Open Access

A label-free differential quantitative mass spectrometry method for the characterization and identification of protein changes during citrus fruit development

  • Ehud Katz1,
  • Mario Fon1,
  • Richard A Eigenheer2,
  • Brett S Phinney2,
  • Joseph N Fass3,
  • Dawei Lin3,
  • Avi Sadka4 and
  • Eduardo Blumwald1Email author
Proteome Science20108:68

DOI: 10.1186/1477-5956-8-68

Received: 20 October 2010

Accepted: 16 December 2010

Published: 16 December 2010

Abstract

Background

Citrus is one of the most important and widely grown commodity fruit crops. In this study a label-free LC-MS/MS based shot-gun proteomics approach was taken to explore three main stages of citrus fruit development. These approaches were used to identify and evaluate changes occurring in juice sac cells in various metabolic pathways affecting citrus fruit development and quality.

Results

Protein changes in citrus juice sac cells were identified and quantified using label-free shotgun methodologies. Two alternative methods, differential mass-spectrometry (dMS) and spectral counting (SC) were used to analyze protein changes occurring during earlier and late stages of fruit development. Both methods were compared in order to develop a proteomics workflow that could be used in a non-model plant lacking a sequenced genome. In order to resolve the bioinformatics limitations of EST databases from species that lack a full sequenced genome, we established iCitrus. iCitrus is a comprehensive sequence database created by merging three major sources of sequences (HarvEST:citrus, NCBI/citrus/unigenes, NCBI/citrus/proteins) and improving the annotation of existing unigenes. iCitrus provided a useful bioinformatics tool for the high-throughput identification of citrus proteins. We have identified approximately 1500 citrus proteins expressed in fruit juice sac cells and quantified the changes of their expression during fruit development. Our results showed that both dMS and SC provided significant information on protein changes, with dMS providing a higher accuracy.

Conclusion

Our data supports the notion of the complementary use of dMS and SC for label-free comparative proteomics, broadening the identification spectrum and strengthening the identification of trends in protein expression changes during the particular processes being compared.

Background

Fruit ripening and development has being studied using transcriptomic, proteomics, and metabolomics approaches [18]. Quantitative proteomics provides an alternative approach for studies of fruit development. In the last few years, quantitative proteomics has been widely applied for the quantification of complex biological samples [911]. The most commonly used approach for comparative proteomic analysis of plant tissues is the application of 2DE-gels. This method is limited in sensitivity, has a low dynamic range, it is inefficient when analyzing insoluble proteins or proteins with very high or low molecular mass and are limited in their reproducibility [12], although reproducibility has been improved with the use of differential imaging gel electrophoresis (DIGE) [13, 14]. Alternative techniques to 2DE-gels are non-gel LC-MS/MS-based shotgun proteomics [1518], where quantification is performed using the mass-spectrometer data. Some success for the quantification of proteins has been achieved by using stable isotope labeling, 15N, 13C, 2H and SILAC [19], ICAT [20, 21], iTRAQ [22] and 18O stable isotope incorporation [23]. One of the main limitations of these methods is that full labeling of the proteins is rarely achieved and that different peptides incorporate the label at different rates which complicates data analysis. Recently, a label-free method for comparative proteomic analysis has emerged [911, 24].

Label-free proteomics allows for the quantification of peptides using spectral characteristics such as retention time, m/z ratio and peak intensity by comparing the direct mass spectrometric signal intensity for any given peptide (differential Mass Spectrometry, dMS) or by counting the number of acquired tandem mass spectra matching to a specific peptide as an indicator for their abundance in a given sample (spectral counting, SC) [25, 26]. dMS is based on comparisons of chromatographic peaks of peptide precursor ion measurements belonging to a specific protein extracted from an LC-MS/MS run [2732]. This approach is based on the observation that dMS in most cases is proportional to the concentration of the peptide in the sample investigated [10, 2729]. Peak intensity for every individual spectrum is determined and the comparison of spectra between multiple LC-MS runs provides quantitative measurement of thousands of peptides. From this massive data a selected list of differential peptides can be produced for subsequent fragmentation by LC-MS/MS for sequence determination and protein identification. In order to match the massive spectra data according to retention time and precursor m/z characteristics various software have been developed. Once matched, expression ratio in peak intensity is calculated according to peak areas corresponding to the matched peptides. SC counting is based on counting and comparing the number of spectra identifying specific peptides of a given protein to assess relative protein abundance, also found to be in good correlation with protein abundance [15, 30].

Proteomics has been used successfully to characterize and identify changes in plant protein compositions during different developmental stages [3, 5, 33, 34], and proteomic comparative analysis of citrus fruits, mainly using 2DE-gels, have been published recently [3538].

Label-free comparative proteomics is a relatively new approach that has been used successfully in different systems (humans, yeast, fly, etc.) [3942], but its application in plants is scanty [26, 43]. Using LC-MS/MS we recently analyzed soluble and enriced membrane fractions of mature citrus fruit to identity the proteome of fruit juice cells and classified these proteins according to their putative function according to known biosynthetic pathways [18]. Here, we describe a method for the use of label-free LC-MS/MS-based shotgun differential proteomics for the study of fruit development in Citrus, a non-model plant lacking a fully sequenced genome. The method combines the use of dMS and SC and the creation of iCitrus, a citrus fruit-specific database and interface, for the identification of the protein changes occurring during the development of citrus fruits.

Results

Citrus proteins annotations using iCitrus

Although the citrus genome has not been fully sequenced yet, a comprehensive citrus EST database has been developed in the past few years [44]. Several groups have contributed to EST sequencing efforts using different species, including C. sinensis (sweet orange), C. clementina (Clementine mandarin), C. paradisi (grapefruit), Poncirus trifoliata, and other hybrids (C. sinensis × Poncirus trifoliata, Carrizo citrange). A wide range of libraries derived from multiple reproductive and vegetative tissues at different developmental stages were used in addition to different treatments or stresses to create a relatively large database. To date, there are 582,334 citrus sequences in the National Center for Biotechnology Information (NCBI) EST database. With the advantage of comprehensive sequence dataset in hand, there were many challenges to be addressed before using the databases for proteomic research. Some of these challenges arose from the nature of EST databases, over-representation of highly-expressed genes (and the underrepresentation of weakly-expressed genes), redundancy, incomplete sequences, poor annotation etc. The challenge of using the EST database for proteomics came from the fact that a highly redundant database with many similar sequences would artificially decrease the significance of potential "hits". On the other hand, a strong reduction in sequence-based redundancy, relying on sequence similarity rather than identity, would significantly reduce the number of possible hits. To solve some of these problems, iCitrus http://​citrus.​bioinformatics.​ucdavis.​edu/​ was created (Figure 1). The iCitrus collected dataset was produced by excluding sequences shorter than 50 amino acids between stop codons and removing redundant sequences with 100% identity to another longer sequence in the dataset. Similar sequences, sharing less than 100% similarity were kept for spectra search. Keeping sequences sharing high similarity (97-99% identify) was a necessity because the citrus ESTs database consists of sequences originated from a wide range of citrus cultivars and species. Minor differences in nucleotide sequences between similar ESTs could lead to differences in amino acid sequences and therefore to differences in virtual spectra derived from the database during mass-spectra search. Keeping these sequences served to broaden our chances of identifying proteins in the databases while discarding them could lead to miss-identification or no identification of proteins. A disadvantage of this approach was the redundancy of accessions that were dealt with by manually aligning the sequences of the proteins of interest. In a few cases where the accessions shared a high similarity, the redundancy resulted in the identification of two or more ESTs with only one peptide. If these ESTs belong to the same unigene, then two or more peptides could identify the same specific protein.
https://static-content.springer.com/image/art%3A10.1186%2F1477-5956-8-68/MediaObjects/12953_2010_Article_220_Fig1_HTML.jpg
Figure 1

iCitrus database. Three major sources were used in creating iCitrus dataset: UC Riverside HarvEST:citrus (C46 assembly), NCBI/citrus/unigenes and NCBI/citrus/proteins (see text). The first two datasets were translated into all 6 reading frames, split at stop codons, and sequences shorter than 50 amino acids were removed. These were combined with the NCBI protein sequences, and all three protein sequence sets were then clustered at 100% identity using CD-HIT http://​bioinformatics.​ljcrf.​edu/​cd-hi/​, meaning that sequences that aligned with 100% identity to a longer sequence in the combined set were removed. All remaining sequences were then blasted to TAIR proteins, and separately to the subset of NCBI's nr database belonging to taxa within Viridiplantae, to collect GO-term and descriptive annotation for the clustered sequences.

To date, there are 62,415 sequences in the iCitrus collected database; 41,018 from the HarvEST:Citrus assembly http://​harvest.​ucr.​edu/​, 20,949 from NCBI's unigenes (C. sinensis and C. clementina), and 448 from NCBI's proteins (C. sinensis and C. clementina) (Figure 1). iCitrus dataset in a FASTA file format and a description of the iCitrus interface structure can be found as Additional File 1 and a conversion table of HarvEST:Citrus, NCBI/Citrus/ESTs and NCBI/Citrus/Proteins accessions into iCitrus accessions can be found in Additional File 2: Table S1.

Label-free LC-MS/MS based shotgun proteomics, differential Mass-Spec and Spectral Counting

To achieve a better identification of differentially expressed proteins during fruit development and to decrease sample complexity, the juice sac cells were fractionated into soluble and membrane-bound proteins (Figure 2). Two alternative strategies for label-free mass spectrometric analysis; peptide ion intensities measurements and spectral counting were used. The peptide ion intensities measurements, also referred as differential Mass Spec (dMS), integrate the peak area which is proportional to the concentration of the peptide in the sample (Additional File 3: Figure S1). Determining the area for each mass extracted peptide ion chromatogram retention time pair and comparing the areas between multiple LC-MS runs of different samples can provide a comprehensive quantification of thousands of peptides within samples. The alternative strategy, Spectral Counting (SC), calculates the number of MS/MS scans that are attributed to the same peptide ion. The frequency of these MS/MS scans correlates with the abundance of a given peptide in the sample. In this study we have used dMS strategy to analyze and identify differential proteins changes during fruit development in citrus juice sac cells (Figure 2) and SC as an alternative strategy to validate our finding. Identification of proteins was done by MS spectra search against the iCitrus database and annotations by using the iCitrus interface.
https://static-content.springer.com/image/art%3A10.1186%2F1477-5956-8-68/MediaObjects/12953_2010_Article_220_Fig2_HTML.jpg
Figure 2

Experimental design. Soluble and membrane-bound proteins were extracted from juice sac cells from at least 20 fruits at three stages of Citrus fruit development (early Stage II, Stage II and Stage III) and pooled at each stage. Five technical repeats of each pooled sample (older vs younger fruit) were each analyzed by SIEVE using blanks (washes) between each sample run. Comparisons were conducted in pairs in the following: Stage II vs. early Stage II and stage III vs. Stage II. Methods as described in Experimental Procedures.

