Large Scale Identification and Quantitative Profiling of ...Thelen2… · identity of 103 spots was determined using LC-MS/MS. The identified spots represented 70 non-redundant phos-phoproteins

Large Scale Identification and QuantitativeProfiling of Phosphoproteins Expressed duringSeed Filling in Oilseed Rape*□S

Ganesh Kumar Agrawal‡§¶ and Jay J. Thelen‡

Seed filling is a dynamic, temporally regulated phase ofseed development that determines the composition ofstorage reserves in mature seeds. Although the metabolicpathways responsible for storage reserve synthesis suchas carbohydrates, oils, and proteins are known, little isknown about their regulation. Protein phosphorylation is aubiquitous form of regulation that influences many as-pects of dynamic cellular behavior in plant biology. Here asystematic study has been conducted on five sequentialstages (2, 3, 4, 5, and 6 weeks after flowering) of seeddevelopment in oilseed rape (Brassica napus L. Reston) tosurvey the presence and dynamics of phosphoproteins.High resolution two-dimensional gel electrophoresis incombination with a phosphoprotein-specific Pro-Q Dia-mond phosphoprotein fluorescence stain revealed �300phosphoprotein spots. Of these, quantitative expressionprofiles for 234 high quality spots were established, andhierarchical cluster analyses revealed the occurrence ofsix principal expression trends during seed filling. Theidentity of 103 spots was determined using LC-MS/MS.The identified spots represented 70 non-redundant phos-phoproteins belonging to 10 major functional categoriesincluding energy, metabolism, protein destination, andsignal transduction. Furthermore phosphorylation within16 non-redundant phosphoproteins was verified by map-ping the phosphorylation sites by LC-MS/MS. Althoughone of these sites was postulated previously, the remain-ing sites have not yet been reported in plants. Phospho-protein data were assembled into a web database. To-gether this study provides evidence for the presence of alarge number of functionally diverse phosphoproteins, in-cluding global regulatory factors like 14-3-3 proteins,within developing B. napus seed. Molecular & CellularProteomics 5:2044–2059, 2006.

The storage reserves found in most plant seeds consist ofcarbohydrates, oils, and proteins (1–5). They contribute up to90% or more of the dry seed weight and are necessary for

seed viability and early seed germination and seedling growth(6). In nature, the relative proportions of the stored compo-nents in seeds vary drastically among different plant species(1, 7). Variation also exits within plant races. For example, arecent survey of oil content in the seed of �360 knownArabidopsis ecotypes revealed a range from 34 to 46% ofseed dry weight (8). Extensive studies on seed developmenthave firmly established that the components of storage re-serve begin to accumulate and their relative levels are deter-mined in the mature seed during a particular phase of seeddevelopment (for reviews, see Refs. 5, 9, and 10), referred toas seed filling (1, 4, 11, 12). The seed filling phase involves celldivision, cell expansion, and the early maturation stage (1, 2).

Numerous studies including two global approaches, tran-scriptomics and proteomics, conducted to date on seed fill-ing, in particular oilseed plants such as oilseed rape (Brassicanapus L.), soybean, and Arabidopsis, continue to producenew paradigms and refine our understanding of the existingbiosynthetic pathways responsible for accumulation of seedstorage reserves (5, 9, 10, 12, 13–20). A microarray of devel-oping Arabidopsis seed provided insight into primary tran-scriptional networks that coordinate the metabolic responsesto seed developmental programs and lead to the distributionof carbon among carbohydrate, oil, and protein reserves (12).Proteomics studies have also been performed in Medicagotruncatula (18), pea (19), and soybean (20) using high resolu-tion two-dimensional gel electrophoresis (2-DGE)1 in combi-nation with either MALDI-TOF-MS or LC-MS/MS. Of thesestudies, the soybean investigation was the most systematicwith respect to quantitative expression profiling and proteinidentification. In that study, 679 and 422 protein spots wereprofiled and identified, respectively, representing 216 non-redundant proteins and 14 functional classes (20). Despitethese investigations, including the large data sets of genesand proteins, the specific underlying regulatory mechanismsthat control the levels of storage reserves in the seed remainlargely unknown.

From the ‡Biochemistry Department, University of Missouri-Co-lumbia, Columbia, Missouri 65211 and §Research Laboratory forAgricultural Biotechnology and Biochemistry, P. O. Box 8207,Kathmandu, Nepal

Received, March 13, 2006, and in revised form, May 22, 2006Published, MCP Papers in Press, July 6, 2006, DOI 10.1074/

mcp.M600084-MCP200

1 The abbreviations used are: 2-DGE, two-dimensional gel electro-phoresis; CBB, Coomassie Brilliant Blue; CV, coefficient of variation;FA, formic acid; PGK, phosphoglycerate kinase; Pro-Q DPS, Pro-QDiamond phosphoprotein stain; WAF, weeks after flowering; TEMED,N,N,N�,N�-tetramethylethylenediamine; 2-D, two-dimensional; ACP,acyl-carrier-protein.

Research

© 2006 by The American Society for Biochemistry and Molecular Biology, Inc.2044 Molecular & Cellular Proteomics 5.11This paper is available on line at http://www.mcponline.org

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/DC1http://www.mcponline.org/cgi/content/full/M600084-MCP200Supplemental Material can be found at:

http://www.mcponline.org

http://www.mcponline.org/cgi/content/full/M600084-MCP200/DC1

Understanding the underlying regulatory mechanism(s) ofthe networks by identifying and characterizing their regulatorycomponents will undoubtedly broaden our knowledge on howthe levels of stored components in the seed are fine tuned.Reversible phosphorylation is a major post-translationalmechanism by which cells transduce cellular, developmental,and environmental signals and thereby control a myriad ofbiological processes in diverse organisms including plants (forreviews, see Refs. 21–23). Previous studies have shown thatseveral intermediary and primary metabolic enzymes are reg-ulated by reversible phosphorylation in plants, including su-crose synthase (24), sucrose-phosphate synthase (Ref. 25; fora review, see Ref. 26), trehalose-6-phosphate synthase (27,28), pyruvate kinase (29), acetyl-CoA carboxylase (30), phos-phoenolpyruvate carboxylase (31), nitrate reductase (32), andthe mitochondrial pyruvate dehydrogenase complex (33).

Hence understanding the dynamics of this post-translationmodification in response to cellular as well as environmentalcues can lead to the identification of candidate regulatoryproteins (protein kinases) and their substrates and therebycan help in dissecting the signaling and metabolic networks.This emerging new area of systems biology is termed phos-phoproteomics (34–36). Many advances in phosphoproteom-ics technologies, including enrichment, detection, phospho-rylation site mapping, and quantification of phosphoproteins,have made the large scale study of phosphoproteins a feasi-ble task (23, 34–39). One of the major developments in thisarea is the detection of phosphoproteins using a unique flu-orescence dye, Pro-Q Diamond phosphoprotein stain (Pro-QDPS; Ref. 40). Two major advantages of this stain are: 1) it canbe used for global quantitative analysis of phosphoproteins asit binds directly to the phosphate moiety of phosphoproteinswith high sensitivity and linearity regardless of phosphoaminoacid and 2) the stain is fully compatible with other stainingmethods and modern MS. Despite these recent advance-ments, phosphoproteins in plants have been rarely studied ona large scale basis (for a review, see Ref. 41). One example ofa large scale phosphoproteomics study is the identification ofmore than 300 phosphorylation sites from Arabidopsis plasmamembrane proteins (42). To our knowledge, no large scalestudy of phosphoproteins has been carried out on developingplant seeds.

We have embarked on large scale phosphoproteomicsstudy during seed filling in B. napus with the following objec-tives: (i) to obtain quantitative expression profiles of phospho-proteins through seed development, (ii) to generate a 2-DGEphosphoprotein reference map, (iii) to determine the phospho-rylation sites of phosphoproteins, and (iv) to begin buildingresources for dissecting biological processes that might beregulated by reversible phosphorylation, including metabo-lism. A high throughput 2-DGE approach in combination withPro-Q DPS and LC-MS/MS were applied on five sequentialstages 2, 3, 4, 5, and 6 weeks after flowering (WAF), coveringthe majority of seed filling. This study reports major achieve-

ments toward fulfilling these objectives by the establishmentof a high resolution 2-DGE phosphoprotein reference mapcomprising 234 quantitative expression profiles, 103 identifiedphosphoproteins, and a map of phosphorylation site in 16non-redundant phosphoproteins. This study also extends theoilseed proteomics web-based database with a “Brassicaphosphoproteomics” resource for the plant community.

EXPERIMENTAL PROCEDURES

Materials—Pro-Q DPS (product number P33301) and Peppermint-Stick phosphoprotein molecular weight standards (hereafter calledPeppermintStick standards; product number P33350) were from Mo-lecular Probes (Eugene, OR). A protein wide range SigmaMarker(hereafter called SigmaMarker; 6.5–205.0 kDa) was from Sigma (cat-alog number M4038). Acetic acid, acetone, acetonitrile, acrylamide,ammonium persulfate, DTT, methanol, N,N�-methylene bisacrylam-ide, phenol, sodium acetate, SDS, and TEMED were from FisherScientific. Immobiline IPG (pH 4–7 and 3–10, 24-cm) strips wereobtained from GE Healthcare. All other reagents used in this studywere of analytical grade.

