Os02g0668100

M

M

AK058218 ZmGR1a protein

AK110794 ATP synthase B chain (Subunit I).


AK119209 Sedoheptulose-1,7-bisphosphatase,

AK102155 MipE

family protein

6

11

3

2

15

AK065849 ATP-dependent RNA helicaseb

AK071107 Fumarylacetoacetate (FAA) hydrolase

18

11

25

24

9

4

2

7

10

6

12

17

5

8

14

6

18

19

1

8

17

4

1

AK061719 Glycolate oxidase

AK067105 Malate dehydrogenase, glyoxysomal precursor

mitochondrial precursor

AK100404 Serine hydroxymethyltransferase,

AK072313 Phosphoglucomutase


AK120254 3-isopropylmalate dehydrogenase,

AK072820 ABC Transporter, ATP binding component

AB116073 Peptidase S14, ClpP family protein

AK069064 Ribosomal protein L10-like

precursor

AK066527 50S ribosomal protein L15, chloroplast

protein

AK105642 Alanine:glyoxylate aminotransferase-like

AK066378 Catalase isozyme 2

AK067755 Glyceraldehyde-3-phosphate dehydrogenase

protein

AK070213 Peroxisomal biogenesis factor 11 family


AK065077 Oligopeptidase A

AK100446 Glutathione reductase, cytosolic

AK072426 Plastidic aspartate aminotransferase

precursor

AK065228 Heat shock 70 kDa protein, mitochondrial

protein

AK119455 Exocyst complex component Sec6 family

AK061968 Geranylgeranyl hydrogenase

AF529237 Phosphoribulokinase, chloroplast precursor

AK105878 Polyprenyl synthetase family protein

85.94

174.65

49.2

30.66

226.07

269.53

168.95

370.3

341.48

137.49

64.56

31.63

111.38

134.85

83.42

169.95

267.03

68.37

129.08

203.51

81.82

245.94

287.78

20.9

113.65

258.83

55.66

16.2

38

34

14

12

32

69

50

72

58

41

18

9

45

34

24

30

46

27

60

25

20

46

35

8

25

61

13

10

sequence

unique annotation


EST

Os number

category

mean

total

7

5.94 106

1.46 108

43

73

4.42 107

110

7

7.07 107

4.14 107

69

2.08 108

1.54 108

93 142

7.77 107

109

41

5.26 107

147

25

2.94 107

1.01 108

10

2.14 108

54

3.97 107

54

6.28 107

3.76 107

18

5.24 107

22

4.61 107

52

63

1.27 108

269

59

7.63 107

2.42 108

16

4.62 107

2.85 108

89

1.96 108

104

19

6.92 107

76

2.05 108 1.70 108

9 19

1.40 107

7


identification

functional

Table 1. Continued

7

14

6

2

5

14

10

14

33

7

3

1

10

5

3

4

17

3

12

11

2

22

8

2

5

7

2

3

peptides

compared

0.27

0.36

2.18

0.58

2.70

1.29

0.29

0.13

0.06

0.29

0.91

2.58

0.20

0.63

0.03

0.14

SD

1.26

2.48

1.53

0.23

7.40

2.78

2.06

1.78

1.36

4.23

0.77

0.28

0.67

0.39

0.07

2.96

0.58

0.51

0.32

0.29

1.03

0.63

1.67 ND

0.95

0.89

3.82

2.53

6.15

2.74

1.05

1.12

0.66

1.34

1.92

3.18

0.87

2.39

0.20

0.24

ratio

mean

24 h/0 h

1.99

5.50

0.27

0.58

5.27

11.58

3.33

3.46

1.16

1.20

2.40

ND

5.23

2.60

2.18

34.81

15.19

3.97

6.71

1.35

3.99

2.32

2.32

0.71

4.97

9.47

3.33

1.36

transcript

6

14

3

0

5

9

9

14

19

6

3

2

10

7

1

3

16

4

9

14

2

13

3

2

2

7

1

2

peptides

compared 0.37

SD

2.09

2.28

7.57

3.23

2.17

1.48

1.29

1.55

0.43

0.30

1.90

4.13

7.41

0.29

ND

2.32

4.89

3.95

3.99

3.28

5.80

3.64

4.47

3.20

3.38

2.02

0.60

3.55

33.84

10.50

2.08

3.41

1.68

3.69

1.61

1.57

1.03

1.82

6.63

2.30

2.26

transcript

0.72

3.37

0.03

ND

0.59

1.89

1.37

0.86

1.19

3.25

0.60

1.49

3.55

0.23

1.02

2.39

8.40

2.84

2.55

1.78

0.89

1.15

4.11 ND

0.91

1.89

35.89 ND

9.70

11.21

14.89

6.61

4.31

3.22

3.37

4.59

1.51

3.28

5.36

0.21 ND

0.48

ratio

mean

96 h/0 h



337

Os05g0401100

Os05g0574400

Os06g0103500

O

M

M

Os07g0108300

Os07g0168000

Os07g0176900

Os07g0182100

Os07g0513000

M

M

M

M

M

Os06g0725900

Os05g0395000

O

RD

Os05g0346300

T

Os06g0669400

Os05g0301700

M

PF

Os05g0135800

D

Os06g0288200

Os04g0688300

D

O

Os04g0678700

M

Os06g0264800

Os04g0659100

M

O

Os04g0623800

M

Os06g0133900

Os04g0613600

T

M

Os04g0602100

D

Os06g0133800

Os04g0554800

PF

M

Os04g0538100 RCc3 protein

40S ribosomal protein S11

(EC 3.6.1.34)