Label-free relative quantitative analysis detects, selects and compares spectra that are significantly different between samples (either by dMS or SC). However, many of the spectra that were selected as being different in their intensity or abundance were found to be not statistically different between the developmental stages compared and will be discussed later.

Using dMS, 1494 and 1364 proteins were identified by at least two peptides in the comparisons between Stage II (55 mm fruit diameter) versus early Stage II (35 mm fruit diameter) and Stage III (80 mm fruit diameter) versus Stage II, respectively (Figure 3). A high number of identified proteins were down- and up-regulated during the earlier and later stages of development, respectively (Figure 3a).
https://static-content.springer.com/image/art%3A10.1186%2F1477-5956-8-68/MediaObjects/12953_2010_Article_220_Fig3_HTML.jpg
Figure 3

Numbers of protein identified by dMS and SC.

Accessions identified by SC and dMS were compared using both iCitrus and Arabidopsis homologs (Figure 4). These comparisons were made to minimize possible redundancies of identified citrus ESTs and to conserved citrus protein accessions that might originate from different unigenes but belonging to the same gene family. Once again, aconitase can provide a good example for database redundancy as the accessions 45840 and 47264, sharing 99% amino acid similarity, are essentially the same unigene originating from two different citrus species (Table 1). These accessions shared little similarity with 39802 and sequence alignment showed that their sequences did not overlap but shared high homology with the other members, i.e. 55395 and 43680. Notably, some proteins did not share homology to any Arabidopsis proteins, providing support to the use of citrus accessions for comparisons. In some cases, these accessions could be assembled to one contig while in other cases these ESTs could not be assembled. Two possibilities arose, either these EST sequences originated from the same gene but did not overlap, therefore could not be assembled, or these ESTs were originated from different genes belonging to the same family.
https://static-content.springer.com/image/art%3A10.1186%2F1477-5956-8-68/MediaObjects/12953_2010_Article_220_Fig4_HTML.jpg
Figure 4

Venn diagrams representing the number of proteins identified by both dMS and SC workflows and the number of proteins identified by only one of the workflows. (a) Analysis was conducted by using iCitrus accessions of the identified proteins (b) Analysis was conducted by using the corresponding Arabidopsis homologs of the same iCitrus accessions presented in (a).

Table 1

Identification and quantification of aconitase by dMS and SC.

dMS

  

Stage II vs. early Stage II

Stage III vs. Stage II

      

HarvEST Citrus ID

iCitrus ID

Blast Hit to TAIR ID

Ratio

Pvalue

No. peptides

Max. Xcorr

Ratio

Pvalue

No. peptides

Max. Xcorr

      

UC46_7402

39802

AT2G05710

24.9

9.9E-20

3

5.39

33.6

3.621E-07

4

4.56

      

UC46_5405

43680

AT2G05710

25.3

9.9E-20

2

5.39

13.6

9.9E-20

2

4.56

      

UC46_5554

47264

AT2G05710

62.0

9.9E-20

2

5.40

4.9

0.0007414

3

6.52

      

UC46_5555

45840

AT2G05710

49.7

9.9E-20

4

5.40

4.8

0.0014824

4

6.52

      

UC46_9228

55395

AT2G05710

25.3

9.9E-20

2

5.39

9.4

4.115E-06

3

5.07

      

Spectral Counting

Stage II vs. early Stage II

Stage III vs. Stage II

HarvEST Citrus ID

iCitrus ID

Blast Hit to TAIR ID

Bayes

Factor

Fold

Change

Direction

No. peptides

-Log(e)

FDR Down

FDR

up

Bayes Factor

Fold

Change

Direction

No. peptides

-Log(e)

FDR Down

FDR up

UC46_7402

39802

AT2G05710

0.66

1.88

0

4

9.3

1.26

0.91

30.46

5.52

1

3

4.6

0.80

0.00

UC46_5405

43680

AT2G05710

-

-

-

-

-

-

-

143.42

12.24

1

2

3.7

1.03

0.88

UC46_5555

45840

AT2G05710

0.4

1.08

0

3

14.9

1.20

0.91

0.58

1.29

0

4

13.0

0.89

0.75

UC46_9228

55395

AT2G05710

0.42

1.08

0

2

11.7

1.26

0.91

0.68

1.11

0

3

11.0

0.73

0.86

 

HarvEST

 

UC46_7402

UC46_5405

UC46_5555

UC46_5554

UC46_9228

         

Origin

Citrus ID

iCitrus ID

39802

43680

45840

47264

55395

         

C. Sinensis

UC46_7402

39802

 

87

17

18

86

         

C. Sinensis

UC46_5405

43680

  

87

86

86

         

C. Sinensis

UC46_5555

45840

   

99

90

         

P. Trifoliata

UC46_5554

47264

    

89

         

C. Sinensis

UC46_9228

55395

              

All iCitrus accessions for aconitase that were identified by both methods were homolog to the Arabidopsis gene At2g05710. Identification of aconitase by dMS and SC. The column "direction" under SC represents up-regulated = 1, no change = 0, down-regulated = -1. Aconitase iCitrus accessions amino acids sequences similarities.

Most of the proteins identified by both dMS and SC also showed similar expression patterns (Figure 5). Out of 452 proteins identified by both methods in the comparison between fruits at Stage II versus fruits at early Stage II, 308 proteins (69%) had the same expression pattern therefore referred as "matching" (Figure 5a). In the comparison between fruits at Stage III versus Stage II 51% of the shared proteins displayed similar expression pattern and the rest fell under the "weak matching" category (Figure 5a). "Weak matching" refers to proteins showing significant expression changes with one method while showed no significant expression differences when analyzed with the other method (Figure 5b-d). Only few proteins, 1 and 16, showed contradicting expression patterns in the comparisons between Stage II versus early Stage II and between Stage III and Stage II, respectively. The high percentage of proteins shared by dMS and SC that show the same expression pattern serves also as a strong validation for protein expression.
https://static-content.springer.com/image/art%3A10.1186%2F1477-5956-8-68/MediaObjects/12953_2010_Article_220_Fig5_HTML.jpg
Figure 5

Comparisons of expression trends of proteins identified by dMS and SC. (a). Up-regulated (1-red), unchanged (0-black), down-regulated (-1-green). (b-c) Proteins showing the same trend in dMS and SC (strong-match), contradicting trends (no-match) and "weak-match" for proteins identified by one of the methods as not changing. D-down-regulated, N-no change, U-up-regulated. Strong expression "match", (U/U, D/D, N/N according to dMS/SC) (white columns); "weak-match", (N/U, U/N, D/N, N/D by dMS/SC) (grey columns); "no-match", (D/U, U/D) (black columns); (b) Stage II vs. early Stage II (c) Stage III vs. Stage II. (d) Proteins identified by dMS and SC that have the same expression trend ("match", white), contradicting expression trend ("no-match", black) and proteins up-regulated or down-regulated in one method but unchanged in the other ("weak-match", grey).

Changes in protein expression during fruit development

Label-free LC-MS/MS analysis of juice sac cells indicated significant changes in protein synthesis during fruit development (Table 2). Changes in the expression of 1834 and 1004 iCitrus accessions during fruit development were identified by dMS and SC, respectively. These numbers consisted of accessions identified by the four types of comparisons conducted (Stage II vs. early Stage II, Stage III vs. Stage II, membrane-bound proteins and soluble), and proteins appearing at more than one stage of development were only counted once. In most cases, the discrepancies between the two methods were due to differences on the bioinformatics associated with dMS and SC workflows (see Discussion).
Table 2

Functional classification of proteins identified by dMS and SC workflows (see Experimental Procedures) after search of the iCitrus database.

 

Stage II vs. early Stage II

Stage III vs. Stage II

 

Up

No Change

Down

Total

Up

No Change

Down

Total

Functional category

dMS

SC

dMS

SC

dMS

SC

dMS

SC

dMS

SC

dMS

SC

dMS

SC

dMS

SC

Cell Cycle

0

0

1

1

3

1

4

2

0

0

0

2

0

0

0

2

Cell Wall

18

2

8

3

8

4

34

9

37

4

3

3

3

10

43

17

Energy

26

4

23

11

35

24

84

39

24

10

12

20

4

2

40

32

Heat Shock/Chaperone

66

25

40

15

29

8

135

48

80

21

55

24

0

10

135

55

Metabolism

86

34

87

44

115

38

288

116

238

53

91

111

5

27

334

191

Oxidative processes

32

11

39

11

34

11

15

33

90

20

18

24

6

5

114

49

Processing

9

15

34

16

63

14

106

45

62

26

40

35

7

16

109

77

Signaling

30

16

23

16

36

16

89

48

39

7

14

22

11

11

64

40

Structure

48

2

13

5

20

10

81

17

25

1

10

2

30

8

65

11

Trafficking

14

6

32

16

41

14

87

36

40

11

39

24

0

1

79

36

Transcription

4

2

26

12

24

12

54

26

33

4

2

13

3

6

38

23

Translation

65

10

35

28

113

32

213

70

43

5

87

39

10

28

140

72

Transport

29

6

15

16

43

24

87

46

70

12

29

24

0

1

99

37

Unknown

24

23

50

28

53

30

127

81

69

28

28

49

7

24

104

101

Sum.

451

156

426

222

617

238

1494

616

850

202

428

392

86

149

1364

743

Proteins were classified into 14 major groups and are represented according to fruit development stages comparisons according to the method used (dMS and SC).

A significant number of proteins (772 and 560) were identified and classified as "not changed" by dMS and SC, respectively (Table 2). Although these proteins were found to match differentially expressed peptides, did not pass the statistical threshold. Although not differentially expressed, the identification of these proteins provides valuable information because: (i) they are proteins that are active during fruit development; (ii) they strengthen the confidence in the identification of the same peptides in other comparisons [39]. Here, we have classified the fruit proteins into 14 major functional groups (Table 2). In general, the expression of a large number of proteins identified decreased during the transition from early Stage II to Stage II of development (617 were down-regulated and 451 were up-regulated). This trend reversed during the transition from Stage II to Stage III where 850 proteins were up-regulated and 86 were down-regulated (Table 2). Most of the up-regulated proteins belonged to Metabolism, Processing, Oxidative processes, Trafficking, Transcription and Transport.

