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First published online January 19, 2007; 10.1104/pp.106.092148 Plant Physiology 143:1203-1219 (2007) © 2007 American Society of Plant Biologists OPEN ACCESS ARTICLE
Developmental Analysis of Maize Endosperm Proteome Suggests a Pivotal Role for Pyruvate Orthophosphate Dikinase1,[W],[OA]Unité Mixte de Recherche 206, Chimie Biologique, Institut National de la Recherche Agronomique, Institut National Agronomique Paris-Grignon, F–78850 Thiverval Grignon, France (V.M.); Laboratoire Biotechnologie des Plantes, Unité Mixte de Recherche 8618, Université Paris Sud, F–91405 Orsay, France (C.T., J.-L.P.); Centre National de la Recherche Scientifique, F–91405 Orsay, France (C.T., J.-L.P.); and Unité Mixte de Recherche 8120, Institut National de la Recherche Agronomique, Université Paris Sud, Centre National de la Recherche Scientifique, Institut National Agronomique Paris-Grignon, F–91190 Gif sur Yvette, France (M.L.G., C.D.)
Although the morphological steps of maize (Zea mays) endosperm development are well described, very little is known concerning the coordinated accumulation of the numerous proteins involved. Here, we present a proteomic study of maize endosperm development. The accumulation pattern of 409 proteins at seven developmental stages was examined. Hierarchical clustering analysis allowed four main developmental profiles to be recognized. Comprehensive investigation of the functions associated with clusters resulted in a consistent picture of the developmental coordination of cellular processes. Early stages, devoted to cellularization, cell division, and cell wall deposition, corresponded to maximal expression of actin, tubulins, and cell organization proteins, of respiration metabolism (glycolysis and tricarboxylic acid cycle), and of protection against reactive oxygen species. An important protein turnover, which is likely associated with the switch from growth and differentiation to storage, was also suggested from the high amount of proteases. A relative increase of abundance of the glycolytic enzymes compared to tricarboxylic acid enzymes is consistent with the recent demonstration of anoxic conditions during starch accumulation in the endosperm. The specific late-stage accumulation of the pyruvate orthophosphate dikinase may suggest a critical role of this enzyme in the starch-protein balance through inorganic pyrophosphate-dependent restriction of ADP-glucose synthesis in addition to its usually reported influence on the alanine-aromatic amino acid synthesis balance.
The economic and nutritional value of maize (Zea mays) kernels is mainly due to its high starch content because it represents approximately 75% of mature seed weight. Starch is accumulated in endosperm, which, in maize as in other cereals, is persistent at seed maturity and forms a reserve used for the development of the embryo during germination. Cereal endosperm development presents five key steps (Olsen, 2001  ): coenocytic phase, cellularization, differentiation, reserve synthesis, and maturation. In the first place, within hours following pollination, endosperm nuclei undergo numerous divisions without any cellularization. Cell wall formation generally begins 4 d after pollination (dap) when the endosperm contains several thousand cells. Cell division still occurs, but at a lower rate until 20 dap (Larkins et al., 2001). Consistently, an important synthesis of all components involved in cell wall formation takes place during 5 to 10 dap. Next, differentiation takes place and storage products are synthesized from 12 dap to maturity. Thus, most starch present in the endosperm is synthesized within a 25-d period, from about day 12 to day 35 post pollination. During the same time, the endosperm increases in size from less than 0.1 mm to up to 10 mm. Very little is known about the beginning of cell death in the starchy endosperm, but in standard growing conditions in European Community, dry-matter accumulation levels off at about 35 dap and the dry-down process begins rapidly. Endosperm is an important tissue from the point of view of the breeder, because it is a major component of yield, and from the point of view of the physiologist and evolutionist, because it provides the ground for seedling germination and initial growth.
Whereas morphological steps of endosperm development are well described, the underlying molecular mechanisms are still largely unknown. As highlighted before, endosperm development is a complex phenomenon that must be driven by coordinate expression of numerous genes. Approaches using both spontaneous and induced mutants allow characterization of individual developmental steps. In Arabidopsis (Arabidopsis thaliana), despite the nonpersistent nature of the endosperm in seeds, screens of mutants can help to shed light on several events occurring during endosperm development (Berger, 1999
mRNAs are the primary products of gene expression, but their levels are often faintly correlated to cognate protein levels (Gygi et al., 1999
Several proteomic analyses of seed development have been reported in the past years in dicots (Gallardo et al., 2003
To date, to our knowledge, there has been no proteomic study of maize endosperm development. We have established a comprehensive maize endosperm reference map, based on the proteome of 14-dap endosperms (Méchin et al., 2004
Accumulation of Nonstorage Proteins during Development
We characterized the proteome at seven developmental stages scaled on the morphoanatomical description of development. At the early stages (4, 7, and 10 dap), dissection to separate embryo and endosperm was tricky (Fig. 1A
) and thus the proteome of whole kernels was analyzed. The endosperm proteome was analyzed at four later stages: 14, 21, 30, and 40 dap. Accumulated proteins ranging from 10 to 100 kD in Mr and from 4 to 7 in pI were visualized with silver staining. We chose this pH range to exclude most of the abundant storage proteins (zeins that have pIs comprised between 7 and 9) and because we intended to focus on the time course of proteins involved in the setup of the endosperm and the establishment of the enzyme equipment for starch and storage protein accumulation metabolism. Experiments comparing patterns obtained from a pH 3 to 10 range versus a pH 4 to 7 range showed that, whereas supplementary proteins could indeed be detected above pH 7, the quality of resolution, especially in the latest developmental stages because of the abundance of zeins, was better using a pH 4 to 7 range. Our 2-D map was thus based on a pI 4 to 7 2-D pattern, allowing more than 600 proteins to be processed (Méchin et al., 2004
Total protein loading was increased as development proceeded, in an effort to compensate for the increase in zein abundance in the total protein extracts. This allowed better visualization of low-abundance proteins that are supposed to take part in the biochemical processes involved in development. Actually, the compensation appeared partly successful particularly at the oldest stages, which could account for a limited decrease in the number of reproducible spots from 4 dap (1,632) to 40 dap (1,483). The distribution of standardized spot volumes appeared very similar at every stage (data not shown). Nevertheless, the global pattern changed during development, and streaking due to zeins was visible from 14 dap onward (Fig. 1, B–D). Because we intended to follow the accumulation pattern of identified proteins, we used the 14-dap stage as the reference for matching patterns of all other stages. Among the 1,551 reproducible spots obtained at 14 dap (Fig. 1C), 94% could be matched to one or more other stages. The proportion of reproducible spots of any given stage that could be matched to the 14-dap stage was quite high, ranging from 61% at 7 dap to 88% at 21 dap. This indicates that quantitative variations, rather than large qualitative variations, probably account for the apparent global change in 2-D patterns during development.