AK064835 ATP synthase gamma chain, chloroplast

AK104002 Tryptophan synthase alpha chain

AK060861 Ribose-5-phosphate isomerase precursor

AK065622 Polyribonucleotide phophorylase

AK067732 Alanine aminotransferase


AK065019 Cell division protein ftsH homologue,

AK072047 FtsH protease (VAR2)

AK071127 Hypothetical protein


synthase

AF413082 5-enolpyruvylshikimate-3-phosphate

AK067452 Transketolase, chloroplast precursor

AK100758 Acyl-CoA oxidase

AK073698 Malate dehydrogenase

family protein

AK067179 Protein of unknown function DUF477


AK061627 40S ribosomal protein S7

AK070578 Cytochrome c1

AK066258 Pto kinase interactor 1

family protein

AK065090 Haem peroxidase, plant/fungal/bacterial

precursor

AK103940 Protochlorophyllide reductase A, chloroplast


AK065491 Glutamine synthetase shoot isozyme,

precursor

AK099234 Aminomethyltransferase, mitochondrial

()

family protein

AK069838 Haem peroxidase, plant/fungal/bacterial

()

AK058778 Elongation factor G (EF-G)

AK119676 Bet v I allergen family protein

5

11

6

9

5

17

16

17

8

4

3

29

20

11

6

6

5

7

2

11

23

13

16

6

12

1

17

64.52

160.39

90.28

149.84

74.72

281.54

260.05

257.97

138.08

63.76

42.01

453.38

279.06

191.61

89.78

74.66

70.34

108.86

32.91

182.16

326.77

214.31

233.19

73.12

170.04

17.38

265.95

45

30

53

8

48

35

40

30

29

7

63

39

42

32

12

38

31

8

48

66

44

50

33

36

9

61

39

34

Os04g0465600

131.2

T

8

O

family protein

AK061199 Glycyl-tRNA synthetase, alpha2 dimer

Os04g0398300

T

sequence

unique annotation


EST

Os number

category

mean

total

15 49 18 64

8.89 107 2.57 107 2.23 108

6.99 107

77

98 110

1.02 108

3.90 108

28

2.49 107

18

7

7.49 107

7.54 107

414

2.95 108

2.21 108

66

33

4.66 107

93

14

2.71 107

6.04 107

38

6.70 107

1.08 108

6 27

5.58 107

109

275

120

5.76 107

6.24 107

3.01 108

2.07 108

33 125

64

3.02 107

1.32 108

16

2.49 107

7.72 107

9 105

1.58 108

56

2.65 107

7


identification

functional

Table 1. Continued

7

4

6

2

10

13

19

2

5

2

52

13

9

3

2

9

2

2

8

28

20

14

1

6

3

13

2

14

peptides

compared

0.68

2.47

0.57

0.00

0.35

SD

1.92

0.79

0.78

1.26

3.57

2.41

2.56

6.29

1.60

3.29

1.53

0.66

2.19

1.18

3.02

0.96

0.30

0.30

1.44

0.18

3.47

1.24

0.89

0.50

0.49

0.81

0.72

0.63

0.25

2.34

0.16

2.46

0.16

0.12

0.95

0.15

0.47

0.36

0.09

0.22

0.30

0.03

1.47

0.25

0.79 ND

1.86

4.26

2.29

0.15

1.41

ratio

mean

24 h/0 h

5.81

1.28

4.31

2.66

4.04

14.80

8.42

1.95

5.18

0.96

4.15

0.91

1.32

5.76

0.96

0.86

0.76

0.79

3.60

0.03

7.79

9.74

1.30

8.40

0.10

8.48

0.64

1.21

transcript

8

3

5

2

7

12

9

1

3

1

47

6

5

4

0

3

1

0

6

5

16

12

2

8

2

14

0

6

peptides

compared

ND

2.22

0.00

0.63

4.78

0.04

3.30

0.44

0.49

ND

4.78

SD

1.98

0.62

1.56

2.07

0.86

ND

1.03

6.20

5.27

3.84

0.17

9.87

3.10

5.40

3.15

2.95

1.32

0.08

3.70

0.59

1.85

1.67 ND

4.17

16.84 ND

4.35

3.32

3.02

4.58

ND

3.74

0.40 ND

ND

5.38

0.01

3.21

11.10

0.19

7.20

0.77

3.11

ND

7.89

ratio

mean

96 h/0 h

5.82

1.45

2.90

0.96

4.62

6.99

4.67

1.04

2.99

1.18

2.98

1.22

1.39

2.38

0.90

0.61

0.89

0.88

0.93

0.05

3.51

6.28

0.88

3.41

0.35

2.24

1.28

0.75

transcript



338

Os12g0616900

M

Os10g0492000

O

Os12g0448900

Os10g0445600

O

D

Os10g0167300

M

Os12g0420200

Os09g0567300

D

T

Os09g0535000

M

Os12g0244100

Os09g0491100

M

PF

Os09g0277800

M

Os12g0128700

Os08g0549100

D

Os11g0707000

Os08g0502700

M

M

Os08g0424500

M

M

Os08g0379400

M

Os11g0171300

Os08g0345800

M

M

Os08g0162600

M

Os10g0571200

Os08g0102100

O

M

Os07g0657200

SG

Os10g0516100

Os07g0609000

M

M

Os07g0575100

ARM repeat fold domain containing protein

beta subunit.