Changes in protein associated with vesicular trafficking during fruit development

In order to illustrate similarities and disparities between dMS and SC for the quantitation of protein changes during fruit development, we analyzed changes in proteins associated with vesicular trafficking and protein movements. The global changes in protein profiles and the metabolic processes associated with the quantitative protein changes during fruit development will be presented and discussed elsewhere (Katz et al., in preparation).

In this study, many small G-proteins and other proteins associated with a large number of cellular processes such as vesicle formation; vesicular traffic and docking, etc. [4547] were found to be differentially expressed during fruit development (Tables 2, 3).
Table 3

Vesicular trafficking-related proteins identified by dMS and SC.

    

Stage II (55 mm) vs. early stage II (35 mm)

Stage III (80 mm) vs. Stage II (55 mm)

    

dMS

SC

dMS

SC

Gene Family

Annotation

iCitrus ID

Blast Hit TAIR ID

Peptides

Ratio

Bayes

Factor

Fold Change

Direction*

Peptides

Ratio

Bayes

Factor

Fold Change

Direction*

Rab

RABA1a/ARA2

5282

AT1G06400

3

0.03

--

--

--

2

2.85

--

--

--

 

RABA1d/Rab11B

50939

AT4G18800

7

0.025

1.34

1.52

0

--

--

0.95

1.53

0

 

RABA1f

23943

AT5G60860

5

0.11

465

31.9

-1

--

--

0.8

1.2

0

 

RABA2a/Rab11C

33548

AT1G09630

6

0.72

63362

23

-1

2

7.43

31.8

4.5

1

 

RABA2b

27900

AT1G07410

3

0.026

3302

28

-1

--

--

--

--

--

 

RABB1b/Rab2C

28361

AT4G35860

--

--

--

--

--

3

33.41

--

--

--

 

RABB1c

57271

AT4G17170

5

0.11

72

3.2

-1

4

28.6

1555

5.02

1

 

RABD1/FP8

44137

AT3G11730

3

0.33

395

17.8

-1

--

 

0.9

1.45

0

 

RABD2a/Rab1b

22194

AT1G02130

3

0.07

1472

24

-1

2

31.24

62.5

5.00

1

 

RABD2b/Rab1A

21238

AT5G47200

2

0.04

--

--

--

--

--

--

--

--

 

RABE1a/Rab8

55887

AT3G53610

3

0.03

54.9

3.6

-1

--

--

1.04

1.5

0

 

RABE1e/Rab8E

58806

AT3G09900

2

0.04

--

--

--

--

--

--

--

--

 

RABE1c/Rab8/ARA-3

21701

AT3G46060

4

1.06

8.8

1.8

0

2

3.48

0.82

1.2

0

 

RABG3d

44916

AT1G52280

2

0.01

--

--

--

--

--

--

--

--

 

RABG3f/Rab7B

30351

AT3G18820

3

0.006

0.8

1.3

0

2

6.94

2.16

1.79

0

 

RABH1b/Rab6A

53105

AT2G44610

5

0.07

478

18.3

-1

--

--

0.75

1.2

0

 

RABH1e

30604

AT5G10260

2

0.05

--

--

--

--

--

--

--

--

Arf

ARLA1c

26509

AT3G49870

3

0.79

0.58

1.13

0

2

4.49

1.07

1.67

0

 

ARFA1e

422

AT3G62290

3

0.13

--

--

--

3

26.62

--

--

--

 

ARFA1f

22081

AT1G10630

5

0.15

--

--

--

6

19.71

--

--

--

 

SAR1c

34375

AT4G02080

--

--

--

--

--

3

4.91

--

--

--

Ran

STL2P/SEC12P-Like

54385

AT2G01470

--

--

2319

5.37

1

--

--

0.67

1.04

0

 

RANBP1

42600

AT5G58590

5

0.5

3.4

2.8

0

7

129.80

0.6

1.98

0

 

RANBP1b

2905

AT2G30060

--

--

1.3

4.2

0

5

130.08

280

22.3

1

 

RAN3

57970

AT5G55190

2

0.87

0.64

1.82

0

7

20.25

3.7

1.67

0

Rho

GP3/ROP4

29311

AT1G75840

2

0.28

--

--

0

--

--

--

--

--

GDI

GDI1

34016

AT2G44100

--

--

1.58

2.18

0

3

5.00

0.73

1.13

0

 

GDI2-like

876

AT5G09550

--

--

--

--

--

2

3.11

--

--

--

VAMP/R-SNAREs

SEC22

58654

AT1G11890

2

0.20

26.9

13.6

-1

--

--

1.06

1.46

0

 

VAP27-1

54676

AT3G60600

2

0.77

--

--

--

--

--

--

--

--

 

VAMP713

23669

AT5G11150

2

1.41

--

--

--

--

--

--

--

--

Qa-SNAREs

SYP132 (syntaxin 132)

12539

AT5G08080

--

--

3

6.2

0

2

3.21

1.25

1.65

0

 

VAM3 (syntaxin 22)

37248

AT5G46860

--

--

1.35

1.86

0

--

--

4.5

2.3

0

Qb-SNAREs

VTI11

24253

AT5G39510

2

0.05

0.65

1.37

0

--

--

0.5

1.2

0

Qc-SNAREs

SYP71 (SYNTAXIN)

27696

AT3G09740

--

--

2.17

4.34

0

--

--

--

--

--

 

ALPHA-SNAP2

2375

AT3G56190

--

--

1.00

1.00

0

--

--

6.41

7.6

0

ESCRT III

SNF7.1

35782

AT4G29160

4

1.31

4.88

2.99

0

7

597.36

106

5.5

1

Dynamin

ADL6 (dynamin-like protein 6)

36589

AT1G10290

--

--

--

--

--

2

6.63

--

--

--

others

SEC61

35474

AT1G29310

3

0.10

171.70

17.65

-1

--

--

1.00

1.4

0

 

SEC14

30056

AT1G72160

5

3.42

37.17

13.65

1

16

12.92

4.8

1.5

0

 

PATL2

35474

AT1G22530

3

0.104

170.5

17.65

-1

3

6.83

1.04

1.43

0

 

SCAMP

34675

AT1G32050

3

1.70

1.50

1.55

0

2

4.11

0.74

1.18

0

 

SC3 (secretory carrier 3)

30772

AT1G61250

2

0.98

2.9

6.12

0

--

--

1.00

1.02

0

 

SYT1

45523

AT2G20990

4

8.7

0.3

1.5

0

4

2.64

1.2

1.6

0

 

COP-I coatomer, B-COPα

29871

AT2G21390

2

0.4

0.6

1.22

0

3

11.47

1.31

1.73

0

 

RTNLB3 reticulon

42672

AT1G64090

2

0.53

2.1

3.7

0

2

2.31

0.72

1.48

0

 

RTNLB5 reticulon

13580

AT2G46170

--

--

0.97

1.02

0

--

--

4.35

2.97

0

 

RTNLB6 reticulon

22293

AT3G10260

--

--

1.24

1.4

0

2

36.56

187.81

5.76

1

 

clathrin heavy chain

57153

AT3G08530

--

--

83.92

13.36

-1

--

--

0.76

1.17

0

 

clathrin heavy chain

60690

AT3G08530

2

0.05

63.7

13.65

-1

--

--

46.45

10.98

-1

 

Clathrin light chain

51128

AT2G20760

2

119

2300591

11.55

1

2

1.8

0.8

1.2

0

 

Clathrin light chain

21278

AT3G51890

2

118

1511

6.2

1

--

--

1.63

1.57

0

 

myosin heavy chain

38388

AT4G31340

--

--

0.6

1.00

0

--

--

0.75

1.3

0

 

myosin heavy chain

37311

AT1G06530

3

0.02

156

16

-1

--

--

1.1

1.4

0

* The column "direction" under spectral counting measurement represent expression direction, 1 = up-regulated, 0 = no change, -1 = down-regulated.

Proteins identified by dMS were considered to be upregulated when expression fold > 2, not changed when 0.5 < fold < 2 and down-regulated when fold change was < 2. For SC Bayes factor of > 10 was used for significance difference.

Several small G-proteins belonging to the sub-families RAB, ARF, RHO and RAN were differentially expressed during fruit development. For example, proteins belonging to the RAB-like sub-family (nomenclature according to Vernoud et al., [48]); RABA1a, RABA1 d, RABA1f, RABA2a, RABA2b, RABB1b, RABB1c, RABD1, RABD2a, RABD2b, RABE1a, RABE1c, RABE1e, RABG3 d, RABG3f, RABH1b and RABH1e were found to be differentially expressed (Table 3). During the transition between early Stage II to Stage II most of this group of proteins was down-regulated according to dMS, except for RABA2a and RABE1c. Similarly to dMS, SC showed that RABA1f, RABA2a, RABA2b, RABB1c, RABD1, RABD2a, RABE1a and RABH1b, were down-regulated while no changes were detected in RABA1 d, RABE1c and RABG3f. During the transition from Stage II to Stage III, RABA1a, RABA2a, RABB1b, RABB1c, RABD2a, RABE1c and RABG3f were shown to be up-regulated by dMS (Table 3). SC detected up-regulation only for RABA2a, RABB1c and RABD2a at these stages. Few members of the ADP-ribosylation factor (ARF) were also found to be differentially expressed during fruit development. ARFA1e and ARFA1f were down-regulated during the transition from early Stage II to Stage II. On the other hand, ARLA1c remained unchanged (Table 3). ARFA1e, ARFA1f, ARLA1c and SAR1 provide another example of the difference in accuracy between dMS and SC. While dMS indicated that these four proteins were up-regulated during the later stages of fruit development (Table 3), SC indicated no change. Four members of the RAN family, SEC12p, RANBP1, RANBP1b and RAN3 were identified in this study. The expression of RAN3 remained unchanged during the early stages of fruit development (as shown by both dMS and SC) but was up-regulated during the later stages. Both dMS and SC indicated that the expression of RANBP1b was up-regulated during the later stages of fruit development while only dMS showed up-regulation of RANBP1. SEC12p was up-regulated during the early stages and remained unchanged during the later stages of fruit development.

Among the members of the RHO family, ROP4 was down-regulated at earlier stages of development (Table 4). Interestingly, dMS showed that two RAB GDI (GDP-RAB dissociation inhibitors), GDI1 and GDI2-like were up-regulated during the later stages of fruit development while only GDI1was identified by SC. Three R-SNAREs were identified; SEC22 that was down-regulated during the transition from early Stage II to Stage II, VAMP27-1 and VAMP713 were identified but were not found to be differentially expressed. Five Q-SNAREs were identified but only VTI11 (Qb-SNARE) was found to be down-regulated during early stages of development while SYP132 (Qa-SNARE, syntaxin) was found to be up-regulated during late stages of development. SNF7, a component of the endosomal ESCRT III complex that functions in cargo recognition and sorting [49], was up-regulated during the late stages of development. Additional proteins related to vesicular trafficking such as dynamin, COP-I coatomer, reticulon 3 and 6, and proteins related to secretory membrane carriers such as SEC14, PATL2 and SYT1 were up-regulated during the late stages of fruit development, while SEC 14, SYT1, and light chain of clathrin were up-regulated during the transition from early Stage II to Stage II. Heavy chain of clathrin was down-regulated throughout development (Table 3).
Table 4

Structure-related proteins identified by dMS and SC.