Plant Materials and Growth Conditions—B. napus (var. Reston)seeds were grown in soil (Promix, Quakertown, PA) in a growthchamber (light/dark cycles of 16 h (23 °C)/8 h (20 °C), 48% humidity,and light intensity of 8000 lux). Plants were fertilized at 2-week inter-vals (all purpose fertilizer, 15:30:15, nitrogen-phosphorus-potassium).Flowers were tagged immediately after opening of buds (between 1and 3 p.m.). Harvesting of developing seeds was also performedbetween 1 and 3 p.m. during the daytime at precisely 2, 3, 4, 5, and6 WAF. At each developmental stage, the dry weight and total proteincontent of whole seeds were also measured. Total protein was quan-tified in triplicate using dye-binding protein assay kit (Bio-Rad) andchicken �-globulin as standard.

Two-dimensional Gel Electrophoresis—Total seed protein was pre-pared from each developmental stage according to a modified phe-nol-based procedure as described previously (20). The protein pellet,obtained from the above extraction procedure, was suspended in IEFextraction buffer (8 M urea, 2 M thiourea, 2% (w/v) CHAPS, 2% (v/v)Triton X-100, 50 mM DTT) and vortexed at low speed for 30 min atroom temperature followed by centrifugation at 14,000 rpm for 15 minto remove insoluble material. Supernatant was used for measuringprotein concentration and 2-DGE analysis. 2-DGE was carried outusing IPG strips (pH 3–10 or 4–7, 24 cm; GE Healthcare) as describedpreviously (20). One milligram of total protein was mixed with 2.25 �lof IPG buffer (pH 3–10 or 4–7; GE Healthcare) and IEF extractionbuffer to bring up to 450 �l, vortexed for 30 s, and subjected to IEFfollowed by SDS-PAGE. A total of six to eight high resolution 2-D gelswere run for each developmental stage using protein isolated fromfour independent biological samples to finally select four gels forfurther analysis. Additionally “reference gels” were also run for thepurpose of spot matching and their downstream analysis (20) and forthe development of a reference map of phosphoproteins. A referencegel is defined as 0.2 mg of total protein from each of the five devel-opmental stages pooled and resolved by 2-DGE.

SDS-PAGE for Phosphorylation Site Mapping—SDS-PAGE wasperformed by standard methods utilizing 4% T, 2.6% C stacking gels,pH 6.8, and 12% T, 2.6% C separating gels, pH 8.8 (43). The % T isthe total monomer concentration expressed in grams/100 ml, and %C is the percentage of cross-linker. The stacking and separating gelbuffer concentrations were 0.125 M Tris-HCl, pH 6.8, and 0.375 M

Tris-HCl, pH 8.8, respectively. The reservoir buffer concentration was0.025 M Tris, 0.192 M glycine, pH 8.3. All gel and reservoir bufferscontained SDS to a final concentration of 0.1% (w/v). SigmaMarker orprotein samples were heated for 5 min at 75 °C in SDS loading buffer

Quantitative Phosphoproteomics of Developing Rapeseed

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(5% (v/v) glycerol, 60 mm SDS, 100 mm DTT, 0.03 mm bromphenolblue, and 60 mm Tris-HCl, pH 6.8) and were cooled to room temper-ature before loading to 10 and 15% gels. A total of 500 �g was loadedper lane, and electrophoresis was conducted overnight in a Hoefervertical SE600 electrophoresis unit (GE Healthcare catalog number80-6171-96) at room temperature until the dye reached the bottom ofthe gel. Gels were stained with colloidal Coomassie Brilliant Blue(CBB) to detect protein.

Detection of Phosphoproteins and Proteins—For phosphoproteindetection, 2-D gels were stained with a modified protocol using Pro-QDPS (44). Briefly all gels were treated with fixation solution (2 � 30min), washed with deionized water (2 � 15 min), stained with 3-folddiluted Pro-Q DPS in deionized water (120 min), destained withdestaining solution (4 � 30 min) to remove gel-bound nonspecificPro-Q DPS, and washed again with deionized water (2 � 5 min).Following scanning of Pro-Q DPS-stained gels, the same gels werethen overstained with colloidal CBB G-250 to detect proteins (45). Allgels in the incubation solution were constantly shaken on an orbitalshaker (GeneMate, ISC Bioexpress) at room temperature at speeds of35 rpm.

Image Analysis of 2-D Gels—Following the Pro-Q DPS procedure,gels were imaged using an FLA 5000 laser scanner (Fuji MedicalSystems, Stamford, CT) with 532 nm excitation and 550-nm band-pass emission filter. Data were collected as 100-�m resolution, 16-bitTIFF files using the Image Gauge Analysis software (Fuji MedicalSystems). With this software, fluorescent protein signals in 2-D gelswere displayed as dark spots. To quantify phosphoprotein spots inprofile mode, 2-D gels were analyzed using ImageMaster 2D Platinumsoftware version 5 (hereafter called ImageMaster software; GEHealthcare) as described by Hajduch et al. (20). Under applied strin-gent criteria to select phosphoprotein spots for quantification andexpression profiling, only those spots were analyzed that were pres-ent in all four gels derived from independent biological samples of onedevelopmental stage and expressed at least in two of five develop-mental stages. The relative volumes of high quality phosphoproteinspots were determined followed by establishment of their expressionprofiles. To obtain the statistical significance of the variation of eachexpressed phosphoprotein spot volumes across all four selectedreplicates of each developmental stage, the coefficient of variation(CV) was calculated using the following formula,

CV ��

i�1

n

�xi � x�2

n � 1x

�� 100 (Eq. 1)

where x� is the average of relative volumes (x) of spots in biologicalquadruplicate analysis and n is the sample size (four in case ofbiological quadruplicate).

It is important to mention that we used two types of protein mark-ers, PeppermintStick standards and SigmaMarker, in SDS-PAGE.The PeppermintStick standards carry two phosphorylated (ovalbuminand bovine �-casein of 45.0 and 23.6 kDa, respectively) and fournon-phosphorylated (�-galactosidase, bovine serum albumin, avidin,and lysozyme of 116.25, 66.2, 18.0, and 14.4 kDa, respectively)proteins. SigmaMarker contains one phosphorylated protein (ovalbu-min, 45.0 kDa) of 13 protein markers. Detected phosphoprotein spotson 2-D gel were normalized against positive and negative phospho-protein markers to eliminate false positive spots. Colloidal CBB-stained gels were imaged using a Scan Maker 9800XL densitometer(Microtek, Carson, CA). Digitized 2-D gel images (300 dpi, 16-bitgrayscale pixel depth) were analyzed using ImageMaster software asdescribed previously (20).

Hierarchical Cluster Analysis of Expression Profiles—The expres-sion profiles of phosphoproteins established by ImageMaster soft-ware were subjected to hierarchical cluster analysis using SAS sta-tistical software (SAS Institute Inc., Cary, NC). The programaccomplishes the cluster analysis of expression profiles in two steps.The first step is to establish a number of classes that is best suited fora present data set. The “CLUSTER” keyword is used with options“STANDARD METHOD � AVERAGE CCC PSEUDO” as the com-mand for step 1 in which “STANDARD” means to normalize thevariables, “AVERAGE” means a certain clustering method in contrastto the other 10 methods which are included in SAS IDE, and “CCC”and “PSEUDO” are both options for calculating some statistical vari-ables that are used to determine the class number. The second stepis to cluster expression profiles into each of the established classes.Expression profile data are also normalized as follows: any zerobetween two non-zero points is replaced with the average of thevalues of two neighbors, and a linear transformation is used to nor-malize expression profiles of different spots to uniform scale. The SASprogram used procedure “FASTCLUS” for the real clustering and themaximum number of clusters established earlier. SAS also generatesthe variable distance parameter for each spot.

In-gel Trypsin Digestion—Phosphoprotein spots were excised froma 2-D reference gel, transferred to polypropylene 96-well filter bottomplates, and digested with sequencing grade modified trypsin (Pro-mega, Madison, WI) according to a previous procedure (20). In caseof SDS-PAGE, the colloidal CBB-stained gel was cut into 54 bandsper protein sample, diced into 1-mm cubes, and transferred into asterile 1.5-ml microcentrifuge tubes. The gel pieces were washed withwater, destained, and in-gel digested with sequencing grade modifiedtrypsin as described previously (20).

Identification of Phosphoprotein 2-DGE Spots by LC-MS/MS—Before MS analysis, 50 �l of 0.1% (v/v) FA in water (Milli-Q grade) wasadded to each sample well to reconstitute dried peptides. Ten micro-liters were used for mass spectral analysis using a configurationtermed high throughput protein identification on an LTQ ProteomeXlinear ion trap LC-MS/MS instrument. Briefly on-line capillary LCincluded two polymeric PSDVB-based peptide traps (2-�g capacityeach; MicroBioresource) and a fast equilibrating C18 capillary column(Micro-Tech Scientific, Cousteau Ct. Vista, CA; packed with C18, 5�m, 300 Å, 150-�m inner diameter � 10 cm). The method alternatedbetween loading/equilibration and elution using the two peptide trapsto reduce the time required for sample analysis on the LC-MS/MSinstrument. Sample was loaded onto peptide traps for concentrationand desalting prior to final separation by C18 column using an ace-tonitrile gradient (0–80% solvent B in solvent A for a duration of 20min; solvent A � 0.1% (v/v) FA in water; solvent B � 100% acetonitrilecontaining 0.1% FA). The peptide trap and C18 column were thenreset for 2 min and re-equilibrated for 10 min with 100% of solvent Abefore the sample already loaded onto the second trap was eluted.The m/z ratios of eluted peptides and fragmented ions from fusedsilica PicoTip emitter (12 cm, 360-�m outer diameter, 75-�m innerdiameter, 30-�m tip; New Objective, Ringoes, NJ) were analyzed inthe data-dependent positive acquisition mode on the LC-MS/MSinstrument. Following each full scan (400–1600 m/z), a data-depend-ent triggered MS/MS scan for the most intense parent ion was ac-quired. The heated fused silica PicoTip emitter was held at ion spraysof 1.7 kV and a flow rate of 250 nl/min.