AK063753 Pyruvate dehydrogenase E1 component

AK100592 Animal haem peroxidase family protein

AK111521 38 kDa ribosome-associated protein

AK121949 Heat shock 70 protein

isoenzyme AXAH-II

AK072576 Arabinoxylan arabinofuranohydrolase

AK104332 Ribulose-bisphosphate carboxylase activase

precursor

AK073758 Fructose-bisphosphate aldolase, chloroplast

(EC 2.7.1.40)

AK120979 Pyruvate kinase isozyme G, chloroplast

AK120480 Glycine decarboxylase complex H-protein

precursor.

AK064354 Chloroplast inner envelope protein, 110 kD


AK073662 Enolase 2

free radical reductase)

AK071558 Monodehydroascorbate reductase (Ascorbate

precursor

AK058712 Triosephosphate isomerase, chloroplast

AK066710 Beta-primeverosidase


AK103141 Enoyl-[acyl-carrier-protein] reductase,

AK070842 Peroxisome type ascorbate peroxidase

AK064774 Aminotransferase 2

AK071221 Betaine-aldehyde dehydrogenase

AK069608 Quinone oxidoreductase-like protein

small subunit, chloroplast precursor

AK103906 Glucose-1-phosphate adenylyltransferase

AK060121 Glyoxalase I family protein


AK072927 WD40-like domain containing protein

AK106125 Cytochrome c and b562 family protein

()

AK071634 Rieske FeS protein precursor

6

3

5

20

32

5

19

20

6

3

5

8

16

6

15

8

9

9

17

8

13

6

10

7

6

5

20

105.25

46.38

70.51

294.28

453.85

67.2

298.63

296.84

84.02

49.58

71.49

121.89

276.99

81.21

242.61

103.14

121.78

129.75

253.77

117.51

205.74

86.99

158.98

96.05

103.8

83.71

266.97

9

10

57

52

11

63

67

14

27

35

24

47

20

66

21

35

41

64

21

52

20

45

16

8

19

27

36

25

Os07g0556200

56.98

SG

4

M

pyrophosphate isomerase

AK065871 Isopentenyl pyrophosphate:dimethyllallyl

Os07g0546000

M

sequence

unique annotation


EST

Os number

category

mean

total

162 97 9 19

1.78 108 4.26 107 3.49 107

22 1.77 108

496

2.82 108 1.50 107

257

18 4.97 108

16

16

5.46 107

5.50 107

22

1.83 108

192

1.79 108

60

2.39 108

2.87 107

20 127

8.42 107

36

6.34 107

1.15 108

51

7.05 107

14

1.15 108

105

85

1.55 108

1.97 108

26

3.38 107

30

24

2.64 107

74

17

4.33 107

8.10 107

56

1.27 108

45

6.94 107

10

2.53 107

7


identification

functional

Table 1. Continued

4

1

10

19

3

30

32

2

2

2

3

2

6

19

5

3

12

11

5

8

2

2

6

4

2

10

5

2

peptides

compared

0.89

0.21

0.50

0.97

0.43

0.15

0.22

0.04

8.37

0.72

0.96

0.23

3.78

0.28

0.29

1.53

0.10

0.53

0.53

0.28

0.45

0.43

0.03

1.73

0.49

0.17

SD

3.05

1.42

0.67 ND

3.05

1.83

1.85

4.42

2.57

0.18

0.33

0.33

9.93

0.88

2.30

1.57

4.68

0.60

1.54

4.59

0.28

2.50

1.88

1.77

1.12

1.03

0.08

3.51

1.81

0.32

ratio

mean

24 h/0 h

0.89

1.26

10.80

6.22

0.62

11.01

1.88

0.64

15.28

2.10

2.63

0.57

4.77

3.69

0.35

1.91

3.65

5.32

0.96

5.17

1.70

1.18

0.96

0.74

0.66

1.03

13.61

0.95

transcript

3

2

11

17

3

30

27

2

2

3

3

2

5

21

1

2

8

9

6

10

2

2

3

2

2

5

2

0

peptides

compared

0.01

1.24

2.88

0.28

0.58

0.47

3.87

8.06

0.03

0.05

0.58

3.32

ND

SD

1.97

4.50

9.05

3.88

3.44

15.17

8.39

0.82

6.10

1.64

8.67

3.42

3.54

5.06

0.96

0.64

2.91

0.47

1.76

4.91

1.85

0.50

0.98

1.20

6.41

3.19

1.28

1.08

0.31 ND

0.14

4.03

5.46

0.78

3.12

3.84

10.76

8.78

0.24

0.33

1.43

5.46

ND

ratio

mean

96 h/0 h

1.17

1.76

8.58

2.83

0.32

7.79

1.49

1.25

8.15

0.93

0.99

0.80

2.50

1.99

0.14

1.07

3.57

3.46

1.52

3.82

0.83

1.54

1.31

0.94

1.00

0.79

11.80

0.91

transcript



annotation

339

()

()

O

M

protein

Jacalin-related lectin domain containing

AK100516 Aspartyl-tRNA synthetase

multifunctional protein

AK103620 Peroxisomal fatty acid beta-oxidation

S45168

AK062150 60S ribosomal protein L112 (L16)

AK105744 Actin-depolymerizing factor 3

AK059835 40S ribosomal protein S15A

AK101977 Elongation factor 1 gamma-like protein

(Antidisease protein 1)