   

Stage II (55 mm) vs. early stage II (35 mm)

Stage III (80 mm) vs. Stage II (55 mm)

   

dMS

SC

dMS

SC

Annotation

iCitrus ID

Blast Hit TAIR ID

Peptides

Ratio

Bayes Factor

Fold Change

Direction*

Peptides

Ratio

Bayes Factor

Fold Change

Direction*

TUB1 (tubulin beta-1)

33157

AT1G75780

4

0.024

--

--

--

2

0.06

--

--

--

TUA3 (tubulin alpha-3)

46363

AT5G19770

4

0.07

--

--

--

--

--

--

--

--

TUA4 (tubulin alpha-4)

22742

AT1G04820

10

0.025

24.68

2.83

-1

  

1473619

13.49

-1

TUA4 (tubulin alpha-4)

28975

AT1G04820

10

0.025

--

--

--

4

0.16

--

--

--

TUA4 (tubulin alpha-4)

37760

AT1G04820

8

0.026

884.12

10.19

-1

3

0.7

0.6

1.4

0

TUB5 (tubulin beta-5)

30386

AT1G20010

9

0.02

17.65

4.6

-1

4

0.41

527

9.15

-1

TUB5 (tubulin beta-5)

32879

AT1G20010

9

0.04

3125

46.15

-1

--

--

--

--

--

TUB5 (tubulin beta-5)

50323

AT1G20010

10

0.02

27.89

14.3

-1

3

0.4

16293

33.34

-1

TUB5 (tubulin beta-5)

51971

AT1G20010

10

0.025

537

26.95

-1

4

0.39

12048

32.23

-1

TUB5 (tubulin beta-5)

53023

AT1G20010

9

0.033

3784

49.2

-1

--

--

--

--

--

TUA6 (tubulin alpha-6)

2232

AT4G14960

7

0.044

--

--

--

--

--

--

--

--

TUA6 (tubulin alpha-6)

48045

AT4G14960

4

0.025

--

--

--

2

21.7

--

--

--

TUB6 (tubulin beta-6)

54455

AT5G12250

6

0.027

--

--

--

 

2.18

--

--

--

TUB7 (tubulin beta-7)

54189

AT2G29550

2

0.6

--

--

--

2

2.19

--

--

--

TUB8 (tubulin beta-8)

55174

AT5G23860

5

0.022

--

--

--

3

45.85

--

--

--

ACT1 (actin 1)

45172

AT2G37620

3

0.3

--

--

--

3

22.49

--

--

--

ACT7 (actin 7)

3401

AT5G09810

6

0.025

42.61

2.24

-1

--

--

--

--

--

ACT7 (actin 7)

31765

AT5G09810

6

0.22

--

--

--

5

17.50

--

--

--

ACT8 (actin 8)

57479

AT1G49240

13

0.04

20.5

2.12

-1

8

21.93

20.36

2.12

1

ACT11 (actin 11)

51444

AT3G12110

4

0.26

--

--

--

3

27.16

--

--

--

CAM5 (Calmodulin 5)

18478

AT2G27030

--

--

7135

1.85

-1

--

--

32767

2.69

-1

CAM5 (Calmodulin 5)

51416

AT2G27030

7

0.15

--

--

--

--

--

--

--

--

KIS (KIESEL)

26013

AT2G30410

--

--

51.78

4.89

-1

--

--

48.8

8.36

-1

VLN3 (VILLIN 3)

11096

AT3G57410

--

--

1.19

3.00

0

--

--

1.55

1.74

0

microtubule associated protein (MAP65/ASE1)

38803

AT4G26760

4

0.03

95.54

28.3

-1

--

--

135.6

13.6

-1

PFN1/PRF1 (PROFILIN 1)

4145

AT2G19760

2

1.36

--

--

--

2

15.81

0.80

1.14

0

PFN3/PRF3 (PROFILIN 3)

23065

AT5G56600

3

1.47

0.85

1.4

0

2

15.81

1.4

3.8

0

PRF5 (PROFILIN5)

5851

AT2G19770

--

--

0.9

1.34

0

--

--

0.6

1.03

0

ADF4 actin depolymerizing

7407

AT5G59890

--

--

6.8

5.2

0

--

--

28.89

10.4

-1

* The column "direction" under spectral counting measurement represent expression direction, 1 = up-regulated, 0 = no change, -1 = down-regulated.

Proteins identified by dMS were considered to be up-regulated when expression fold > 2, not changed when 0.5 < fold < 2 and down-regulated when fold change was < 2. For spectral counting Bayes factor of > 10 was used for significance difference.

Differential protein expression was also found in other important groups of proteins, actins and tubulins, key factors in trafficking, cell division and enlargement [50]. TUB1, TUA3, TUA4, TUB5, TUA6, TUB6 and TUB8 were down-regulated in the transition from early Stage II to Stage II (Table 4). TUB1, TUA4, TUB5, TUA6 and TUB6 were down-regulated further during the transition from Stage II to Stage III while TUB7 and TUB8 were up-regulated during this transition. Actins, driving vesicular movement towards their destination, showed significant changes during fruit development (Table 4). ACT1, ACT7, ACT8 and ACT11 were down-regulated during the transition from early stage II to stage II and were up regulated during the transition from stage II to stage III (Table 4).

Down-regulation of other proteins related to the vesicle movements such as CaM5 (which binds to the motor protein kinesin [51, 52] and myosin were detected (Table 4). Profilins, PFN1, PFN3 and PFN5, involved in actin polymerization and cytoskeleton organization did not change during the transition from early Stage II to Stage II, but PFN1 and PFN3 were up-regulated during the transition from Stage II to Stage III. Another protein, ADF4, involved in actin de-polymerization was down regulated during the transition to Stage III. Microtubule Associated Protein 65 (MAP65) and KIS (Tubulin cofactor A) involved in tubulin complex assembly and cell division [53, 54], were down-regulated throughout fruit development (Table 4).

Transporters play a crucial role in cell growth and homeostasis, especially in specialized solute accumulating cells such as citrus juice cells. As expected, many changes in transporters protein expression were noted during fruit development (Table 5). During the transition from early Stage II to Stage III, there was a significant down-regulation of subunits of lysosomal ATPases and cation transporters associated with K+- and Na+-coupled transport. On the other hand, only one plasma membrane-bound ATPase displayed down-regulation (similar to AHA8), while those similar to AHA2, AHA4 and AHA10 were not significantly changed. In general, these changes were noted using both dMS and SC. Most of the proteins that were down-regulated during the transition from early to Stage II, were up-regulated during the transition from Stage II to Stage III (Table 5), suggesting their role during fruit expansion. Similar results were seen with mitochondrial-bound proteins such as ACP4, ADP/ATP carriers and others. Two tonoplast monosaccharide transporters, TMT1and TMT2 were up-regulated during the transition from early to stage II and TMT2 was further up-regulated during the later stages of fruit development. A dicarboxylate/tricarboxylate carrier was up-regulated throughout development. Plasma membrane water channels PIP1B/PIP1;2, TMP-C/PIP1;4, PIP2;8/PIP3B and PIP2;5/PIP2 D were down-regulated during the transition from early to Stage II according to SC (Table 5).
Table 5

Transport-related proteins identified by dMS and SC.

   

Stage II (55 mm) vs. early stage II (35 mm)

Stage III (80 mm) vs. Stage II (55 mm)

   

dMS

SC

dMS

SC

Annotation

iCitrus ID

Blast Hit TAIR ID

Peptides

Ratio

Bayes Factor

Fold Change

Direction*

Peptides

Ratio

Bayes Factor

Fold Change

Direction*

Ca2+ -ATPase

45644

AT1G07670

--

--

1.2

2.9

0

2

20.88

27.99

4.99

1

Ca2+/Na+ exchanger

47787

AT1G53210

2

0.27

14.27

3.00

-1

2

9.04

0.71

1.15

0

KEA2 (K+/H+ antiporter)

36862

AT4G00630

2

0.40

1.41

1.75

0

--

--

0.8

1.2

0

porin, putative; voltage-dependent anion channel

23759

AT3G01280

2

0.19

13.9

6.34

-1

2

6.09

15.91

4.81

1

DET3 C subunit C (V-type H+- ATPase)

58235

AT1G12840

3

0.27

45.53

3.26

-1

--

--

1.2

1.48

0

AVP1 (vacuolar H+-PPiase)

36100

AT1G15690

4

2.31

1.1

1.2

0

  

2.76

1.63

0

VHA-A

27256

AT1G78900

13

0.04

1011648

3.99

-1

12

9.94

38.82

2.2

1

VHA-A3 (V-ATPase)

49974

AT4G39080

4

0.44

--

--

--

3

4.01

--

--

--

V-type ATPase subunit B1

3683

AT1G76030

8

0.05

--

--

--

5

13.78

--

--

--

V-type ATPase subunit B2

62399

AT4G38510

11

0.06

--

--

--

9

11.25

--

--

--

V-type ATPase subunit B3

37671

AT1G20260

11

0.06

140.60

2.13

-1

9

11.25

1004

2.64

1

V-type ATPase subunit D

23422

AT3G58730

--

--

34.4

5.4

-1

--

--

1.55

1.45

0

V-type ATPase subunit E1

52391

AT4G11150

--

--

19.83

3.22

-1

2

5.12

7.23

1.94

0

VMA10 (V-type ATPase 10); Subunit G

28722

AT3G01390

3

0.28

36.6

2.8

-1

--

--

0.15

1.78

0

V-type ATPase subunit H

38700

AT3G42050

6

0.25

36.6

2.82

-1

4

13.57

0.58

1.06

0

AHA2 (H+-ATPase 2)

50370

AT4G30190

3

0.53

0.98

1.23

0

4

11.79

1.75

1.49

0

AHA4 (H+- ATPase 4)

28397

AT3G47950

3

1.14

11055

20.4

-1

3

7.40

0.9

1.5

0

AHA8 (H+- ATPase 8)

56228

AT3G42640

2

0.35

--

--

--

2

13.60

--

--

--

AHA10 (Autoinhibited H+- ATPase isoform 10)