Phosphorylation Site Mapping—To map phosphorylation site(s), 10�l of the reconstituted tryptic peptides with 0.1% (v/v) FA in water wassubjected to LC-MS/MS using the ProteomeX-based configuration,termed phosphorylation site mapping method. This configuration uti-lizes two Zorbax 300SB C18 traps (5 �m, 5 � 0.3 mm; Agilent) and oneC18 PicoFrit capillary column (10 cm, 360-�m outer diameter, 75-�minner diameter, 15-�m tip; packed with 5-�m Biobasic C18; Thermo-


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Finnigan). Peptides were loaded onto one of the peptide traps viaautosampler, concentrated, desalted, and eluted into the C18 PicoFritcapillary column with an acetonitrile gradient (0–80% solvent B insolvent A for a duration of 72 min; solvent A � 0.1% (v/v) FA in water;solvent B � 100% acetonitrile containing 0.1% FA). The peptide trapand C18 PicoFrit capillary column were then reset for 2 min andre-equilibrated for 15 min with 100% of solvent A. Following re-equilibration, the sample already loaded onto the second trap waseluted and analyzed. The m/z ratios of eluted peptides and frag-mented ions from the C18 PicoFrit capillary column were analyzed inthe data-dependent neutral loss MS/MS/MS mode. In this mode,each full scan (400–1600 m/z) was followed by three data-dependentMS/MS scans (isolation width, 2 amu; normalized collision energy,35%; minimum signal threshold, 500 counts; dynamic exclusion (re-peat count, 2; repeat duration, 30 s; exclusion list size, 50; exclusionduration, 180 s)) on the top three intense ions from that scan. AnMS/MS/MS scan was automatically performed when the most in-tense peak from the MS/MS spectrum corresponded to a neutral lossevent of 98 (�1), 49 (�2), and 32.7 (�3) m/z � 1.0 Da. The heated C18

PicoFrit tip was held at ion sprays of 1.7 kV and a flow rate of 250nl/min. The total run time was 98 min per sample.

Database Customization and Indexing—The National Center forBiotechnology Information (NCBI; ftp.ncbi.nih.gov/blast/) non-redun-dant database (as of March 2005) was used for querying all data. TheFASTA database utilities and indexer of the BioWorks 3.1SR1 soft-ware allowed us to create a plant database (keywords Arabidopsis,Oryza sativa, Zea mays, Medicago, Brassica, and Glycine) extractedfrom NCBI non-redundant database and to index it against trypsinenzyme and static and differential modifications, respectively. Twotypes of indexed databases were created using the plant database.The indexed database I carried cysteine (carboxyamidomethylation;�57 Da) and methionine (oxidation; �16 Da) as static and differentialmodifications, respectively. The indexed database II carried a staticmodification of �57 Da on cysteine and differential modifications of�16 Da on methionine and �80 Da on serine, threonine, and tyrosineresidues.

Database Search—For identification of phosphoprotein spots, LC-MS/MS (high throughput configuration) data were searched againstthe indexed database I using the SEQUEST algorithm (46, 47) as partof the BioWorks 3.1SR1 software suite. The search parameters forthis database were set as follows: enzyme, trypsin; number of internalcleavage sites, 2; mass range, 400–4000 Da; threshold, 500; mini-mum ion count, 35; and peptide mass tolerance, 1.5 Da. Matchingpeptides were filtered for correlation scores (XCorr at least 1.5, 2.0,and 2.5 for �1, �2, and �3 charged ions, respectively). The XCorr

value represents the overlap correlation between experimental andtheoretical MS/MS spectra produced by candidate peptides in thedatabase. For all protein assignments, a minimum of two uniquepeptides was required. Assignments annotated as “unknown” wereBLASTP searched against the NCBI non-redundant database (as ofMarch 2005) to further query their homology.

The indexed database II was used for analysis of raw data (MS/MSspectra) collected using the configuration “phosphorylation site map-ping method.” The search parameters for this database were thesame except peptide mass tolerance was 2 Da as mentioned for theindexed database I. XCorr values of at least 1.9, 2.5, and 3.3 for �1,�2, and �3 charged ions, respectively, were applied for databasematches. MS/MS/MS spectra were also searched against the data-base with a variable modification of 18 to serine and threonineresidues (dehydroalanine). Resulting spectra were inspected manu-ally to verify phosphorylation events.

Database Construction—The annotated high resolution 2-DGE ref-erence gel image and associated experimental, predicted, and otherdata presented in this study can be freely accessed via the oilseed

proteomics server (oilseedproteomics.missouri.edu) under the links“Phosphoproteomics of B. napus seed filling.” Data are viewablethrough 2-D gel reference map viewer and a protein identificationtable (Table I). The spots on the reference gel are hyperlinked todisplay expression profile and protein identification data.

RESULTS

Physiological Characterization of Seed Development to De-termine Seed Filling Phase—A detailed study of B. napus hasshown previously that the process of embryo developmentoccurs over a period of 11–12 weeks beginning shortly afterpollination through seed desiccation (1). The aqueous solublecomponents and starch constitute up to 80% of the drymatter within the first 4 WAF and is followed by a marked risein the lipid and protein content coinciding with an equivalentdecrease in aqueous and starch fraction between 4 and 6WAF (1, 5, 11). Based on these studies, flowers were taggedimmediately after opening of buds, and siliques were col-lected precisely at stages 2, 3, 4, 5, and 6 WAF to conduct theinitial physiological study to ensure complete coverage ofseed filling.

Seeds collected at different stages and embryos dissectedfrom these seeds are shown in Fig. 1, top panel. A visualobservation of seeds and embryos indicated that seeds at 2WAF are largely occupied with liquid endosperm because theembryos occupied only a small portion of the total seed mass.As seed development progressed, the color of seeds turnedgreen, and the embryo mass increased. The light pale greencolor of seeds changed to deep green and then part of the

FIG. 1. Changes in fresh seed weight, embryo development, andtotal protein during seed filling in B. napus. Dissected seeds andembryos, collected at 2, 3, 4, 5, and 6 WAF, are shown in the toppanel. Fresh seed and embryo mass measured at given stages aregraphically presented as mg per seed or embryo, whereas totalprotein content in seed is presented as mg per seed. Standarddeviation represents the average value of four independent experi-ments and a total of 10 seeds.



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seed coat changed to dark red at 4 and 6 WAF, respectively.A dramatic expansion in embryo size occurred between 2 and4 WAF and continued at a much slower rate until 6 WAF,indicating consumption of aqueous soluble constituents andsugars by the embryo. This is consistent with a previousfinding where a shift from starch to oil accumulation duringthe early stages of cotyledon filling (i.e. embryo development)was shown (48); that is, embryo size increases with reservedeposition. Furthermore a decrease in deep green color fol-lowed by an increase in dark red color at 6 WAF indicates theinitiation of maturation phase around 5 WAF. Another inter-esting observation was that embryos acquired a degree ofphotosynthetic capacity by 4 WAF as apparent by dark greenembryos followed by a change to light green suggesting adecrease in photosynthesis (Fig. 1, Embryos). Two physiolog-ical parameters of seed, fresh seed and embryo mass andtotal protein contents in seed, were also measured at eachseed developmental stage to support our above observations(Fig. 1, lower panel). The fresh seed weight reached a maxi-mum at 5 WAF (6.1 mg/seed) followed by sudden decline at 6WAF (3.6 mg/seed). A similar profile for embryo was alsofound except for only a slight increase in embryo mass at 3WAF. The protein content increased gradually until 4 WAF andthen sharply from 4 to 6 WAF. Therefore, to include mostprocesses occurring during seed filling, seed materials atthese studied stages were ideal for a systematic phosphopro-teomics study.

The pI Range 4–7 Resolves Approximately 90% of Phos-phoproteins—B. napus seed proteins were initially analyzedusing pH 3–10 and pH 4–7 24-cm IPG strips to evaluate theirsuitability for developing a high resolution reference map ofphosphoproteins. Analysis of pH 3–10 2-D gels revealed thatthe vast majority of phosphoprotein spots (�90%) were con-centrated in the region between pH 4 and 7, and many spotsin this region overlapped with each other (data not shown).Overlapping spots cause problems in downstream analysis,such as precise volume quantification and identification byMS. In contrast, analysis of pH 4–7 showed distinct anddefined spots; a slight spot overlap was noticed near the endof pH 4 and 7 and around the region of 84 kDa. Becausequantitative expression profiling of phosphoproteins and es-tablishment of a reference map were the main aims of thisstudy, we decided to use pH 4–7 IPG strips exclusively.