AK119605 Ferredoxin I, chloroplast precursor

AK062559 Glyceraldehyde-3-phosphate dehydrogenase

reaction center subunit V)

AK059143 Cytochrome b559 alpha subunit (PSII

AK119175 Ribosomal protein L32e family protein


AK072473 Photosystem I reaction center subunit II,

AK070556 DnaJ protein homologue

10

14

4

7

4

3

12

1

11

2

6

7

9

134.56

189.89

73.44

93.53

63.83

39.73

152.44

14.47

159.59

29.08

81.72

108.22

127.36

26

29

56

43

38

22

35

10

32

33

44

12

50

mean

total

15 60 30

8.71 107 8.49 107 2.07 108

5.68 107

35

38

13

8.56 107

48

2.68 108

8

1.80 108

41

9.23 107

13

1.24 108

21

6.47 108

44

1.73 108 9.68 108

322

4.71 109

intensity peptides

7

5

8

3

3

3

7

1

5

3

3

4

18

peptides

compared

0.74

0.77

0.39

1.34

0.14

0.29

4.25

transcript

0.23 ND

0.23 ND

0.13 ND

0.65 ND

0.11 ND

0.17 ND

3.19 ND

ND

0.25 ND

0.81 ND

0.21 ND

2.20 ND

4.21 ND

SD

0.16 ND

1.23

1.72

0.71

5.47

6.97

ratio

mean

24 h/0 h

2

2

3

2

1

2

6

2

3

3

3

2

17

peptides

compared

SD

transcript

0.16

0.28

0.30

0.30

0.07 ND

0.06 ND

0.09 ND

0.02 ND

ND

0.07 ND

3.07 ND

0.34 ND

1.29 ND

1.78 ND

5.30 ND

0.54 ND

0.56

3.64

3.94

4.05

4.11

5.79

34.14 27.69 ND

51.01 26.14 ND

ratio

mean

96 h/0 h

Os numbers and annotations are based on the RAP-DB (Rice Annotation Project Database; http://rapdb.dna.affrc.go.jp/). a Annotation was corrected because the ortholog of Os01g0279100 (AT3G56940) has been recently shown to encode magnesium-protoporphyrin IX monomethyl ester (oxidative) cyclases (Bang et al., 2008). b Annoation was corrected using the salad database (http://salad.dna.affrc.go.jp/CGViewer/v1.0/cgv_search.jsp?seq_acc=Os03g0827700&lang=null). Functional categories are as follows: M, metabolism; T, translation; PF, protein fate; RD, RNA/DNA-related proteins ; SG, signal transduction; D, defense; O, others or unknown.

()

()

T

T

()

()

D

PF

()

M

()

()

M

T

()

T

()

()

M

T

()

PF

sequence

unique

EST


Os number

category

identification

functional

Table 1. Continued




ARTICLE

Figure 4. Fluctuations in the protein and mRNA levels of enzymes involved in chlorophyll synthesis. The chlorophyll synthesis pathway is shown together with inserts indicating the relative mRNA levels (red lines) and protein levels (black bars) of the respective enzymes. The inserts are positioned in this schematic diagram at the reaction that each enzyme catalyzes. The protein and mRNA levels of the respective components are shown relative to their levels in nonilluminated shoots (set at a reference value of 1).

common peptides that could be derived from different proteins encoded by a gene family. In this manner, we could compare the relative abundance of 14 different proteins and found good correlations between the relative MS intensities and the protein levels (Figure 1D). The standard curves we obtained for 13 out of 14 different proteins were similar. However, the slope of the standard curves was slightly higher for the remaining protein. We therefore concluded that the data obtained with the nano flow HPLC-Chip/MS system can be regarded as semiquantitative.

of a typical light-inducible gene, the chlorophyll a/b binding protein gene (Cab), was also found to be induced in proportion to the increase in the chlorophyll content under our conditions (Figure 2C). On the other hand, the total protein amount per tissue was similar in every sample of the dark-grown seedlings or the seedlings illuminated for 24 or 48 h, whereas these levels were much higher in the seedlings illuminated for 72 or 96 h and peaked after 96 h (Figure 2D). We therefore prepared whole protein samples from an equivalent weight of seedling shoots illuminated for 0, 24, and 96 h and analyzed them by SDS-PAGE (Figure 2E). The results indicated that (1) the expression of most proteins is unaffected by illumination; (2) both light-inducible and repressible proteins were present in our samples; and (3) fluctuations of the abundance of some proteins were already evident after illumination for 24 h, but for other proteins were evident only in the seedlings illuminated for 96 h. We therefore performed comparative proteome analysis using the whole

Samples for Proteome Analysis

For comparative proteome analysis of the rice greening process, we used the shoots of rice seedlings that had been grown for 13 days in the dark and then illuminated for four days (Figure 2A). The total chlorophyll content was found to have gradually elevated during the last four days of this period (Figure 2B) and did not plateau within 96 h, and the expression 340



ARTICLE

Figure 5. Fluctuations in the protein and mRNA levels of enzymes involved in the Calvin cycle. The metabolic pathway is shown together with inserts indicating the relative mRNA levels (red lines) and protein levels (black bars) of the indicated enzymes. Missing black bars or red lines indicate “not determined”. The inserts are positioned in the schematic diagram at the reaction that each enzyme catalyzes. The protein and mRNA levels of the respective components are shown relative to the levels in nonilluminated shoots (set at a reference value of 1).