56764

AT1G17260

4

1.13

5.8

1.55

0

4

41.48

1.36

1.19

0

H+- ATPase

42025

AT3G28710

2

0.11

335

18.72

-1

2

17.27

22.04

4.37

1

TMT1 (tonoplast monosaccharide transporter1)

44853

AT1G20840

3

24.79

3.0

1.1

0

--

--

382

5.1

1

TMT2, (tonoplast monosaccharide transporter 2

41259

AT4G35300

8

32.69

16121

2.64

1

6

15.26

9.5

1.9

0

GPT2 (glucose-6-phosphate/phosphate translocator 2)

44184

AT1G61800

2

0.10

28.2

12.3

-1

--

--

0.8

1.2

0

mannitol transporter

32978

AT2G16130

--

--

439

18.8

-1

--

--

5.8

3.2

0

LPT(lipid transfer protein)

45077

AT1G27950

2

0.01

5258.00

22.44

-1

--

--

--

--

--

LTP3

58907

AT5G59320

--

--

2.86

4

0

--

--

7447

21.8

-1

PDR6/PDR6 (pleiotropic drug resistance 6); ATPase

26072

AT2G36380

--

--

4.5

7.11

0

--

--

49.8

3.9

1

PDR11/PDR11 (pleiotropic drug resistance 11); ATPase

52044

AT1G66950

--

--

--

--

--

2

11.74

1.18

1.56

0

PDR12/(PLEIOTROPIC DRUG 12); ATPase

19314

AT1G15520

--

--

0.98

1.03

0

2

14.97

5.44

3.46

0

MAPR2 (membrane-associated progesterone binding protein 2)

28962

AT2G24940

3

1.62

1.57

1.65

0

3

45.68

1.38

1.69

0

MRP4 (multidrug resistance-associated protein 4)

46730

AT2G47800

2

1.36

2.62

2.64

0

5

58.12

19.3

2.6

1

PGP7 (P-GLYCOPROTEIN 7); ATPase

10552

AT5G46540

--

--

2.06

5.56

0

--

--

10.4

3.6

1

PIP1B (plasma membrane intrinsic protein 1;2); water transport

18285

AT2G45960

--

--

149.18

27.06

-1

--

--

--

--

--

PIP2;8/PIP3B (plasma membrane intrinsic protein 2;8); water channel

25444

AT2G16850

--

--

462

23

-1

--

--

0.9

1

0

PIP2;5/PIP2 D (plasma membrane intrinsic protein 2;5); water channel

41682

AT3G54820

--

--

635.26

39.02

-1

--

--

--

--

--

TMP-C (plasma membrane intrinsic protein 1;4); water channel

28678

AT4G00430

3

0.11

35.06

10.97

-1

--

--

2.76

2.70

0

protein transporter

51128

AT2G20760

3

30

0.2

1.6

0

2

1.80

0.84

1.22

0

protein transporter

21278

AT3G51890

2

118.94

1510

6.2

1

--

--

1.63

1.57

0

TOM20-3 (translocase of outer membrane)

33905

AT3G27080

4

0.45

0.6

1.16

0

3

4.75

18.02

2.97

1

ACP4 (acyl carrier 4)

25090

AT4G25050

3

0.34

3.1

2.27

0

--

--

13.4

7.8

1

AAC2 (ADP/ATP carrier)

53189

AT5G13490

8

0.27

16767

10.36

-1

5

21.36

1.01

1.02

0

AAC3 (ADP/ATP carrier)

26451

AT4G28390

10

0.22

1040

3.48

-1

6

76.16

32.95

2.75

1

mitochondrial phosphate transporter

44051

AT5G14040

4

0.10

10.8

3

-1

2

4.29

0.79

1.2

0

PDE120 protein import (Tic40)

57128

AT5G16620

2

0.34

--

--

--

--

--

--

--

--

di/tricarboxylate carrier

39221

AT5G19760

5

56.47

0.52

1.49

0

2

25.92

0.69

1.13

0

TOM22-V (translocase outer mitochondrial membrane)

38717

AT5G43970

--

--

160

17.8

-1

--

--

2.98

2.65

0

Lipocalin

23197

AT5G58070

2

29.74

44.2

4.3

1

3

10.49

40.34

2.3

1

* The column "direction" under spectral counting measurement represent expression direction, 1 = up-regulated, 0 = no change, -1 = down-regulated.

Proteins identified by dMS were considered to be up-regulated when expression fold > 2, not changed when 0.5 < fold < 2 and down-regulated when fold change was < 2. For spectral counting Bayes factor of > 10 was used for significance difference.

Discussion

In this study we describe a label-free shotgun approach to establish a proteomics workflow for the identification of the protein changes occurring during citrus fruit development. We analyzed and compared juice sac cells extracted from fruits at three stages of development. The end of Stage I (early Stage II), characterized by extensive cell division; Stage II, where cell division ceases and the juice cell sacs expand with the accumulation of large amounts of solutes and water; and Stage III, where the fruit matures and ripens [55, 56]. It should be noted that it was practically impossible to extract juice sac cell proteins at Stage I (fruit diameter ≈10-15 mm) because at this stage the juice sac cells are not well developed.

Comparative proteomics studies in plants are still lagging behind studies done in mammalian cells and are predominantly performed by employing 2DE-gels [57]. Although differential proteomics studies employing label-free quantification have been published during the last few years [9, 10, 24], in plants these studies are scarce [26, 43].

In order to employ an efficient proteomics study in citrus, a plant species lacking a full sequenced genome, we established a workflow that dealt with few of the problems arising from using a ESTs database. We created iCitrus, a database and interface that collected sequences from three different sources, HarvEST:Citrus http://​harvest.​ucr.​edu/​, NCBI's Citrus unigenes and NCBI's Citrus proteins http://​www.​ncbi.​nlm.​nih.​gov/​Taxonomy/​Browser/​wwwtax.​cgi?​mode=​Info&​id=​2711&​lvl=​3&​lin=​f&​keep=​1&​srchmode=​1&​unlock to create one unified database with reduced redundancy for mass spectra search. iCitrus was created to provide a compact database for the identification of citrus proteins and a more accurate quantitative expression measurements. The iCitrus interface enabled a fast identification of lists of accessions including Arabidopsis homologs, and the use of bioinformatics tools such as MapMan, AraCyc and Cytoscape (Katz et al. in preparation).

The iCitrus resource is essentially an interface that can be used to access pre-calculated Blast results. iCitrus itself does not make or summarize GO assignments based on rules that weight GO terms from various hits; this is the (perfectly reasonable) philosophy behind Blast2GO and related tools. We chose to allow users, instead of iCitrus, to determine if they trust and adopt particular annotations or not. We took this approach to allow individual users to use specific knowledge of protein families or taxonomical differences (i.e. Citrus versus Arabidopsis) to influence their interpretation of the BLAST results. In addition, there may be cases in which GO annotation is absent in the BLAST results against Arabidopsis or Viridiplantae, but a consensus could emerge from the descriptive text accompanying a hit. We think this combined approach of manual annotation with the assistance of pre-computed BLAST results is more effective when predicting functional information for a not well-annotated organism like Citrus.

Two widely used, but fundamentally different, label-free methods for quantification were used in this study; peak integration (dMS) and spectral counting (SC). For dMS, we used a two-fold change as a threshold for differential expression of the identified proteins [25] and a Bayes factor of 10 for spectral counting [58]. Such a stringent threshold is needed because the protein ratios are calculated by averaging the intensity weight of peptide ratios, and because the number of peptides identifying each protein is highly variable. In most cases, both methods identified similar proteins with some discrepancies (Figure 4a). These discrepancies derived from the way SIEVE (for dMS) and Scaffold (for SC) handled the peptides information. Scaffold is able to identify peptides in similar proteins and group them together, thus identifying database redundancy, on the other hand, SIEVE does not group similar proteins. When we compare the number of identified proteins by the two methods using the corresponding Arabidopsis homologs of each iCitrus accession identified (Figure 4b) the differences decreased significantly, particularly for dMS (Figure 4). Yet, additional redundancy could arise from possible gene families in Citrus. The wide range of Citrus species used to create HarvEST:Citrus database including Citrus sinensis, Citrus paradise, Citrus unshiu, C. reticulata, C. jambhiri, C. aurantium, C. clementina, C. macrophylla and Poncirus trifoliate, consists of sequences that are similar but not identical therefore were not screened out from the iCitrus dataset. In addition, some of the sequences in the database that might originate from the same unigene did not overlap therefore could not be assembled, contributing to the difference in number of proteins identified (Table 1).

Currently, non-overlapping sequences cannot be assembled until more ESTs can be produced to cover the missing gaps or until the Citrus genome is fully sequenced [59]. A significant number of proteins (144 in dMS and 118 in SC in the Stage II vs. early Stage II comparison, and 119 in dMS and 255 proteins in SC, in the Stage III vs. Stage II comparison) were identified by only one of the methods due to the inherent differences of dMS and SC workflows. SEQUEST and SIEVE (dMS workflow) use protein probability cut-off based on false discovery rate (FDR) according to the Decoy method [60]. X!Tandem, Scaffold and Qspec (SC workflow) use peptide identification probability criteria as specified by the Peptide Prophet algorithm [61]. The different workflows affect some of the proteins identification. The performance of the SC method depends strongly on the depth of the MS/MS sampling because ratios by SC are most significant for proteins with large numbers of product ion spectra, while ratios by dMS are most significant for proteins with large numbers of overlapping peptide ions [25]. This also explains the higher percentage of proteins that were found to be significantly different by dMS and not significant by SC (Figures 3, 5a). Therefore, dMS provides more accurate measurements of compared samples while SC is faster and easier to use. Our data show that dMS is more accurate in measuring differences in protein expression [25]. dMS provide rich information of the LC-MS data but requires a massive computational effort to be spent on processing the data including background filtering, peak frame detection and alignment [62, 63]. Spectral counting is conceptually simpler and can be as sensitive as dMS in terms of detection range while retaining linearity [25, 30, 64]. Nevertheless, SC is less accurate in detecting differences in protein expression, in particular for less abundant proteins. Our results clearly show that the integrated use of both methods for quantification increases the power for detecting changes in shotgun proteomics experiments, and that both methods should be use in combination to gain insight of the complex protein network and a complete identification of its components.