As mentioned under “Experimental Procedures,” high res-olution (24-cm) 2-D gels of 2, 3, 4, 5, and 6 WAF weredeveloped along with their reference gel. In the reference gel,300 phosphoprotein spot groups were assigned by Image-Master software. The dynamic range of spot volume of allPro-Q DPS-detected spots ranged from 1 to 3.2 � 104. Per-functory analysis of 2-D gels revealed dynamic changes ofphosphoprotein spots throughout seed filling (Fig. 2); gel sec-tions presented are representative of four high quality 2-Dgels obtained for each stage. The phosphoprotein spot num-bers were assigned by ImageMaster software after image

analysis of the gels (described below). For example, spotnumbers 669 and 679 in area I were expressed at all thestages analyzed with maximum accumulation at 3 WAF,whereas spot numbers 792 and 868 in area II were initiallydetected at 5 WAF followed by a decrease in their expressionlevels. In area III, spot numbers 831 and 833 were also de-tected at 5 WAF, and their abundance increased dramaticallyat 6 WAF.

After visualization of phosphoprotein spots on 2-D gels andtheir image acquisition, all gels were subsequently stainedwith colloidal CBB in an attempt to directly correlate phos-phoprotein with total protein expression in the same gel.Approximately 1000 high quality protein spots were definedby ImageMaster software in the reference gel. The dynamicrange of spot volume of all visualized protein spots rangedfrom 1 to 4.5 � 106. When the phosphoprotein and totalprotein 2-D images were merged together, only 3% of thespots were unequivocally matched (supplemental figure). Thissuggests that the high sensitivity of Pro-Q DPS (minimum,1–16 ng; Refs. 40 and 44) compared with colloidal CBB (min-imum, 50–100 ng; Ref. 49) precludes direct spot matchingbetween these two stains. Comparison of these images alsoindicated that highly abundant protein spots were not stainedwith Pro-Q DPS, suggesting high specificity of Pro-Q DPS tophosphoproteins.

Quantitative Analysis of Phosphoprotein Spots Established234 Developmental Expression Profiles—A strategy for anal-ysis of acquired 2-D images is schematically presented in Fig.3. Selection of spots and their matching were carried outusing ImageMaster software as described under “Experimen-

FIG. 2. Dynamic variations of phosphoprotein profiles. Threedifferent areas (Areas I-III) of a high resolution 2-D gel were selectedto display the dynamic variations of phosphoproteins during seeddevelopment. Total protein (1 mg) extracted from seeds at 2, 3, 4, 5,and 6 WAF was separated on linear IPG strips (pH 4–7; 24 cm) in thefirst dimension followed by SDS-PAGE (12%) in the second dimen-sion to develop 2-DGE phosphoproteomes. Phosphoprotein spotswere detected using the modified protocol of Pro-Q DPS (44). Theseselected areas are also marked in the 2-D gel phosphoprotein refer-ence map in Fig. 5. Molecular mass of phosphoprotein spots variesbetween 36 and 20 kDa. Results presented here are representative offour independent biological experiments.



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tal Procedures.” As shown at the left-hand side, four 2-D gelsderived from biological quadruplicates were processed toselect high quality spots. To generate high quality quantitativeexpression profiles, the following threshold criteria were ap-plied: first, the spot should be present in all four biologicalreplicate gels, and second, these spots should be detected inat least two developmental stages. Under these conditions,

ImageMaster software established 234 developmental ex-pression profiles (supplemental table); the matched spots andtheir volume were also manually validated. The variance ofacquired quantification data on protein spots was calculatedusing two different statistical formulas, standard deviation(STDEV) and CV, and are presented in the supplemental table.The standard deviation represented the biological and tech-

FIG. 3. Schematic depiction of experimental design for quantitative expression analysis, MS and database analyses, and databaseconstruction. Four 2-D gels from four independent biological samples of each developmental stage were analyzed together with gels fromother developmental stages and a reference gel using the ImageMaster 2D Platinum software version 5 to select those phosphoprotein spotspresent in all four gels analyzed from one stage and also in at least two different developmental stages. An example for 6 WAF and referencegel is given where the numbers at the left corner represent the bar code assigned for each IPG strip by the manufacturer. High quality spotgroups were automatically processed to calculate relative spot volumes, which were then used to generate expression profiles as shown inStep 1 for spot number 718. For MS analysis, phosphoprotein spots were excised from the reference gel, trypsin-digested, and analyzed byLC-MS/MS as shown in Step 2. LC-MS/MS data were analyzed by Sequest, and generated data were assembled to construct a databasetermed Brassica phosphoproteomics. Data are available at the web site oilseedproteomics.missouri.edu/links.php.

FIG. 4. Occurrence of six major ex-pression trends during seed filling.The dynamic phosphorylation profiles of234 phosphoprotein spots were ana-lyzed by hierarchical clustering usingSAS statistics software. The total num-ber of phosphoprotein spots groupedtogether for each profile is listed in pa-rentheses. Values on the y axes are rel-ative volumes of spots in that cluster.



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TABLE IA list of phosphoproteins identified by LC-MS/MS

Identified B. napus phosphoproteins were classified into functional classes as described by Bevan et al. 50). Phosphoproteins were identifiedby LC-MS/MS. Two unique peptides matching to the sequence were considered as positive identifications. The table lists group ID ofphosphoprotein spots, phosphoprotein names, species, accession numbers, theoretical mass (kDa)/pI, experimental mass (kDa)/pI, numberof unique peptides/coverage (%), and expression cluster number. At, Arabidopsis thaliana; Bj, Brassica juncea; Bn, B. napus; Gm, Glycine max;Os, O. sativa; Zm, Z. mays.



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TABLE I—continued



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nical variations arising from four independent sample harvest-ing events followed by four independent protein extractions.The CV values allowed direct spot-to-spot comparison ofsignificance levels in acquired quantitative data; therefore, thestatistical significance of differentially expressed phospho-protein is inversely proportional to the CV value. The dynamicrange in spot volume for these spots varied from 4.5 � 102 to3.2 � 104. An example of quantitative expression profile forspot number 718 is shown (Fig. 3, upper panel).

To characterize the expression trends, hierarchical clusteranalysis was performed on all 234 phosphoprotein expressionprofiles. A total of six expression clusters were identified fromthese data (Fig. 4). In clusters 1, 2, and 3, the relative volumeof phosphoprotein spots increased as seed development pro-gressed and reached a maximum at 3, 4, and 5 WAF, respec-tively, followed by a decrease in abundance. Phosphoproteinspredominantly expressed in the early phase of seed fillinggrouped with clusters 4, 5, and 6, and their abundance gen-erally decreased with seed development. Of particular inter-est, the abundance of phosphoprotein spots in cluster 4dropped to a minimum at 3 WAF and then gradually accumu-lated, reaching a level slightly higher than their initial abun-

dance at 6 WAF. Comparison of clusters 5 and 6 indicatedthat abundance of phosphoprotein spots in cluster 6 declinedsharply, reaching almost undetectable levels at 4 WAF, andremained nearly at the same level until 6 WAF, whereas phos-phoprotein spots in cluster 5 were expressed until 6 WAF. Thetotal number of phosphoprotein expression profiles groupedtogether in each cluster was 15, 32, 99, 8, 34, and 46 inclusters 1, 2, 3, 4, 5, and 6, respectively (Fig. 4). Therefore, themost abundant group is cluster 3, which represents 43% ofthe expressed phosphoproteins during seed filling.

LC-MS/MS and Database Analyses Identified 70 Non-re-dundant Phosphoproteins—Using LC-MS/MS, 103 phospho-protein spots were identified (Table I) of which 70 were uniquephosphoproteins (i.e. independent accession numbers). Fur-thermore a distribution of 7, 10, 44, 4, 17, and 21 phospho-protein spots of 103 identified belonged to clusters 1, 2, 3, 4,5, and 6 in Fig. 4, respectively. The expression cluster numberfor each phosphoprotein is listed in Table I.

Database Construction and Establishment of High Resolu-tion 2-DGE Phosphoprotein Reference Map—The 2-D refer-ence gel map, quantitative expression profiles, and assignedphosphoprotein spots by LC-MS/MS were assembled to de-

FIG. 5. Reference map of phosphoproteins expressed during seed development in B. napus. The group ID number is listed for onlythose spots whose protein identities have been successfully assigned; broken circles indicate proteins of unknown function. The three differentareas (Areas I-III; marked by dotted squares) are expanded in Fig. 2. Molecular masses of protein markers are shown on the left.



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velop the “B. napus phosphoproteomics” database (Fig. 3).An interactive high resolution 2-DGE phosphoprotein refer-ence map serves as the portal for expression and identifica-tion data (Fig. 5). The presence of the cursor on any activespot automatically shows experimental molecular weight, pI,and phosphoprotein name (if identified by LC-MS/MS). Eachof the active spots is hyperlinked to another web page dis-playing the expression profile.

Identified Phosphoproteins Belong to 10 Functional Cate-gories—Seventy non-redundant phosphoproteins were clas-sified into 10 functional categories (Fig. 6): energy (25.7%),metabolism (18.6%), protein destination (12.9%), signal trans-duction (11.4%), disease/defense (8.6%), unclassified (8.6%),protein synthesis (4.3%), storage proteins (4.3%), cell struc-ture (2.9%), and cell growth/division (2.9%). This classificationwas based on that established for Arabidopsis by Bevan et al.(50). The major functional categories were energy, metabo-lism, protein destination, signal transduction, and disease/defense. Phosphoproteins sorted within functional categoriesare listed in Table I.

Phosphoprotein Verification by Mapping the Phosphoryla-tion Sites in 16 Non-redundant Phosphoproteins—To map thephosphorylation sites in identified phosphoproteins, we firstdetermined whether the phosphorylation site mappingmethod on the LTQ linear ion trap LC-MS/MS instrument wasvalid. The instrument method associated with this configura-tion applies data-dependent acquisition of MS/MS/MS spec-tra for improved phosphopeptide identification. Under thiscondition, if an MS/MS spectrum of a peptide yields a neutralloss of 98 amu, an MS/MS/MS analysis is triggered. Applica-tion of this concept was recently demonstrated for character-ization of HeLa cell nuclear phosphoproteins using the LCQDECA XP ion trap mass spectrometer (51).