proteins prepared from the same weight of fresh shoots from seedlings illuminated for 0, 24, or 96 h. We also prepared RNA samples from the same shoot samples to directly compare expression changes in proteins and their corresponding transcripts.

or decrease by more than 3-fold under illumination for 24 or 96 h (Figure 3B). However, the reliability of fluctuation was high for only 127 different proteins, which are listed in Table 1, as these products were identified using MS spectra of multiple peptides of intensities greater than 1 107. In previous proteome analyses using rice seedlings, 52 different proteins were found to be upregulated or down-regulated during greening.24 Hence, our current data expand the coverage of light-inducible or -repressible proteins in rice. Indeed, our list includes not only proteins that were identified in previous proteome analysis, such as those that function in photosynthetic electron transfer and enzymes for carbon assimilation,24 but also a number of proteins that have not yet been identified in this context. Catalase isozyme (Os03g0131200) is one such example. Our current results indicate that the levels of this protein were elevated 2.5-fold following 24 h of illumination and 9.7-fold after a 96 h illumination. Consistently, in previous studies using conventional biochemical analyses, it has been shown that catalase activity is induced in maize under similar conditions.39 SBPase is a key enzyme of the Calvin cycle that catalyzes the dephosphorylation of sedoheptulose 1,7-bisphosphate to sedoheptulose 7-phosphate.40 SBPase (Os04g0234600) was also identified as a light-inducible protein (Table 1). However, in a previous proteomic screen, SBPase was revealed to be a protein that is down-regulated by illumination.24 In our current study, therefore, we performed immunoblotting analysis using antibodies against SBPase, together with antibodies against GS2 (Os04g0659100). GS2 is a key enzyme in photosynthetic ammonia assimilation in chloroplasts41 and it was also identified as a light-inducible protein in our current proteome analysis (Table 1). The results indicated the light-inducible accumulation of both SBPase and GS2 (Figure 3C), further supporting the accuracy of our comparative proteome experiments. We note

Comprehensive and Comparative Proteome Analysis of the Greening Process Using Rice Seedling Shoots

Using shoots of rice seedlings that were illuminated for various periods, we identified more than 4000 different peptides and more than 800 different proteins in each independent assay (Table S1, Supporting Information). Moreover, 45571 peptide identifications in total in three independent assays led to the identification of 1751 different proteins in total (Table S2 and S3, Supporting Information). However, we could calculate relative abundance of only 886 different proteins that were detected in both illuminated and nonilluminated seedlings in a consecutive assay (Table S2, Supporting Information). As this panel included both proteins identified with two or more unique peptides (786 different proteins) and proteins identified with a single unique peptide (100 different proteins), we show in Figure S1 the MS/ MS figures of the single unique peptides that were used for protein identification (Supporting Information). The identified proteins were classified into seven functional categories based on the information from RAP-DB (Figure 3A). About 40% of these products were classified as functionally associated with metabolism, whereas a relatively small number (5%) were classified as signal transduction-related proteins, probably reflecting the different levels of this type of functional protein in the plant cells. The levels of the identified proteins under conditions of illumination were calculated relative to the levels in the nonilluminated shoots, which were set as 1 in each case. The expression of 222 different proteins was suggested to increase 341



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Figure 6. Fluctuations in the protein and mRNA levels of photosystem components. Inserts indicating the relative mRNA levels (red lines) and protein levels (black bars) of the indicated factors are positioned below the complexes that include the identified proteins in the schematic diagram. The protein and mRNA levels of the respective components are shown relative to the levels in nonilluminated shoots (set at a reference value of 1).

that the results of searches using the SALAD database (http:// salad.dna.affrc.go.jp/salad/en/) suggested that SBPase and GS2 genes are single copy genes in rice as well as in Arabidopsis.42,43 Recently, we succeeded in indentifying two proteins with a decreased abundance in transgenic rice plants overexpressing a stress protein by proteome analysis of lipid rafts using our MS intensity based label-free method. We confirmed this result by immunoblotting analysis using newly generated antibodies against the two proteins (Ishikawa et al., unpublished data). This further validates the accuracy of the MS intensity based label-free method in the proteomic screening of both up- and down-regulated proteins.

at the mRNA and protein levels in which a delay in protein synthesis was considered, the correlation between the fluctuations at these levels for light inducible and repressible proteins was found. This comparison was performed for 569 of the genes in Table S2 (Supporting Information), because their relative protein and mRNA levels were obtained from both 24- and 96-h illuminated seedlings. This result is summarized by heat map analysis shown in Figure 3E. Among the 134 proteins for which the mRNA levels were elevated by more than 3-fold after a 24-h illumination, 53 and 27 proteins showed more than 3-fold and 23-fold increases at the protein level in the 96-h illuminated seedlings, respectively, whereas only 4 and 5 proteins showed more than 3- and 23-fold increases at the protein level in the 96-h illuminated seedlings. On the other hand, among 20 proteins for which mRNA levels were lowered more than 3-fold following a 24-h illumination, 8 and 2 proteins showed more than a 2-fold decrease and increase at the protein level in the 96-h illuminated seedlings, respectively. The relative mRNA levels for 344 proteins were barely affected by a 24-h illumination with relative values of between 0.5 and 2. Among these factors, 49 and 59 proteins showed more than a 3-fold increase or decrease and 23-fold increase or decrease at the protein level in the 96-h illuminated seedlings, respectively.