Changes in a large number of small GTPases were identified during citrus fruit development. The expression of a relatively large number of members of the RAB, ARF, RHO and RAN families of small GTPases changed during the different stages. Although we cannot assign specific roles to all of these proteins, they clearly indicate a different role(s) of these members during the stages of citrus juice sac cell development. Vesicular trafficking is essential for fruit development [6567]. During the Stage I there is intensive cell division [56]. Cytoskeleton elements (actins, tubulins, etc.) together with small G-proteins and coatomer complexes are vital to cell division, cell plate formation, cell polarity, etc. [68]. The expression of many of these proteins decreased during the transition from early Stage II to Stage II. This correlated well with the attenuation of cell division in the growing fruit and the prevalence of cell expansion. This notion was reinforced by the notable increase in expression of other small GTPases, auxiliary proteins and cytoskeletal components. Similar to the small G-proteins, changes in the expression of proteins associated with vesicular movements, docking and fusion were seen. In addition to different SNAREs (Qa, Qb, Qc, syntaxins, etc.), there was changes in COPI coatomers, clathrin, dynamin, and others suggesting the occurrence of endocytosis, exocytosis and vesicular trafficking during fruit development. Notably, while the expression of plasma membrane-associated H+-ATPases did not change during the early stages of development, changes in endosomal-associated H+-ATPases (V-type) paralleled the changes seen in the secretory and vesicular trafficking machinery. V-type ATPases and organellar acidification is essential for vesicular trafficking along exocytotic and endocytotic pathways [69, 70].

Although significant changes in sugar contents and sugar homeostasis are expected during fruit development [71, 72], changes in expression of only two putative vacuolar monosaccharide transporters (TMT1 and TMT2) were noted. A plausible explanation is that the expression of other sugar transporters did not change (although they could have been modified by post-translational mechanisms). In support of this notion, Etxeberria et al. [73, 74] demonstrated a mechanism of sugar transport into the juice sac cells and sucrose into the vacuoles that is mediated by endocytosis and intracellular vesicular trafficking. The protein inventory developed in this work, provides a preliminary glance at the function(s) of these proteins during the different stages of fruit development and in particular during cell division (Stage I, early Stage II) and cell expansion (Stage II) and assimilate mobilization, sugar accumulation and processes regulating fruit maturation and ripening.

In conclusion, we developed a workflow for the analysis and identification of proteins during fruit development in citrus, a non-model plant, using comparative label-free shotgun proteomics. We established iCitrus, a comprehensive sequence database by merging three major sources of sequences and improving the annotation of existing unigenes. iCitrus provided a useful bioinformatics tool for the high throughput identification of citrus proteins. Two methods for label-free based shotgun proteomics were used and compared; peak integration (or differential mass-spec) and spectral counting. We have identified approximately 1500 citrus protein accessions expressed in fruits and quantified their expression changes during fruit development. Our results showed that both methods can provide significant information on protein changes, with dMS providing higher accuracy. Our results clearly suggest that dMS and SC are matching, broadening the identification spectrum and providing complementary data on the change trends during the particular processes being compared.

Methods

Plant material, protein extraction and precipitation

Orange Navel fruits at three different developmental stages, early stage II (35 mm in fruit diameter), stage II (55 mm) and stage III (80 mm) [55] were obtained from the Lindcove Research Center, University of California, Exeter, CA. Juice sacs were collected from at least 20 fruits and pooled at each stage. Two independent biological repetitions from two consecutive years were used and proteins were isolated as described before [18]. Soluble proteins were precipitated using a chloroform/methanol extraction method as described by Wessel and Flugge [75]. The samples were resuspended with 100 μl of 1% Acetonitrile and sonicated for 10 min and centrifuged at 10,000 g for 3 min. The supernatant was spin-dialyzed into 50 mM ammonium-bicarbonate (AMBIC), then prepared for MS analysis using standard reduction, alkylation, and tryptic digest procedures [76]. Dichloromethane was added (50/50 v/v with aqueous digest) before vortexing for 1 min. Samples were centrifuged for 5 min at 10,000 g in a microcentrifuge and the upper layer-containing peptides dried down and the peptides resolubilized in 2% acetonitrile/0.1% trifluoroacetic acid for LC-MS/MS analysis.

Membrane-bound proteins were spin-dialyzed into 50 mM AMBIC. An endo-polygalacturonanase (Megazyme) was employed to degrade pectins overnight at room temperature and the suspensions centrifuged and the pellets retained. Membranes were resolubilized in 50 mM AMBIC and digested with trypsin. The suspension was centrifuged 10 min at 10,000 g and the supernatant containing tryptic peptides retained. Delipidation was performed with dichloromethane and the peptides resolubilized in 2% acetonitrile/0.1% trifluoroacetic acid for LC-MS/MS analysis.

Mass Spectrometry and Data Analysis

Digested peptides were separated by reversed-phase chromatography using a Waters nanoACQUITY-UPLC system (Milford, MA), with a Waters BEH C18 1.7 μm, 100 μm × 10 cm column. A binary solvent gradient was employed; buffer A was composed of 0.1% formic acid and buffer B composed of 100% acetonitrile (CAN). The 120 min gradient consisted of the steps 2-45% buffer B in 40 min, 45-80% buffer B in 65 min, hold for 1 min, 80-2% buffer B in 4 min, then hold for 10 min. Separated peptides were analyzed in a Thermo-Scientific LTQ-FT Ultram mass-spectrometer (San Jose, CA) with a Michrom captive spray nano-electrospray ionization source at a flow rate of 2 μl/min. MS and MS/MS spectra were acquired using a top 4 method, where the 4 most abundant ions in the MS scan were selected for automated low energy Collision-induced Dissociation (CID) with a 30 s exclusion time and repeat count of 2. The FTMS scan was obtained for the m/z range 300-1400 Da at 50,000 resolution. An isolation width of 2.5 Da was used for ITMS, and a normalized collision energy of 35% was used for the fragmentation. Five technical repeats of each pooled sample (older vs younger fruit) were each analyzed by SIEVE using blanks (washes) between each sample run.

Protein Identification and Validation, dMS workflow

Tandem mass spectra were extracted with Xcalibur version 2.0.7. All MS/MS samples were analyzed using SEQUEST (Protein Discoverer 1.1; Thermo-Scientific, San Jose, CA). SEQUEST was set up to search a FASTA file of the iCitrus Protein Database (see below), assuming the digestion enzyme trypsin. SEQUEST was searched with a peptide ion mass tolerance of 25 ppm and a fragment ion mass tolerance of 1.0 Da. Oxidation of methionine and iodoacetamide derivative of cysteine was specified in SEQUEST as possible modifications. DTASelect software was used to filter out low score matching. The filtering criteria consisted of Cross-correlation (xcorr) values larger than 1.5 for single-charged ions, 2.2 for double-charged ions, and 3.3 for triple-charged ions, for both half or fully tryptic peptides. This resulted in a false discovery rate of less than 5% using a decoy search strategy.

Differential Expression mass spectrometry, dMS workflow

Samples were analyzed using a Thermo Scientific LTQ-FT mass-spectrometer and a Michrom-Paradigm HPLC. Peptides were separated using a 200 μm × 15 cm Michrom Magic C18 reverse-phase column over 45 min using an acetonitrile gradient of 2%-60%. The mass-spectrometer was set to acquire spectra in standard top 3 method where 1 high resolution scan (100 K resolution) was acquired every sec with subsequent MS/MS spectra acquired in the LTQ simultaneously.

Samples were analyzed using SIEVE (Thermo Scientific, San Jose Ca). SIEVE is a label-free-differential expression package that aligns the MS spectra over time from different experimental conditions and then determines features in the data (m/z and retention time pairs) that differ across the different conditions. These differences were assigned using various statistics methods such as a P-Value and standard deviation and then sorted based on significance [10], based on the values obtained from the data of each biological replicate. Label free proteomic profiling was accomplished using SIEVE 1.3 (Thermo Scientific, San Jose, CA). The following parameters were set to align the retion time and generate the frames needed for abundance calculations. Alignment Parameters; Alignment Bypass = False, Correlation Bin Width = 1, RT Limits For Alignment = True, Tile Increment = 150, Tile Maximum = 300, Tile Size = 300, Time Threshold = 0.6. Frame Parameters; AVGCharge Processor = False, MS2 Corr Processor = False, M/Z Min = 300, M/Z Max = 1,400, Frame time Width (min) = 5.0 minutes, Frame M/Z width = 0.02 da, Search Window % = 50%, Retention Time Start = 5.0 min, Retention Time Stop = 110 min, Peak Intensity threshold = 50,000, Processor Modules = Isotagger V1.1, PCA V1.0. Significance was calculated within SIEVE using a standard T-test and results were filtered for a minimum of two peptides identified per protein (using the identification criteria stated in this method section) with frames having a p value of less than 0.05.

Tandem mass-spectra from peptide features that are considered differentially expressed across conditions are then searched using SEQUEST against iCitrus (see below). Search results were filtered for a False Discovery rate of 5% employing a decoy search strategy utilizing a reverse database [60].

Protein Identification and Validation for Spectral counting

Tandem mass-spectra were extracted by Bioworks-3.3. Charge state de-convolution and de-isotoping were not performed. All MS/MS samples were analyzed using X! Tandem http://​www.​thegpm.​org; version TORNADO (2008.02.01.2)). X! Tandem was set up to search the 62,415 entries of iCitrus (see below) assuming the digestion enzyme trypsin. X! Tandem was searched with a fragment ion mass tolerance of 0.40 Da and a parent ion tolerance of 25 ppm. Iodoacetamide derivative of cysteine was specified in X! Tandem as a fixed modification. Deamidation of asparagine, oxidation of methionine, sulphone of methionine, tryptophan oxidation to formylkynurenin of tryptophan and acetylation of the N-terminus were specified in X! Tandem as variable modifications. Different tandem MS programs were used (SEQUEST for dMS and X!Tandem for Spectral Counting) because of licensing restriction and limited access to SEQUEST that would have generated significant time delays in the data analysis. Nonetheless, the use of SEQUEST or X!Tandem would have make little or no difference. In addition, in this report we aim at comparing overall methodology (i.e. dMS versus SC) and not their individual components.

Criteria for protein identification for Spectral Counting

Scaffold 2.06.00 (Proteome Software Inc., Portland, OR) was used to validate MS/MS-based peptide and protein identifications. Peptide identifications were accepted if they could be established at greater than 80.0% probability as specified by the Peptide Prophet algorithm [61]. Protein identifications were accepted if they could be established at greater than 95.0% probability and contained at least 2 identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm [77]. Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony.

Statistical Analysis for Spectral Counting

Unweighted Spectral counts for the identified proteins obtained from the samples corresponding to two consecutive growth seasons were exported from Scaffold and analyzed using QSpec [58] for significance analysis. Proteins were considered significantly different across sample conditions if QSpec reported a Bayes factor of > 10. This corresponds to a false discovery rate (FDR) of approximately 5%.