In-gel digested tryptic peptides of BSA and bovine �-casein

(derived from PeppermintStick standards) as non-phospho-rylated and phosphorylated protein markers, respectively,were used to evaluate the above mentioned configuration andinstrument method. As expected, no phosphopeptides weredetected in the BSA trypsin-digested sample. In contrast, amonophosphorylated peptide (FQS*EEQQQTEDELQDK; theasterisk indicates phosphorylation at the serine residue) wasdetected in the bovine �-casein sample with high confidence(Fig. 7A). The total ion chromatogram and acquired MS spec-tra, including the neutral loss-triggered MS/MS/MS spectrum,of the doubly charged FQS*EEQQQTEDELQDK monophos-phopeptide at m/z 1031.81 (XCorr � 5.2) are shown. Peptideswith phosphoserine and phosphothreonine are known to losephosphoric acid (H3PO4 � 98.0 Da) with low collisional energyin MS/MS (52). In Fig. 7A, the MS/MS spectrum showed thatthe fragment ion from the neutral loss is notably abundant,whereas the informative fragment ions are suppressed. Be-cause the parent ion was doubly charged, the theoreticaldifference between parent ion and the neutral loss fragmention was 49.0 m/z. As the most abundant ion in the MS/MSspectrum was the neutral loss fragment ion at m/z 982.62, theMS/MS/MS spectrum of the neutral loss fragment ion wasautomatically acquired to more accurately determine the phos-phopeptide sequence and the phosphorylation site. Abundanceof the neutral loss fragment ion in the MS/MS spectrum and theacquired MS/MS/MS spectrum confidently determined theamino acid sequence of detected monophosphate peptide andmapped the phosphorylation site at the serine residue demon-strated previously for bovine �-casein (53).

To determine the phosphorylation sites in the identifiedphosphoproteins (Table I), the same reconstituted tryptic pep-tides of 2-D gel spots previously used for identification wereanalyzed. In repeated attempts, we failed to detect phos-phopeptides in these samples. However, a number of reasonscould be attributed to this failure (54), most notably the lowabundance of peptide analyte eluted from faint 2-D gel spots.A recent phosphoproteomics study of developing brain used6 mg of proteins to identify protein phosphorylation sites,reasoning that such an amount of proteins is required tosuccessfully identify phosphorylation events at 10% stoichi-ometry (55). They fractionated total proteins by preparativeSDS-PAGE and analyzed the strong cation exchange-en-riched tryptic phosphopeptides by reverse-phase HPLC incombination with LC-MS/MS. Therefore, we applied an SDS-PAGE approach as schematically presented in Fig. 7B; how-ever, unlike the previous study no phosphopeptide enrich-ment step was included. In this approach, total proteins (0.5mg) from 2, 4, and 6 WAF seed were separated by SDS-PAGEand stained with colloidal CBB. Excised bands were trypsin-digested and analyzed by LC-MS/MS. Among the identifiedphosphopeptides, 21 corresponded to 16 non-redundantphosphoproteins identified from 2-D gel: 14 were singly phos-phorylated, and seven were multisite-phosphorylated (TableII). These phosphoproteins are also highlighted in Table I.

FIG. 6. B. napus seed phosphoproteins belong to 10 majorfunctional classes. Of 103 identified phosphoprotein spots, 70 phos-phoproteins were found to be non-redundant. The pie chart showsthe distribution of these non-redundant phosphoproteins into theirfunctional classes in percentages. Functional classification was per-formed according to Bevan et al. (50).



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DISCUSSION

Phosphoproteomics is still an emerging area in plant biol-ogy. The only systematic study available to date in plants isthe identification of more than 300 phosphorylation sites fromplasma membrane proteins of suspension-cultured Arabidop-sis cells (42). However, a quantitative assessment was notprovided for either the phosphopeptides or phosphoproteins.As protein phosphorylation can be either static or dynamic,

quantitation of phosphorylation events is necessary to distin-guish between the two possibilities to elucidate regulatorynetworks. Phosphoprotein assignment depends mostly on asingle phosphopeptide, and so the analysis is assisted bysequenced and annotated genomes to provide some level ofconfidence. Ostensibly in plants such types of large scaleexperiments are currently possible only in Arabidopsis andrice (O. sativa L.). However, because B. napus is closely

FIG. 7. Mapping of phosphorylation site. A, the phosphopeptide mapping workflow on the LC-MS/MS instrument was evaluated using BSA(protein) and �-casein (phosphoprotein). Phosphopeptide was only detected in �-casein, and its phosphorylation sites were mapped with highconfidence (XCorr � 5.2). A MS/MS spectrum of a phosphopeptide shows a typical neutral loss of phosphoric acid from the most intense peak(precursor ion) of a single MS scan. The triggered MS/MS/MS spectrum is then acquired from the neutral loss precursor ion in MS/MS.Abundant peptide bond fragmentation allowed the unambiguous identification of the peptide (FAS*EEQQQTEDELQDK) from �-casein andmapping the phosphorylation site on serine residue marked by an asterisk. B, a preparative SDS-PAGE approach for identification ofphosphorylation sites. A flow chart depicts the steps used to identify phosphorylation sites using trypsin digestion and LC-MS/MS analysiscoupled to database analysis.



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related to Arabidopsis, it seemed plausible that protein iden-tification and phosphopeptide mapping are achievable withthis crop.

A phosphoprotein stain, Pro-Q DPS, was applied to detectand quantify phosphoproteins resolved by 2-DGE. As phos-phorylation is a dynamic process, quantification of phospho-proteins is one major objective of this study in addition to theidentification of phosphoproteins and their phosphorylationsites. To achieve this objective, we recently investigated someof the inherent limitations with Pro-Q DPS and established amodified protocol to reproducibly detect phosphoproteins onhigh resolution 2-D gels (44). Using this protocol, we detected300 phosphoprotein spots on 2-D gels and provided quanti-tative expression profiles for 234 spots expressed in at leasttwo seed developmental stages (Fig. 3 and supplementaltable). Hierarchical clustering of expression profiles furtherrevealed the occurrence of six major expression trends (Fig.4). It appeared that phosphoproteins in each cluster are reg-ulated in a stage-specific and coordinate manner; that is,abundance of phosphoproteins in clusters 1, 2, and 3 reacheda maximum at 3, 4, and 5 WAF, respectively. Identification of70 non-redundant phosphoproteins and their classificationinto 10 functional categories (energy and metabolism are thelargest groups) suggested that protein phosphorylation might

be involved in the regulation of storage reserve synthesis. Asthese phosphoproteins are of diverse functions ranging fromsignal transduction to protein synthesis and destination andare known to operate on different signaling and metabolicpathways, it is highly likely that phosphoproteins are linked toeach other either through kinase function or commonpathways.

The paucity of systematic studies on phosphoproteins inplants precludes us from discussing relational informationabout the phosphoproteins identified here, particularly be-cause many of these phosphoproteins are novel. However,one can extract important information from the given inven-tory of phosphoproteins and their dynamic behavior. A fewexamples are as follows. Analyses of phosphoproteins be-longing to different functional categories and their dynamicexpressions revealed that the majority of phosphoproteins(75%) involved in signal transduction accumulated predomi-nantly at 2 WAF followed by a sharp decrease in their abun-dance as shown for expression clusters 5 and 6 in Fig. 4. Thisis in contrast to expression profiles of �70% of phosphopro-teins belonging to either the energy or metabolism category,which group with clusters 2 and 3 (Fig. 4 and Table I). Suchopposite expression patterns of phosphoproteins suggest atight coordination between the function of phosphoproteins

TABLE IIA list of tryptic peptides in which phosphorylation sites were determined by LC-MS/MS

Identification of phosphopeptides and their phosphorylation sites (marked by asterisks on the right side of the amino acid residue) using acombination of preparative SDS-PAGE and LC-MS/MS analyses of B. napus protein samples derived from 2, 4, and 6 WAF to confirmphosphoproteins identified based on 2-DGE analysis. The table lists phosphoprotein names, species, peptides, charge state of the peptide,XCorr, and Cn. At, A. thaliana; Bo, Brassica oleracea; Os, O. sativa. The hyphens and periods of the peptides indicate the N and C termini oftheir amino acid sequences and the digestion sites of the trypsin enzyme, respectively.