Correlation between Gene Expression Fluctuations at the Protein and mRNA Levels in Illuminated Rice Seedlings

Using RNA extracted from the same shoot samples used in the proteome analysis, we performed DNA microarray analysis to investigate the correlation between expression changes at the protein and mRNA levels of each of the genes during greening. We obtained data on the fluctuation at the mRNA level during greening for 844 identified proteins, (for the 42 further proteins identified by MS, microarray probes were not available). The fluctuations at the mRNA level are also shown in Table S2 (Supporting Information). In scattered plots of the relative protein levels versus the corresponding mRNA levels, the correlation coefficient was too small to suggest any correlation between the changes in individual genes upon illumination at the protein and mRNA levels (Figure 3D). However, when we performed a detailed comparison of the expression profiles

Effects of Light on the Expression of Enzymes Involved in the Chlorophyll Biosynthesis Pathway in Rice Seedlings

The detection of a variety of proteins associated with a particular metabolic pathway, which was beyond the coverage of previous analyses, allowed us to evaluate whether or not 342



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Figure 7. Fluctuations in the protein and mRNA levels of enzymes involved in glycolysis and the TCA cycle. The metabolic pathways are shown with inserts depicting the relative mRNA levels (red lines) and protein levels (black bars) of the indicated enzymes. The inserts are positioned in the schematic diagram at the reaction that each enzyme catalyzes. The protein and mRNA levels of the respective components are shown relative to the levels in nonilluminated shoots (set at a reference value of 1).

these factors accumulate during light-induced greening in a synchronized manner. Indeed, we identified 12 different proteins involved in nine different enzymatic steps of chlorophyll biosynthesis (Figure 4), while only three such enzymes had been identified previously in proteome analysis of maize greening using the shotgun method.25 Our nine identified proteins are glutamyl-tRNA synthetase (Os10g0369000), glutamate-1-semialdehyde 2,1-aminomutase (glutamate-1-semialdehyde aminotransferase) (Os08g0532200), delta-aminolevulinic acid dehydratase (Os06g0704600), porphobilinogen deaminase (Os02g0168800), uroporphyrinogen decarboxylase (Os03g0337600 and Os01g0622300), coproporphyrinogen III oxidase (Os04g0610800), magnesium-chelatase (Os03g0563300 and Os03g0323200), magnesium-protoporphyrin IX monomethyl ester cyclase (Os01g0279100), and NADPH:protochlorophyllide oxidoreductase (Os10g0496900 and Os04g0678700). The relative levels of these proteins, except for the Os01g0622300 product (uroporphyrinogen decarboxylase), were estimated using the mean intensities of multiple distinct peptides with values of over 1 107. Illumination was found to induce the transient accumulation of six mRNAs, but the levels of the corresponding protein enzymes were observed to be almost constant during greening (Figure 4). This is consistent with a previous study suggesting that all enzymes required to convert δ-aminolevulinic acid into

chlorophyll are present in plastids, even in dark-grown leaves.44,45 However, slight increases in the protein levels of glutamyl-tRNA synthetase and glutamate-1-semialdehyde 2,1aminomutase might be associated with an increase in acceleration of chlorophyll biosynthesis during greening, because the acceleration of chlorophyll biosynthesis is known to be accompanied by an increase in the level of δ-aminolevulinic acid-forming activity and inhibited by a protein synthesis inhibitor.44 As observed in previous proteome analysis of maize seedlings,25 the protein levels of magnesium-chelatase, an enzyme that is located at the branch point between the heme and chlorophyll biosynthesis pathways,46 does not increase during greening. Interestingly, strong reductions in the protein and mRNA levels of an isoform of NADPH:protochlorophyllide oxidoreductase (Os04g0678700) were detected in our current analysis, while the expression of another isoform (Os10g0496900) was found not to be reduced by illumination. This finding is consistent with the previous finding that the NADPH:protochlorophyllide oxidoreductase genes are differently regulated by light in Arabidopsis.46 Expression of Calvin Cycle Enzymes Is Up-regulated by Light in a Synchronized Manner in Rice Seedlings at Both the Protein and mRNA Levels

A number of enzymes that function in the Calvin cycle were identified in our current analyses. Unlike the enzymes that operate 343



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Figure 8. Fluctuations in the protein and mRNA levels of enzymes involved in (A) photorespiration, (B) nitrogen assimilation and (C) sucrose synthesis. The metabolic pathways are shown in the schematic diagram together with inserts indicating the relative mRNA levels (red lines) and protein levels (black bars) of the indicated enzymes. The inserts are positioned in the schematic diagram at the reaction that each enzyme catalyzes.

in the chlorophyll synthesis pathway, the Calvin cycle enzymes were found to be coordinately up-regulated at both the mRNA and protein levels in illuminated rice seedlings. In Figure 5, we summarize the fluctuations in the transcript and protein levels of two isoforms of ribulose 1,5-bisphosphate carboxylase/oxygenase (RuBisCO) activase (Os11g0707000 and Os11g0707100) and also carbonic anhydrase (Os01g0639900), in addition to 11 Calvin cycle proteins, including the large (AK105600) and small (Os12g0291200) subunits of RuBisCO, 3-phosphoglycerate kinase (Os05g0496200), glyceraldehyde-3-phosphate dehydrogenase (Os03g0129300), triosephosphate isomerase (Os09g0535000), fructose bisphosphate aldolase (Os11g0171300 and Os01g0118000), transketolase (Os06g0133800), SBPase (Os04g0234600), ribose 5-phosphate isomerase (Os07g0176900), and phosphoribulokinase (Os02g0698000). The relative expression levels of all of these proteins, except for isoforms of fructose-bisphosphate aldolase (Os01g0118000) and RuBisCO activase (Os11g0707100), were calculated from the MS intensities of multiple unique peptides, indicating a high confidence in the results. In the case of Calvin cycle-related proteins, increases in the mRNA abundance prior to increases in protein expression were clearly observed. Many of the enzymes that function in the Calvin cycle showed a stronger induction at the mRNA level after 24 h of illumination, while induction at the protein level was stronger after 96 h of illumination. Furthermore, the induction levels of these enzymes were found to be similar.