Proteomics Data Set

The data associated with this manuscript may be downloaded from ProteomeCommons.org Tranche using the following hash:

Cf3G8KatEeCbDv2kV1Gnw4njaSYARJgmtyzYl+5764Gsbb/M3LX+/oo1zcHnHK1Gs0ukuBM5Rk+Q1t5hpia109pVPXkAAAAAAAAoLg==The hash may be used to prove exactly what files were published as part of this manuscript's data set, and the hash may also be used to check that the data has not changed since publication.

Declarations

Acknowledgements

This work was supported by a research grant No. US-4010-07C from BARD, the United States-Israel Binational Agricultural Research and Development Fund, grant #5200-117 from the California Citrus Research Board, and by the Will W. Lester Endowment, University of California.

Authors’ Affiliations

(1)
Department of Plant Sciences, University of California
(2)
Genome Center, Proteomics Core Facility, University of California
(3)
Genome Center, Bioinformatics Core Facility, University of California
(4)
Department of Fruit Tree Species, ARO, The Volcani Center

References

  1. Sarry JE, Sommerer N, Sauvage FX, Bergoin A, Rossignol M, Albagnac G, Romieu C: Grape berry biochemistry revisited upon proteomic analysis of the mesocarp. Proteomics 2004, 4: 201–215. 10.1002/pmic.200300499PubMedView Article
  2. Carrari F, Baxter C, Usadel B, Urbanczyk-Wochniak E, Zanor M-I, Nunes-Nesi A, Nikiforova V, Centero D, Ratzka A, Pauly M, et al.: Integrated analysis of metabolite and transcript levels reveals the metabolic shifts that underlie tomato fruit development and highlight regulatory aspects of metabolic network behavior. Plant Physiology 2006, 142: 1380–1396. 10.1104/pp.106.088534PubMed CentralPubMedView Article
  3. Rocco M, D'Ambrosio C, Arena S, Faurobert M, Scaloni A, Marra M: Proteomic analysis of tomato fruits from two ecotypes during ripening. Proteomics 2006, 6: 3781–3791. 10.1002/pmic.200600128PubMedView Article
  4. Deluc L, Grimplet J, Wheatley M, Tillett R, Quilici D, Osborne C, Schooley D, Schlauch K, Cushman J, Cramer G: Transcriptomic and metabolite analyses of Cabernet Sauvignon grape berry development. BMC Genomics 2007, 8: 429. 10.1186/1471-2164-8-429PubMed CentralPubMedView Article
  5. Faurobert M, Mihr C, Bertin N, Pawlowski T, Negroni L, Sommerer N, Causse M: Major proteome variations associated with cherry tomato pericarp development and ripening. Plant Physiology 2007, 143: 1327–1346. 10.1104/pp.106.092817PubMed CentralPubMedView Article
  6. Mounet F, Lemaire-Chamley M, Maucourt M, Cabasson C, Giraudel JL, Deborde C, Lessire R, Gallusci P, Bertrand A, Gaudillere M, et al.: Quantitative metabolic profiles of tomato flesh and seeds during fruit development: complementary analysis with ANN and PCA. Metabolomics 2007, 3: 273–288. 10.1007/s11306-007-0059-1View Article
  7. Fait A, Hanhineva K, Beleggia R, Dai N, Rogachev I, Nikiforova VJ, Fernie AR, Aharoni A: Reconfiguration of the achene and receptacle metabolic networks during strawberry fruit development. Plant Physiology 2008, 148: 730–750. 10.1104/pp.108.120691PubMed CentralPubMedView Article
  8. Zanor MI, Rambla J-L, Chaib J, Steppa A, Medina A, Granell A, Fernie AR, Causse M: Metabolic characterization of loci affecting sensory attributes in tomato allows an assessment of the influence of the levels of primary metabolites and volatile organic contents. Journal of Experimental Botany 2009, 60: 2139–2154. 10.1093/jxb/erp086PubMed CentralPubMedView Article
  9. Bantscheff M, Schirle M, Sweetman G, Rick J, Kuster B: Quantitative mass spectrometry in proteomics: a critical review. Analytical Biochemistry 2007, 389: 1017–1031.
  10. America AHP, Cordewener JHG: Comparative LC-MS: A landscape of peaks and valleys. Proteomics 2008, 8: 731–749. 10.1002/pmic.200700694PubMedView Article
  11. Schulze WX, Usadel B: Quantitation in mass-spectrometry-based proteomics. Annual Review of Plant Biology 2010, 61: 491–516. 10.1146/annurev-arplant-042809-112132PubMedView Article
  12. Panchaud A, Affolter M, Moreillon P, Kussmann M: Experimental and computational approaches to quantitative proteomics: Status quo and outlook. Journal of Proteomics 2008, 71: 19–33. 10.1016/j.jprot.2007.12.001PubMedView Article
  13. Ünlü M, Morgan ME, Minden JS: Difference gel electrophoresis. A single gel method for detecting changes in protein extracts. Electrophoresis 1997, 18: 2071–2077.PubMedView Article
  14. Tonge R, Shaw J, Middleton B, Rowlinson R, Rayner S, Young J, Pognan F, Hawkins E, Currie I, Davison M: Validation and development of fluorescence two-dimensional differential gel electrophoresis proteomics technology. Proteomics 2001, 1: 377–396. 10.1002/1615-9861(200103)1:3<377::AID-PROT377>3.0.CO;2-6PubMedView Article
  15. Washburn MP, Wolters D, Yates JR: Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nature Biotechnology 2001, 19: 242–247. 10.1038/85686PubMedView Article
  16. Washburn MP, Ulaszek R, Deciu C, Schieltz DM, Yates JR: Analysis of quantitative proteomic data generated via multidimensional protein identification technology. Analytical Chemistry 2002, 74: 1650–1657. 10.1021/ac015704lPubMedView Article
  17. Wu CC, MacCoss MJ, Howell KE, Yates JR: A method for the comprehensive proteomic analysis of membrane proteins. Nature Biotechnology 2003, 21: 532–538. 10.1038/nbt819PubMedView Article
  18. Katz E, Fon M, Lee Y, Phinney B, Sadka A, Blumwald E: The citrus fruit proteome: insights into citrus fruit metabolism. Planta 2007, 226: 989–1005. 10.1007/s00425-007-0545-8PubMedView Article
  19. Ong S-E, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M: Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Molecular and Cellular Proteomics 2002, 1: 376–386. 10.1074/mcp.M200025-MCP200PubMedView Article
  20. Hansen KC, Schmitt-Ulms G, Chalkley RJ, Hirsch J, Baldwin MA, Burlingame AL: Mass spectrometric analysis of protein mixtures at low levels using cleavable 13 C-isotope-coded affinity tag and multidimensional chromatography. Molecular and Cellular Proteomics 2003, 2: 299–314.PubMed
  21. Li J, Steen H, Gygi SP: Protein profiling with cleavable isotope-coded affinity tag (cICAT) reagents: The yeast salinity stress response. Molecular and Cellular Proteomics 2003, 2: 1198–1204. 10.1074/mcp.M300070-MCP200PubMedView Article
  22. Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, Khainovski N, Pillai S, Dey S, Daniels S, et al.: Multiplexed protein quantitation in saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Molecular and Cellular Proteomics 2004, 3: 1154–1169. 10.1074/mcp.M400129-MCP200PubMedView Article
  23. Miyagi M, Rao KC: Proteolytic 18 O-labeling strategies for quantitative proteomics. Mass Spectrometry Reviews 2007, 26: 121–136. 10.1002/mas.20116PubMedView Article
  24. Wang M, You J, Bemis KG, Tegeler TJ, Brown DPG: Label-free mass spectrometry-based protein quantification technologies in proteomic analysis. Brief Funct Genomic Proteomic 2008, 7: 329–339. 10.1093/bfgp/eln031PubMedView Article
  25. Old WM, Meyer-Arendt K, Aveline-Wolf L, Pierce KG, Mendoza A, Sevinsky JR, Resing KA, Ahn NG: Comparison of label-free methods for quantifying human proteins by shotgun proteomics. Molecular and Cellular Proteomics 2005, 4: 1487–1502. 10.1074/mcp.M500084-MCP200PubMedView Article
  26. Stevenson SE, Chu Y, Ozias-Akins P, Thelen JJ: Validation of gel-free, label-free quantitative proteomics approaches: Applications for seed allergen profiling. Journal of Proteomics 2009, 72: 555–566. 10.1016/j.jprot.2008.11.005PubMedView Article
  27. Bondarenko PV, Chelius D, Shaler TA: Identification and relative quantitation of protein mixtures by enzymatic digestion followed by capillary reversed-phase liquid chromatography-tandem mass spectrometry. Analytical Chemistry 2002, 74: 4741–4749. 10.1021/ac0256991PubMedView Article
  28. Chelius D, Bondarenko PV: Quantitative profiling of proteins in complex mixtures using liquid chromatography and mass spectrometry. Journal of Proteome Research 2002, 1: 317–323. 10.1021/pr025517jPubMedView Article
  29. Wang W, Zhou H, Lin H, Roy S, Shaler TA, Hill LR, Norton S, Kumar P, Anderle M, Becker CH: Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Analytical Chemistry 2003, 75: 4818–4826. 10.1021/ac026468xPubMedView Article
  30. Liu H, Sadygov RG, Yates JR: A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Analytical Chemistry 2004, 76: 4193–4201. 10.1021/ac0498563PubMedView Article
  31. Gilchrist A, Au CE, Hiding J, Bell AW, Fernandez-Rodriguez J, Lesimple S, Nagaya H, Roy L, Gosline SJC, Hallett M: Quantitative proteomics analysis of the secretory pathway. Cell 2006, 127: 1265–1281. 10.1016/j.cell.2006.10.036PubMedView Article
  32. Wang G, Wu WW, Zeng W, Chou C-L, Shen R-F: Label-free protein quantification using LC-coupled ion trap or FT mass spectrometry: reproducibility, linearity, and application with complex proteomes. Journal of Proteome Research 2006, 5: 1214–1223. 10.1021/pr050406gPubMedView Article
  33. Baginsky S: Plant proteomics: Concepts, applications, and novel strategies for data interpretation. Mass Spectrometry Reviews 2009, 28: 93–120. 10.1002/mas.20183PubMedView Article
  34. Bianco L, Lopez L, Scalone AG, Di Carli M, Desiderio A, Benvenuto E, Perrotta G: Strawberry proteome characterization and its regulation during fruit ripening and in different genotypes. Journal of Proteomics 2009, 72: 586–607. 10.1016/j.jprot.2008.11.019PubMedView Article