Serial no. Phosphoprotein name Species Peptide Charge XCorr Cn

1 Mitochondrial transcription terminationfactor-related/mTERF-related

At -.GAS*SSELTQIVSTVPKILGKR.- 2 2.78 0.14

2 JQ1592 tubulin �-8 chain At -.S*T*VCDIPPTGLKMASTFIGNSTSIQEMFR.- 3 3.51 0.043 Monodehydroascorbate reductase Bo -.YET*LIIAT*GST*VLR.- 2 2.53 0.134 Phosphoglycerate kinase At -.MSHIST*GGGASLELLEGK.- 2 2.74 0.305 Pyruvate decarboxylase At -.YEFQMQYGS*IGWS*VGAT*LGYAQASPEK.- 3 3.51 0.056 Putative t-complex protein 1 � chain Os -.MASMMQPQIILLKEGT*DTS*QGR.- 2 2.63 0.147 Cysteine synthase At -.DSAGKVDILVAGVGT*GGTATGVGK.- 2 2.61 0.548 Heat shock protein 70 At -.QAVTNPTNTIFGS*K.- 2 3.06 0.519 26 S proteasome-regulatory particle triple-A

ATPase subunit 5bOs -.EKAPCIIFIDEIDAIGT*KR.- 2 2.65 0.45

10 Putative heat shock protein 82 Os -.T*MEINPENSIMDELR.- 2 3.29 0.19-.T*MEINPENAIMDELR.- 2 4.23 0.24

11 Eukaryotic initiation factor 4B At -.AVERPGS*SASR.- 2 2.50 0.1112 S69215 phosphoprotein phosphatase (EC

3.1.3.16) 2A regulatory chain AAt -.VYS*IREAAANNLK.- 2 2.57 0.31

13 KH domain-like protein Os -.SNPEADS*QYLSELLAEHHK.- 2 2.59 0.18-.S*NPEADSQYLSELLAEHHK.- 2 2.76 0.11-.SNPEADSQYLS*ELLAEHHK.- 2 2.53 0.22

14 Annexin At -.AVMLWT*LDPPER.- 2 2.80 0.1415 14–3-3 protein GF14 � At -.QAFDDAIAELDSLNEES*YKDS*T*LIMQLLR.- 3 3.51 0.1116 FAT domain-containing protein/phosphatidyl-

inositol 3- and 4-kinase familyAt -.NPDS*AAT*KLILHLFR.- 2 3.38 0.47

-.DFVTS*NTPLHHLGYR.- 2 3.18 0.17-.NPDS*AAT*KLILHLFR.- 2 2.72 0.54



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associated with signal transduction and energy/metabolism.The 14-3-3 proteins have been shown previously to regulateboth the signal transduction and metabolic pathways (forreviews, see Refs. 21 and 56–58). The 14-3-3 proteins typi-cally contain two 14-3-3 protein family signature motifs, oneeach near the N- and C-terminal regions and one annexinbinding motif (for reviews, see Refs. 56–58). 14-3-3� has beenshown to interact with and be phosphorylated by multipleprotein kinase C isoforms in platelet-derived growth factor-stimulated human vascular smooth muscle cells (59). How-ever, there is no direct evidence for in vivo phosphorylation of14-3-3 proteins in plants. This study identified two 14-3-3phosphoproteins (Group IDs 720 and 867) and two annexinproteins (Group IDs 658 and 699) (Fig. 5 and Table I). Inaddition, among the known potent targets of 14-3-3 proteins,H�-ATPase and heat shock proteins were also found asphosphoproteins (Fig. 5 and Table I). A recent proteomicsstudy of soybean has also identified four 14-3-3 isoformshighly expressed during seed development (20). These find-

ings suggest that 14-3-3 proteins might be involved in signal-ing and metabolic pathways within developing seed. Theoverall inventory of phosphoproteins also indicated the pres-ence of several phosphoproteins that have not yet been re-ported as a phosphoprotein, including stearoyl-acyl-carrier-pro-tein desaturase (stearoyl-ACP desaturase) (Table I). Stearoyl-ACP desaturase is a key regulator of unsaturated fatty acids andhas been implicated in the regulation of cell growth and devel-opment and the defense/stress responses (60, 61).

By mapping the phosphorylation sites in at least 16 non-redundant phosphoproteins, including the 14-3-3 and an-nexin, this study provided a further level of confidence in theidentified phosphoproteins (Table II). The criteria applied forselection of these phosphopeptides are based on the seminallarge scale phosphoproteomics studies of the developingmouse brain and HeLa cell nuclear phosphoproteins (51, 55).A survey of the published literature on phosphoproteomicsshowed that the criteria for assignment of phosphorylationsites and selection of phosphopeptides are still evolving (42,

FIG. 8. Phosphorylation sites mapped within the 14-3-3 phosphoprotein are conserved across organisms. A, amino acid residuesranging from 184 to 245 in the sequence of H. sapiens 14-3-3 protein isoform are shown. These sequences encompass helices 8 and 9 (boxedregions filled with a gray color). Block 5 is the last block of the sequence that shows conserved identity in 99% of all sequences. The proteinsignature motif 2 of the 14-3-3 protein includes helix 9 and surrounding few sequences. The amino acid motif known as nuclear export signal(NES) is also shown. Bold amino acids represent the full tryptic peptide identified by LC-MS/MS. B, the 14-3-3 proteins were selected fromdiverse organisms, such as mammals, insects, worm, fungi, and plants, as representative, and their sequences were aligned using the ClustalWprogram (European Bioinformatics Institute). For clarity, only those amino acid sequences are shown that match to the tryptic peptide identifiedby LC-MS/MS (see Table II). The source, accession number, total length, and homology of respective 14-3-3 sequences are also given.Because H. sapiens 14-3-3 protein is well studied (62), the sequences of this protein were used as a reference to determine the percentageof homology of other 14-3-3 proteins. Amino acids shaded in a gray color were found to be phosphorylated. Of three phosphorylation sitesidentified in this study, serine is the only one (marked by asterisk in A) previously postulated for phosphorylation (59, 62). aa, amino acids.



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51, 54, 55). Nevertheless among the applied criteria, the XCorr

value of at least 1.9, 2.5, and 3.3 for �1, �2, and �3 chargedions, respectively, appears to be widely accepted. The givenXCorr value has been applied along with the concept of data-dependent MS/MS/MS strategy to accurately assign the phos-phorylation sites in this study. However, in our experience andas discussed by Beausoleil et al. (51), data-dependent MS/MS/MS strategy may be unnecessary for large scale phos-phoprotein analyses. It was previously noted that only 96 of2002 phosphorylation sites were determined from an MS/MS/MS spectrum (51). Of the mapped phosphorylation sitesin this study, one site at the serine residue of 14-3-3 phos-phoprotein was previously postulated based on phosphoryl-ation of human 14-3-3� and on the presence of serine in allspecies (59, 62). Two serine, including one previously specu-lated, and threonine residues were identified as potential can-didate sites for phosphorylation (Table II). Considering 14-3-3proteins as ubiquitous regulatory factors, the 14-3-3 proteinswere selected from diverse organisms, and their amino acidsequences were aligned together to determine the functionalsignification of the mapped phosphorylation sites (Fig. 8). Forthis purpose, Homo sapiens 14-3-3 protein isoform se-quence was used as reference because the crystal structureof this isoform has been determined (62). As highlighted witha gray color in Fig. 8B, each putative phosphorylation site isconserved from plants to mammals and is located within thesignature motif 2. The helix 9 of the C-terminal tail is within thesignature motif 2, and this helix contains seven residues,which are directly involved in peptide binding (62, 63). There-fore, it is likely that phosphorylation at identified phosphoryl-ation sites affects ligand bindings or induces conformationalchanges, which eventually might be important for specificityand selectivity of binding partners. This idea is consistent withincreasing evidence that 14-3-3 post-translational modifica-tions act as mechanisms for regulating interactions (64). Inparticular, the direct phosphorylation of 14-3-3 proteins, me-diated by a variety of protein kinases, alters the binding affinityand dimerization properties of different 14-3-3 isoforms (64).

Tubulin �-8 chain, annexin, and phosphoglycerate kinase(PGK) were among the identified phosphoproteins in whichthe phosphorylation sites were also determined (Tables I andII). Tubulin �-subunit and annexin have previously been im-plicated in cell division during embryogenesis and in a numberof environmental and developmental signals (for reviews, seeRefs. 65–68; Ref. 69). The tubulin �-subunit (Group ID 512)and annexin (Group IDs 658 and 699) phosphoprotein spotswere expressed predominantly at 2 WAF followed by a de-crease in their abundance, and this matched well with theperiod of cell division in B. napus (i.e. before 3 WAF; Refs. 1and 5). The functional significance of these phosphoproteinsin seed development comes from the detected phosphopep-tides and their mapped phosphorylation sites. Sequences ofdetected phosphopeptides of tubulin �-subunit (S*T*VCDIPP-TGLKMASTFIGNSTSIQEMFR) and annexin (AVMLWT-

*LDPPER) were located at the C and N termini of their aminoacid sequences, respectively. In mammals, the C-terminalregion of tubulin �-subunits and the N-terminal region ofannexins, respectively, have been shown previously as thetargets for phosphorylation (66, 67, 70). The threonine residueof annexin is also located within the second annexin repeat ofthe N-terminal region. In addition to tubulin �-subunits andannexins, PGK could also play a key role in seed developmentbecause it catalyzes the conversion of 1,3-bisphosphoglyc-erate into 3-phosphoglycerate. Along with PGK, this studyidentified a total of five glycolytic enzymes as phosphopro-teins (Table I). However, the phosphopeptide could only bedetected for PGK (MSHIST*GGGASLELLEGK). Like the 14-3-3 phosphopeptide, sequence alignment of PGK proteinsselected from diverse organisms showed that the mappedthreonine residue is also conserved from plants to mammals,suggesting the importance of this residue in PGK function. Itis possible that in vivo phosphorylation of tubulin �-subunits,annexins, PGK, and other phosphoproteins identified in thisstudy might alter their activity and stability to properly regulatethe cellular processes of seed filling in B. napus.