I (PSI), photosystem II (PSII) or the cytochrome b6f complex. These proteins are PsaE (Os07g0435300), PSI-D (AK072473), plastocyanin (Os07g0112700 and Os06g0101600), ferredoxin-NADP+ reductase (Os06g0107700), apocytochrome f (Os01g0881700), Rieske [2Fe-2S] protein (Os11g0242400 and Os07g0556200), 23 kDa polypeptide (Os07g0141400), PsbS (Os01g0869800), cytochrome b559 (AK059143), oxygen-evolving enhancer protein (Os07g0544800 and Os01g0501800), ATP synthase gamma chain (Os07g0513000 and AK105071) and H+-transporting ATP synthase (Os03g0278900 and Os02g0750100). Although the relative abundance of plastocyanin (Os07g0112700) was calculated using the MS intensity of a unique peptide, all others were assayed using the MS intensities of multiple unique peptides. The results indicated that only half of these proteins are induced during greening (Figure 6). This contrasts with previously reported data from maize where a number of proteins related to the photosystem were found to be coordinately up-regulated during greening.25 This may suggest that different mechanisms are predominant regulators of the photosystem in different plant species. Earlier studies have also suggested that the coordinated accumulation of the subunits of the photosynthetic complexes is achieved at the posttranslational level by plastid proteases, which degrade excess subunits.47,48 It is noteworthy in this regard that we observed a moderate increase (about 5-fold) in FtsH protease (VAR2) (Os06g0669400), a thylakoid protease that plays a role in the light-induced turnover of the photosystem II D1 protein47 (Table 1).

Effects of Light Illumination on the Expression of Photosystem-related Proteins in Rice Seedlings

Effects of Light Induction on the Protein and mRNA Levels of Glycolysis and TCA Cycle Enzymes

Similar to Calvin cycle-related proteins, photosystemassociated proteins are also chloroplastic. We identified 18 proteins from our screen that are components of photosystem

Light regulation of the enzymes involved in respiratory carbon metabolism has long been a subject of debate.48 Although 344



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catalyze a sequential enzymatic process in the GS/GOGAT cycle for photosynthetic nitrogen assimilation, GS2 (Os04g0659100) and chloroplastic glutamate synthase (Os07g0658400), were identified in our current proteomic screen (Figure 8A). The protein levels of these enzymes were found to increase in a coordinated manner during greening, consistent with a previous observation that these enzymes were induced at their mRNA levels in Arabidopsis and maize.50,51 In addition to these chloroplast systems, a metabolic pathway that requires the presence of multiple organelles was found to be activated in a coordinated manner during greening in rice seedlings. Three enzymes for photorespiration, RuBisCO (AK105600 and Os12g0291200) in chloroplasts, glycolate oxidase (Os03g0786100) in peroxisomes and glycine decarboxylase (Os10g0516100) in mitochondria, were also found to be up-regulated during greening (Figure 8B). On the other hand, the protein levels of the sucrose synthesis enzymes did not appear to be affected under illumination conditions (Figure 8C). This may be due to the importance of posttranslational control, such as the phosphorylation of sucrosephosphate synthase,52 in sucrose synthesis. Differential Accumulation of Components of the Cytosolic and Chloroplastic Translation Machineries and of Isoforms of ATP Synthase during Rice Greening

Figure 9. Heat map analysis of proteins that function in translation in the (A) cytosol and (B) chloroplasts, and also (C) isoforms of ATP synthase. The relative protein and mRNA levels of the respective components are shown using different colors in comparison with the levels in nonilluminated shoots (set at a reference value of 1). The relative expression levels of chloroplast (CP), vascular (VC) and mitochondrial (MT) ATP synthases are shown in C.

previously reported microarray analysis in Arabidopsis has identified light inducible genes encoding enzymes in the TCA cycle,20 some later studies have indicated that few such genes are responsive to light in Arabidopsis.49 In terms of enzymes involved in glycolysis or the TCA cycle, we here identified cytosolic forms of triosephosphate isomerase (Os01g0841600 and Os01g0147900), glyceraldehyde-3-phosphate dehydrogenase (Os04g0486600 and AK062559), phosphoglycerate kinase (Os02g0169300), phosphoglycerate mutase (Os01g0817700), enolase (Os10g0167300), phosphoenolpyruvate carboxylase (Os08g0366000), citrate synthase (Os02g0194100), pyruvate kinase (Os04g0677500 and Os11g0148500), pyruvate dehydrogenase E1 (Os04g0119400, Os12g0616900 and Os02g0739600), aconitate hydratase (Os03g0136900), NADP-isocitrate dehydrogenase (Os01g0654500), 2-oxoglutarate dehydrogenase (Os04g0390000), succinate dehydrogenase (Os07g0134800), and malate dehydrogenase (MDH) (Os05g0574400) (Figure 7). In contrast to the strong inductions of the Calvin cycle-related enzymes, the protein and mRNA levels of the TCA cycle-related enzymes were mostly found to be unchanged during greening in our current analysis. The strongest induction of the TCA cycle enzymes was that of MDH (∼4 fold). It is has also been shown previously that two genes encoding MDH are induced by light via the photoreceptors phyA and phyB.49 However, this induction has been characterized not for the function of MDH in the TCA cycle but for its additional role in the transport of malate/oxaloactetate between organelles.49 We also found an increase in the abundance of glyoxysomal MDH (Os03g0773800) by about 4-fold during greening in our current analyses (Table 1). Enzymes Involved in Photorespiration and Nitrogen Assimilation Are Up-regulated during Greening