  35. Shi JX, Chen S, Gollop N, Goren R, Goldschmidt EE, Porat R: Effects of anaerobic stress on the proteome of citrus fruit. Plant Science 2008, 175: 478–486. 10.1016/j.plantsci.2008.05.019View Article
  36. Muccilli V, Licciardello C, Fontanini D, Russo MP, Cunsolo V, Saletti R, Reforgiato Recupero G, Foti S: Proteome analysis of Citrus sinensis L. (Osbeck) flesh at ripening time. Journal of Proteomics 2009, 73: 134–152. 10.1016/j.jprot.2009.09.005PubMedView Article
  37. Tanou G, Job C, Rajjou L, Arc E, Belghazi M, Diamantidis G, Molassiotis A, Job D: Proteomics reveals the overlapping roles of hydrogen peroxide and nitric oxide in the acclimation of citrus plants to salinity. The Plant Journal 2009, 60: 795–804. 10.1111/j.1365-313X.2009.04000.xPubMedView Article
  38. Yun Z, Li W, Pan Z, Xu J, Cheng Y, Deng X: Comparative proteomics analysis of differentially accumulated proteins in juice sacs of ponkan (Citrus reticulata) fruit during postharvest cold storage. Postharvest Biology and Technology 2010, 56: 189–201. 10.1016/j.postharvbio.2010.01.002View Article
  39. Zhang B, VerBerkmoes NC, Langston MA, Uberbacher E, Hettich RL, Samatova NF: Detecting differential and correlated protein expression in label-free shotgun roteomics. Journal of Proteome Research 2006, 5: 2909–2918. 10.1021/pr0600273PubMedView Article
  40. Foss EJ, Radulovic D, Shaffer SA, Ruderfer DM, Bedalov A, Goodlett DR, Kruglyak L: Genetic basis of proteome variation in yeast. Nature Genetics 2007, 39: 1369–1375. 10.1038/ng.2007.22PubMedView Article
  41. Gstaiger M, Aebersold R: Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nature Reviews Genetics 2009, 10: 617–627. 10.1038/nrg2633PubMedView Article
  42. Choudhary C, Mann M: Decoding signalling networks by mass spectrometry-based proteomics. Nature Reviews Molecular Cell Biology 2010, 11: 427–439. 10.1038/nrm2900PubMedView Article
  43. Zybailov B, Rutschow H, Friso G, Rudella A, Emanuelsson O, Sun Q, van Wijk KJ: Sorting signals, N-terminal modifications and abundance of the chloroplast proteome. PLoS ONE 2008, 3: e1994. 10.1371/journal.pone.0001994PubMed CentralPubMedView Article
  44. Talon M, Gmitter FG: Citrus Genomics. Interbational Journal of Plant Genomics 2008.
  45. Ebine K, Ueda T: Unique mechanism of plant endocytic/vacuolar transport pathways. Journal of Plant Research 2009, 122: 21–30. 10.1007/s10265-008-0200-xPubMedView Article
  46. Zerial M, McBride H: Rab proteins as membrane organizers. Nature Reviews Molecular Cell Biology 2001, 2: 107–117. 10.1038/35052055PubMedView Article
  47. Nielsen E, Cheung AY, Ueda T: The regulatory RAB and ARF GTPases for vesicular trafficking. Plant Physiology 2008, 147: 1516–1526. 10.1104/pp.108.121798PubMed CentralPubMedView Article
  48. Vernoud V, Horton AC, Yang Z, Nielsen E: Analysis of the small GTPase gene superfamily of Arabidopsis. Plant Physiology 2003, 131: 1191–1208. 10.1104/pp.013052PubMed CentralPubMedView Article
  49. Winter V, Hauser M-T: Exploring the ESCRTing machinery in eukaryotes. Trends in Plant Science 2006, 11: 115–123. 10.1016/j.tplants.2006.01.008PubMed CentralPubMedView Article
  50. Smith LG, Oppenheimer DG: Spatial control of cell expansion by the plant cytoskeleton. Annual Review of Cell and Developmental Biology 2005, 21: 271–295. 10.1146/annurev.cellbio.21.122303.114901PubMedView Article
  51. Reddy VS, Safadi F, Zielinski RE, Reddy ASN: Interaction of a kinesin-like protein with calmodulin isoforms from Arabidopsis. Journal of Biological Chemistry 1999, 274: 31727–31733. 10.1074/jbc.274.44.31727PubMedView Article
  52. McCormack E, Tsai Y-C, Braam J: Handling calcium signaling: Arabidopsis CaMs and CMLs. Trends in Plant Science 2005, 10: 383–389. 10.1016/j.tplants.2005.07.001PubMedView Article
  53. Smertenko AP, Chang H-Y, Sonobe S, Fenyk SI, Weingartner M, Bogre L, Hussey PJ: Control of the AtMAP65–1 interaction with microtubules through the cell cycle. Journal of Cell Science 2006, 119: 3227–3237. 10.1242/jcs.03051PubMedView Article
  54. Smertenko AP, Kaloriti D, Chang H-Y, Fiserova J, Opatrny Z, Hussey PJ: The C-terminal variable region specifies the dynamic properties of Arabidopsis microtubule-associated protein MAP65 isotypes. Plant Cell 2008, 20: 3346–3358. 10.1105/tpc.108.063362PubMed CentralPubMedView Article
  55. Katz E, Lagunes PM, Riov J, Weiss D, Goldschmidt EE: Molecular and physiological evidence suggests the existence of a system II-like pathway of ethylene production in non-climacteric Citrus fruit. Planta 2004, 219: 243–252. 10.1007/s00425-004-1228-3PubMedView Article
  56. Bain JM: Morphological, anatomical, and physiological changes in the developing fruit of the Valencia orange, Citrus sinensis (L.) Osbeck. Australian Journal of Botany 1958, 6: 1–23. 10.1071/BT9580001View Article
  57. Oeljeklaus S, Meyer HE, Warscheid B: Advancements in plant proteomics using quantitative mass spectrometry. Journal of Proteomics 2009, 72: 545–554. 10.1016/j.jprot.2008.11.008PubMedView Article
  58. Choi H, Fermin D, Nesvizhskii AI: Significance analysis of spectral count data in label-free shotgun proteomics. Molecular and Cellular Proteomics 2008, 7: 2373–2385. 10.1074/mcp.M800203-MCP200PubMed CentralPubMedView Article
  59. Aleza P, Juarez J, Hernandez M, Pina J, Ollitrault P, Navarro L: Recovery and characterization of a Citrus clementina Hort. ex Tan. 'Clemenules' haploid plant selected to establish the reference whole Citrus genome sequence. BMC Plant Biology 2009., 110:
  60. Elias JE, Haas W, Faherty BK, Gygi SP: Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations. Nature Methods 2005, 2: 667–675. 10.1038/nmeth785PubMedView Article
  61. Keller A, Nesvizhskii AI, Kolker E, Aebersold R: Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Analytical Chemistry 2002, 74: 5383–5392. 10.1021/ac025747hPubMedView Article
  62. Listgarten J, Emili A: Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry. Molecular and Cellular Proteomics 2005, 4: 419–434. 10.1074/mcp.R500005-MCP200PubMedView Article
  63. Qian W-J, Jacobs JM, Liu T, Camp DG, Smith RD: Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications. Molecular and Cellular Proteomics 2006, 5: 1727–1744. 10.1074/mcp.M600162-MCP200PubMed CentralPubMedView Article
  64. Lu P, Vogel C, Wang R, Yao X, Marcotte EM: Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nature Biotechnology 2007, 25: 117–124. 10.1038/nbt1270PubMedView Article
  65. Lu C, Zainal Z, Tucker GA, Lycett GW: Developmental abnormalities and reduced fruit softening in tomato plants expressing an antisense Rab11 GTPase gene. Plant Cell 2001, 13: 1819–1833. 10.1105/tpc.13.8.1819PubMed CentralPubMedView Article
  66. Abbal P, Pradal M, Muniz L, Sauvage F-X, Chatelet P, Ueda T, Tesniere C: Molecular characterization and expression analysis of the Rab GTPase family in Vitis vinifera reveal the specific expression of a VvRabA protein. Journal of Experimental Botany 2008, 59: 2403–2416. 10.1093/jxb/ern132PubMedView Article
  67. Lycett G: The role of Rab GTPases in cell wall metabolism. Journal of Experimental Botany 2008, 59: 4061–4074. 10.1093/jxb/ern255PubMedView Article
  68. Bassham DC, Brandizzi F, Otegui MS, Sanderfoot AA: The secretory system of Arabidopsis. In The Arabidopsis Book. The American Society of Plant Biologists; 2008:1–29. The Arabidopsis Book] 10.1199/tab.0116
  69. Dettmer J, Hong-Hermesdorf A, Stierhof Y-D, Schumacher K: Vacuolar H+-ATPase activity is required for endocytic and secretory trafficking in Arabidopsis. Plant Cell 2006, 18: 715–730. 10.1105/tpc.105.037978PubMed CentralPubMedView Article
  70. Marshansky V, Futai M: The V-type H+-ATPase in vesicular trafficking: targeting, regulation and function. Curr Opin Cell Biol 2008, 20: 415–426. 10.1016/j.ceb.2008.03.015PubMedView Article
  71. Soule J, Grierson W: Anatomy and physiology. In Fresh citrus fruit. Edited by: Wardowski W, Nagy s. W. G: AVI, Westport, Conn; 1986:1–22.View Article
  72. Spiegel-Roy P, Goldschmidt EE: Biology of Citrus. Cambridge University Press; 1996.View Article
  73. Etxeberria E, Gonzalez P, Pozueta J: Evidence for two endocytic transport pathways in plant cells. Plant Science 2009, 177: 341–348. 10.1016/j.plantsci.2009.06.014View Article
  74. Etxeberria E, Gonzalez P, Pozueta-Romero J: Sucrose transport into citrus juice cells: evidence for an endocytic transport system. Journal of the American Society for Horticultural Science 2005, 130: 269–274.
  75. Wessel D, Fluegge UI: A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids. Analytical Biochemistry 1984, 138: 141–143. 10.1016/0003-2697(84)90782-6PubMedView Article
  76. Rosenfeld J, Capdevielle J, Guillemot JC, Ferrara P: In-gel digestion of proteins for internal sequence analysis after one- or two-dimensional gel electrophoresis. Analytical Biochemistry 1992, 203: 173–179. 10.1016/0003-2697(92)90061-BPubMedView Article
  77. Nesvizhskii AI, Keller A, Kolker E, Aebersold R: A statistical model for identifying proteins by tandem mass spectrometry. Analytical Chemistry 2003, 75: 4646–4658. 10.1021/ac0341261PubMedView Article

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