In conclusion, we present the first quantitative global anal-ysis of phosphoproteins during seed filling in B. napus. Phos-phoprotein-specific Pro-Q DPS coupled to a 2-DGE and LC-MS/MS strategy led to the generation of 234 quantitativeexpression profiles of phosphoproteins and to the identifica-tion of 70 non-redundant phosphoproteins. Equally importantwas the establishment of a high resolution 2-D gel phospho-protein reference map, which could be used for comparativefunctional proteomics. Identification of in vivo 14-3-3 phos-phoproteins and their potential phosphorylation sites implieda potential role for 14-3-3 also in seed development. Thiscollective data set is a step forward in the analysis of the seedfilling phosphoproteome in B. napus and toward an under-standing of the regulatory mechanisms behind embryo devel-opment and seed reserve deposition. Future studies will bedirected toward investigating the function of phosphoproteinsinvolved in mediating multiple signaling and metabolic path-way such as 14-3-3 phosphoproteins and developing a suit-able and high throughput system to map phosphorylationsites in Pro-Q DPS-detected phosphoprotein spots on 2-Dgels. The latter part will help determine whether the changesin expression of phosphoproteins are due to total phospho-protein expression or phosphorylation status (i.e. phosphoryl-ation stoichiometry).

Acknowledgments—We thank Prof. Vishwanath P. Agrawal, Exec-utive Director of the Research Laboratory for Agricultural Biotechnol-ogy and Biochemistry (Kathmandu, Nepal), for support during thework. We also thank Zhao Song (Computer Science Department,University of Missouri-Columbia) for hierarchical clustering and allmembers of Dr. Thelen’s laboratory, in particular Dr. Martin Hajduch,for help.

* This work was supported by National Science Foundation PlantGenome Research Grants DBI-0332418 and DBI-0445287 (to



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J. J. T.). The costs of publication of this article were defrayed in partby the payment of page charges. This article must therefore be herebymarked “advertisement” in accordance with 18 U.S.C. Section 1734solely to indicate this fact.

□S The on-line version of this article (available at http://www.mcponline.org) contains supplemental material.

¶ To whom correspondence should be addressed: BiochemistryDept., University of Missouri-Columbia, 109 Life Sciences Center,Columbia, MO 65211. Tel.: 573-884-5979; Fax: 573-884-9676;E-mail: [email protected].

REFERENCES

1. Norton, G., and Harris, J. F. (1975) Compositional changes in developingrape seed (Brassica napus L.). Planta 123, 163–174

2. Mienke, D. W., Chen, J., and Beachy, R. N. (1981) Expression of storage-protein genes during soybean seed development. Planta 153, 130–139

3. Murphy, D. J., Cummins, I., and Kang, A. S. (1989) Synthesis of the majoroil-body membrane protein in developing rapeseed (Brassica napus)embryos. Integration with storage-lipid and storage-protein synthesisand implications for the mechanism of oil-body formation. Biochem. J.258, 285–293

4. Baud, S., Boutin, J., Miquel M, Lepiniec, L., and Rochat, C. (2002) Anintegrated overview of seed development in Arabidopsis thalianaecotype WS. Plant Physiol. Biochem. 40, 151–160

5. Hills, M. J. (2004) Control of storage-product synthesis in seeds. Curr. Opin.Plant Biol. 7, 302–308

6. Bewley, J. D., and Black, M. (1994) Seeds: Physiology of Development andGermination, Plenum Press, New York

7. Gunstone, D. F., Harwood, J. L., and Padley, F. B. (1995) The Lipid Hand-book, Chapman & Hall, London

8. O’Neill, C. M., Gill, S., Hobbs, D., Morgan, C., and Bancroft, I. (2003) Naturalvariation for seed oil composition in Arabidopsis thaliana. Phytochemistry64, 1077–1090

9. Schwender, J., Ohlrogge, J., and Shachar-Hill, Y. (2004) Understanding fluxin plant metabolic networks. Curr. Opin. Plant Biol. 7, 309–317

10. Weber, H., Ljudmilla, B., and Wobus, U. (2005) Molecular physiology oflegume seed development. Annu. Rev. Plant Biol. 56, 253–279

11. Murphy, D. J., and Cummins, I. (1989) Biosynthesis of seed storage prod-ucts during embryogenesis in rapeseed, Brassica napus. J. Plant Physiol.135, 63–69

12. Ruuska, S. A., Girke, T., Benning, C., and Ohlrogge, J. B. (2004) Contra-puntal networks of gene expression during Arabidopsis seed filling. PlantCell 14, 1191–1206

13. Ohlrogge, J., Pollard, M., Bao, X., Focke, M., Girke, T., Ruuska, S., Mekhe-dov, S., and Benning, C. (2000) Fatty acid synthesis: from CO2 tofunctional genomics. Biochem. Soc. Trans. 28, 567–573

14. Girke, T., Todd, J., Ruuska, S., White, J., Benning, C., and Ohlrogge, J.(2000) Microarray analysis of developing Arabidopsis seeds. PlantPhysiol. 124, 1570–1581

15. Slabas, A. R., Simon, J. W., and Brown, A. P. (2001) Biosynthesis andregulation of fatty acids and triglycerides in oil seed rape. Current statusand future trends. Eur. J. Lipid Sci. Technol. 103, 455–466

16. Voelker, T., and Kinney, A. J. (2001) Variations in the biosynthesis ofseed-storage lipids. Annu. Rev. Plant Physiol. Plant Mol. Biol. 52,335–361

17. Rawsthorne, S. (2002) Carbon flux and fatty acid synthesis in plants. Prog.Lipid Res. 41, 182–196

18. Gallardo, K., Le Signor, C., Vandekerckhove, J., Thompson, R. D., andBurstin, J. (2003) Proteomics of Medicago truncatula seed developmentestablishes the time frame of diverse metabolic processes related toreserve accumulation. Plant Physiol. 133, 664–682

19. Schiltz, S., Gallardo, K., Huart, M., Negroni, L., Sommerer, N., and Burstin,J. (2004) Proteome reference maps of vegetative tissues in pea. Aninvestigation of nitrogen mobilization from leaves during seed filling.Plant Physiol. 135, 2241–2260

20. Hajduch, M., Ganapathy, A., Stein, J. W., and Thelen, J. J. (2005) Asystematic proteomic study of seed filling in soybean. Establishment ofhigh-resolution two-dimensional reference maps, expression profiles,and an interactive proteome database. Plant Physiol. 137, 1397–1419

21. Huber, S. C., and Hardin, S. C. (2004) Numerous posttranslational modifi-

cations provide opportunities for the intricate regulation of metabolicenzymes at multiple levels. Curr. Opin. Plant Biol. 7, 318–322

22. Pawson, T., and Scott, J. D. (2005) Protein phosphorylation in signal-ing—50 years and counting. Trends Biochem. Sci. 30, 286–290

23. Mukherji, M. (2005) Phosphoproteomics in analyzing signaling pathways.Expert Rev. Proteomics 2, 117–128

24. Hardin, S. C., Tang, G. Q., Scholz, A., Holtgraewe, D., Winter, H., andHuber, S. C. (2003) Phosphorylation of sucrose synthase at serine 170:occurrence and possible role as a signal for proteolysis. Plant J. 35,588–603

25. McMichael, R. W., Jr., Klein, R. R., Salvucci, M. E., and Huber, S. C. (1993)Identification of the major regulatory phosphorylation site in sucrose-phosphate synthase. Arch. Biochem. Biophys. 307, 248–252

26. Winter, H., and Huber, S. C. (2000) Regulation of sucrose metabolism inhigher plants: localization and regulation of activity of key enzymes. Crit.Rev. Biochem. Mol. Biol. 35, 253–289

27. Moorhead, G., Douglas, P., Cotelle, V., Harthill, J., Morrice, N., Meek, S.,Deiting, U., Stitt, M., Scarabel, M., Aitken, A., and MacKintosh, C. (1999)Phosphorylation-dependent interactions between enzymes of plant me-tabolism and 14-3-3 proteins. Plant J. 18, 1–12

28. Glinski, M., and Weckwerth, W. (2005) Differential multisite phosphorylationof the trehalose-6-phosphate synthase gene family in Arabidopsis thali-ana: a mass spectrometry-based process for multiparallel peptide libraryphosphorylation analysis. Mol. Cell. Proteomics 4, 1614–1625

29. Tang, G. Q., Hardin, S. C., Dewey, R., and Huber, S. C. (2003) A novelC-terminal proteolytic processing of cytosolic pyruvate kinase, its phos-phorylation and degradation by the proteasome in developing soybeanseeds. Plant J. 34, 77–93

30. Savage, L. J., and Ohlrogge, J. B. (1999) Phosphorylation of pea chloro-plast acetyl-CoA carboxylase. Plant J. 18, 521–527

31. Tripodi, K. E., Turner, W. L., Gennidakis, S., and Plaxton, W. C. (2005) Invivo regulatory phosphorylation of novel phosphoenolpyruvate carbox-ylase isoforms in endosperm of developing caster oil seeds. PlantPhysiol. 139, 967–978

32. Garcia-Mata, C., and Lamattina, L. (2003) Abscisic acid, nitric oxide andstomatal closure—is nitrate reductase one of the missing links? TrendsPlant Sci. 8, 20–26

33. Thelen, J. J., Muszynski, M. G., Miernyk, J. A., and Randall, D. D. (1998)Molecular analysis of two pyruvate dehydrogenase kinases from maize.J. Biol. Chem. 273, 26618–26623

34. Mann, M., and Jensen, O. N. (2003) Proteomic analysis of post-translationalmodifications. Nat. Biotechnol. 21, 255–261