Similar to the Calvin cycle and photosystem, the GS/GOGAT cycle is also active in chloroplasts. Two of the enzymes that

It has been shown that light induces chloroplast translation initiation and elongation in a general manner.17,53 Since many eukaryotic and chloroplastic ribosomal proteins and eukaryotic and prokaryotic elongation factors (eIF) were identified in our current analysis, we compared the fluctuations of these components under illumination by generating heat maps (Figure 9A and B). The components involved in eukaryotic translation in the cytosol were found not to be induced at any level during greening, whereas the transcript levels of components of the prokaryotic translation machinery were observed to be coordinately elevated after 24 h of illumination. The corresponding proteins were also induced to a reduced extent, indicating that there is a selective enhancement of chloroplast pathways during greening. Because we identified several subunits of different isoforms of ATP synthase that are present in the cytosol, chloroplast and mitochondria (Figure 9C), we investigated the differential responses of these isoforms to greening using heat map analysis. The results indicated that these chloroplast isoforms are selectively induced during greening but that the cytosolic and mitochondrial ATPases are not. These findings further indicate that comparative proteome analysis can effectively clarify the selective induction of particular functions in plant cells.

’ CONCLUDING REMARKS In our current assay system, the MS-intensities were found to be almost proportional to the protein levels in a consecutive assay if these levels were sufficient to produce a strong signal, suggesting that the MS intensity-based label-free method enables a semiquantitative analysis of relatively abundant proteins. Application of this method to the proteomic characterization of the greening process in rice, together with the corresponding transcriptome analysis, allowed us to define the differential accumulation of proteins involved in different metabolic pathways and the coordinate accumulation of proteins associated with a particular metabolic pathway, following increases in the accumulation of the corresponding mRNAs. Our present data therefore suggest that this method is a viable alternative choice for comprehensive and comparative proteome analysis. This method 345



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may for example be able to assist in drawing an outline of a plant response to various stimuli and screen for novel proteins that are modulated in response to particular environmental stimuli. At the same time, however, it must be noted that this method appeared not to be suitable for the comparative analysis of rare proteins for which the MS intensity is below the threshold of the dynamic range of this assay system. Hence, some methodological improvements may be necessary in the future to expand the applicability of this method.

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’ ASSOCIATED CONTENT

bS

Supporting Information The numbers of proteins identified in each proteomic screen are listed in Table S1. Proteins for which the expression levels could or could not be calculated are listed in supplemental Tables S2 and S3. The MS/MS figures of the peptides that were used for protein identification based on detection of a single unique peptide are shown in Figure S1. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Dr. Shuichi Yanagisawa, Biotechnology Research Center, The University of Tokyo, Yayoi 1-1-1, Tokyo 113-8657, Japan. E-mail: [email protected].

’ ACKNOWLEDGMENT We thank Dr. Tomoyuki Yamaya (Tohoku University, Japan) and Dr. Shigeru Shigeoka and Dr. Masahiro Tamoi (Kinki university, Japan) for generously providing anti-GS2 and antiSBPase antibodies, respectively. We also thank Dr. Toshiki Ishikawa and Dr. Maki Kawai-Yamada (Saitama University) for generous permission for mentioning the unpublished data of our collaboration. This work was supported by a grant from Core Research for Evolutional Science and Technology (CREST) Project of the Japan Science and Technology Agency (JST), and KAKENHI (21114004 and 22380043) from The Ministry of Education, Culture, Sports, Science and Technology of Japan. ’ REFERENCES (1) Aebersold, R.; Mann, M. Mass spectrometry-based proteomics. Nature 2003, 422, 198–207. (2) Peck, S. C. Update on proteomics in Arabidopsis. Where do we go from here? Plant Physiol 2005, 138, 591–599. (3) Washburn, M. P.; Wolters, D.; Yates, J. R., 3rd Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 2001, 19, 242–247. (4) Jung, E.; Heller, M.; Sanchez, J.-C.; Hochstrasser, D. F. Proteomics meets cell biology: The establishment of subcellular proteomes. Electrophoresis 2000, 21, 3369–3377. (5) van Wijk, K. J. Challenges and prospects of plant proteomics. Plant Physiol. 2001, 126, 501–508. (6) Canovas, F. M.; Dumas-Gaudot, E.; Recorbet, G.; Jorrin, J.; MocK, H.-P.; Rossignol, M. Plant proteome analysis. Proteomics 2004, 4, 285–298. (7) Marouga, R.; David, S.; Hawkins, E. The development of the DIGE system: 2D fluorescence difference gel analysis technology. Anal. Bioanal. Chem. 2005, 382, 669–678. (8) Ong, S. E.; Foster, L. J.; Mann, M. Mass spectrometric-based approaches in quantitative proteomics. Methods 2003, 29, 124–130. 346



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Proteomic Characterization of the Greening Process in Rice Seedlings

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