35. Laugesen, S., Bergoin, A., and Rossignol, M. (2004) Deciphering the plantphosphoproteome: tools and strategies for a challenging task. PlantPhysiol. Biochem. 42, 929–936

36. Reinders, J., and Sickmann, A. (2005) State-of-the-art in phosphoproteom-ics. Proteomics 5, 4052–4061

37. Salih, E. (2005) Phosphoproteomics by mass spectrometry and classicalprotein chemistry approaches. Mass Spectrom. Rev. 24, 828–846

38. Kange, R., Selditz, U., Granberg, M., Lindberg, U., Ekstrand, G., Ek, B., andGustafsson, M. (2005) Comparison of different IMAC techniques used forenrichment of phosphorylated peptides. J. Biomol. Tech. 16, 91–103

39. Loyet, K. M., Stults, J. T., and Arnott, D. (2005) Mass spectrometric con-tributions to the practice of phosphorylation site mapping through 2003.Mol. Cell. Proteomics 4, 235–245

40. Steinberg, T. H., Agnew, B. J., Gee, K. R., Leung, W.-Y., Goodman, T.,Schulenberg, B., Hendrickson, J., Beechem, J. M., Haugland, R. P., andPatton, W. F. (2003) Global quantitative phosphoprotein analysis usingMultiplexed Proteomics technology. Proteomics 3, 1128–1144

41. Agrawal, G. K., Yonekura, M., Iwahashi, Y., Iwahashi, H., and Rakwal, R.(2005) System, trends and perspectives of proteomics in dicot plants.Part III: Unraveling the proteomes influenced by the environment, and atthe levels of function and genetic relationships. J. Chromatogr. B Anal.Technol. Biomed. Life Sci. 815, 137–145

42. Nuhse, T. S., Stensballe, A., Jensen, O. N., and Peck, S. C. (2004) Phos-phoproteomics of the Arabidopsis plasma membrane and a new phos-phorylation site database. Plant Cell 16, 2394–2405

43. Laemmli, U. K. (1970) Cleavage of structural proteins during the assemblyof the head of bacteriophage T4. Nature 227, 680–685

44. Agrawal, G. K., and Thelen, J. J. (2005) Development of a simplified,economical polyacrylamide gel staining protocol for phosphoproteins.



at University of M

issouri-Colum

bia on Novem

ber 13, 2006 w

ww

.mcponline.org

Dow

nloaded from


Proteomics 5, 4684–468845. Mooney, B. P., and Thelen, J. J. (2004) High-throughput peptide mass

fingerprinting of soybean seed proteins: automated workflow and utilityof UniGene expressed sequence tag databases for protein identification.Phytochemistry 65, 1733–1744

46. Eng, J., McCormack, A. L., and Yates, J. R., III (1994) An approach tocorrelate tandem mass spectral data of peptides with amino acid se-quences in a protein database. J. Am. Mass Spectrom. 5, 976–989

47. Yates, J. R., III, Eng, J. K., McCormack, A. L., and Schieltz, D. (1995)Method to correlate tandem mass spectra of modified peptides to aminoacid sequences in the protein database. Anal. Chem. 67, 1426–1436

48. da Silva, P. M. F. R., Eastmond, P. J., Hill, L. M., Smith, A. M., andRawsthorne, S. (1997) Starch metabolism in developing embryos ofoilseed rape. Planta 203, 480–487

49. Patton, W. F. (2002) Detection technologies in proteome analysis. J. Chro-matogr. B Anal. Technol. Biomed. Life Sci. 771, 3–31

50. Bevan, M., Bancroft, I., Bent, E., Love, K., Goodman, H., Dean, C.,Bergkamp, R., Dirkse, W., Van Staveren, M., Stiekema, W., Drost, L.,Ridley, P., Hudson, S. A., Patel, K., Murphy, G., Piffanelli, P., Wedler, H.,Wedler, E., Wambutt, R., Weitzenegger, T., Pohl, T. M., Terryn, N.,Gielen, J., Villarroel, R., De Clerck, R., Van Montagu, M., Lecharny, A.,Auborg, S., Gy, I., Kreis, M., Lao, N., Kavanagh, T., Hempel, S., Kotter,P., Entian, K. D., Rieger, M., Schaeffer, M., Funk, B., Mueller-Auer, S.,Silvey, M., James, R., Montfort, A., Pons, A., Puigdomenech, P., Douka,A., Voukelatou, E., Milioni, D., Hatzopoulos, P., Piravandi, E., Obermaier,B., Hilbert, H., Dusterhoft, A., Moores, T., Jones, J. D., Eneva, T., Palme,K., Benes, V., Rechman, S., Ansorge, W., Cooke, R., Berger, C., Delseny,M., Voet, M., Volckaert, G., Mewes, H. W., Klosterman, S., Schueller, C.,and Chalwatzis, N. (1998) Analysis of 1.9 Mb of contiguous sequencefrom chromosome 4 of Arabidopsis thaliana. Nature 391, 485–488

51. Beausoleil, S. A., Jedrychowski, M., Schwartz, D., Elias, J. E., Villen, J., Li,J., Cohn, M. A., Cantley, L. C., and Gygi, S. P. (2004) Large-scalecharacterization of HeLa cell nuclear phosphoproteins. Proc. Natl. Acad.Sci. U. S. A. 101, 12130–12135

52. Tholey, A., Reed, J., and Lehmann, W. D. (1999) Electrospray tandem massspectrometric studies of phosphopeptides and phosphopeptide ana-logues. J. Mass Spectrom. 34, 117–123

53. Kim, J., Camp, D. G., II, and Smith, R. D. (2004) Improved detection ofmulti-phosphorylated peptides in the presence of phosphoric acid inliquid chromatography/mass spectrometry. J. Mass Spectrom. 39,208–215

54. Steen, H., Jebanathirajah, J. A., Rush, J., Morrice, N., and Kirschner, M. W.(2006) Phosphorylation analysis by mass spectrometry: myths, facts,and the consequences for qualitative and quantitative measurements.Mol. Cell. Proteomics 5, 172–181

55. Ballif, B. A., Villen, J., Beausoleil, S. A., Schwartz, D., and Gygi, S. P. (2004)Phosphoproteomic analysis of the developing mouse brain. Mol. Cell.Proteomics 3, 1093–1101

56. Comparot, S., Lingiah, G., and Martin, T. (2003) Function of specificity of14-3-3 proteins in the regulation of carbohydrate and nitrogen metabo-lism. J. Exp. Bot. 54, 595–604

57. MacKintosh, C. (2004) Dynamic interactions between 14-3-3 proteins andphosphoproteins regulate diverse cellular processes. Biochem. J. 381,329–342

58. Yaffe, M. B. (2004) Master of all things phosphorylated. Biochem. J. 379,e1-e2

59. Autieri, M. V., and Carbone, C. J. (1999) 14-3-3� interacts with and isphosphorylated by multiple protein kinase C isoforms in PDGF-stimu-lated human vascular smooth muscle cells. DNA Cell Biol. 18, 555–564

60. Shanklin, J., and Cahoon, E. B. (1998) Desaturation and related modifica-tions of fatty acids1. Annu. Rev. Plant Physiol. Plant Mol. Biol. 49,611–641

61. Kachroo, P., Shanklin, J., Shah, J., Whittle, E. J., and Klessig, D. F. (2001)A fatty acid desaturase modulates the activation of defense signalingpathways in plants. Proc. Natl. Acad. Sci. U. S. A. 98, 9448–9453

62. Rosenquist, M., Sehnke, P., Ferl, R. J., Sommarin, M., and Larsson, C.(2000) Evolution of the 14-3-3 protein family: does the large number ofisoforms in multicellular organisms reflect functional specificity? J. Mol.Evol. 51, 446–458

63. Rittinger, K., Budman, J., Xu, J., Volinia, S., Cantley, L. C., Smerdon, S. J.,Gamblin, S. J., and Yaffe, M. B. (1999) Structural analysis of 14-3-3phosphopeptide complexes identifies a dual role for the nuclear exportsignal of 14-3-3 in ligand binding. Mol. Cell 4, 153–166

64. Aitken, A. (2002) Functional specificity in 14-3-3 isoform interactionsthrough dimer formation and phosphorylation. Chromosome location ofmammalian isoforms and variants. Plant Mol. Biol. 50, 993–1010

65. Mayer, U., and Jurgens, G. (2002) Microtubule cytoskeleton: a track record.Curr. Opin. Plant Biol. 5, 494–501

66. Azimzadeh, J., Traas, J., and Pastuglia, M. (2001) Molecular aspects ofmicrotubule dynamics in plants. Curr. Opin. Plant Biol. 4, 513–519

67. Gerke, V., and Moss, S. E. (2002) Annexins: from structure to function.Physiol. Rev. 82, 331–371

68. Delmer, D. P., and Potikha, T. S. (1997) Structures and functions of annex-ins in plants. CMLS Cell. Mol. Life Sci. 53, 546–553

69. Lee, S., Lee, E. J., Yang, E. J., Lee, J. E., Park, A. R., Song, W. H., and Park,O. K. (2004) Proteomic identification of annexins, calcium-dependentmembrane binding proteins that mediate osmotic stress and abscisicacid signal transduction in Arabidopsis. Plant Cell 16, 1378–1391

70. Luduena, R. F. (1993) Are tubulin isotypes functionally significant. Mol. Biol.Cell 4, 445–457



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Large Scale Identification and Quantitative Profiling of ...Thelen2… · identity of 103 spots was determined using LC-MS/MS. The identified spots represented 70 non-redundant phos-phoproteins