The silver-stained 14-dap stage pattern from the developmental study was matched against the Coomassie Blue-stained 14-dap pattern supporting the reference map (Méchin et al., 2004
To examine the expression tendency within functional categories (or subcategories), composite expression analysis (Hajduch et al., 2005 The protein destination category presented an important peak of expression at 14 dap, whereas the protein synthesis class displayed a large increase between 10 and 30 dap (data not shown). Proteins involved in metabolic processes massively increased and reached a maximum at 21 dap (Fig. 2 ). Between 4 and 21 dap, the relative abundance of metabolic proteins was multiplied by a factor of 2. Proteins involved in soluble carbohydrate and nucleotide metabolism did not display any particular trend in their accumulation profiles during development, whereas enzymes from secondary metabolism and, to a lesser extent, from the tricarboxylic acid (TCA) cycle presented a decrease (Fig. 2). On the other hand, proteins involved in amino acid biosynthesis and the pyruvate orthophosphate dikinase (PPDK) displayed a similar accumulation profile, with a very important peak at 21 dap (Fig. 2). Glycolysis enzymes presented their maximal level of accumulation at 14 dap. Finally, proteins involved in starch biosynthesis were at a very low level before 14 dap and reached their maximum at 30 to 40 dap (Fig. 2).
Accumulation Profile of Identified Proteins during Development
Hierarchical Clustering Analysis
Functional Categories Distribution among Clusters The distribution of the functional categories appeared significantly different among the four main clusters (  2 12 degrees of freedom, 31.90, P  0.01). The early accumulation cluster encompassed the highest number of proteins and also displayed the largest functional diversity (Fig. 4
). Metabolism and protein destination were the most numerous categories (53% of proteins). The midstage accumulation cluster had the lowest functional diversity (Fig. 4), with a predominance of metabolism, protein destination, and protein synthesis functional categories (91% of proteins). These categories were still important in mid-late accumulation and late accumulation clusters (84% and 82% of proteins, respectively), although protein synthesis was weakly represented in the latter (Fig. 4).
Within the metabolism category itself (107 proteins), we found heterogeneity in specific function distribution among clusters ( 2 9 degrees of freedom, 22.696, P 0.01). The early accumulation cluster was characterized by a predominance of proteins involved in secondary compound metabolism (34%) and energy production (glycolysis and TCA cycle, 37%; Fig. 5
). As development proceeds, maximal expression is exhibited by proteins involved in energy production (midstage accumulation cluster) then proteins involved in metabolite production, specifically amino acids, carbon skeletons, and starch (mid-late accumulation and late accumulation clusters). Proteins involved in energy production actually constituted 54% of the proteins in the midstage accumulation cluster. The TCA cycle was represented in the four clusters, with a relatively higher contribution in the early accumulation cluster, whereas the glycolytic pathway was absent from the late accumulation cluster (Fig. 5).
A remarkable feature in the second most numerous category (protein destination, 75 proteins) was the predominance of the proteins involved in degradation processes in the early accumulation cluster. This is in accordance with the disappearance of numerous functional categories from the early accumulation cluster to the midstage accumulation cluster. Zeins that have basic pIs (above 7) were out of the range of 2-D analysis, but two low-abundance storage proteins (legumin like) were detected in the 4 to 7 pH range; as expected, they were both included in the late accumulation cluster.
Accumulation Profile of Isoforms within Functional Families We examined possible coregulation between isoform accumulation within every family by computing Pearson correlation coefficients between spot volumes. In 16 families, no significant correlation appeared between isoforms (e.g. S-adenosyl-Met [Ado-Met] synthase; Fig. 6A ). Eighty-nine of 401 correlations were significant at P < 0.01. Interestingly, these correlations were most often positive (82 of 89), indicating that when coregulation of isoforms took place, it generally resulted in an increase in protein accumulation for a given time course rather than a shift or balancing of accumulation during the considered developmental period. Indeed, these two contrasted behaviors were observed for actin and tubulins (Fig. 6, B and C). Negative coregulation between the two pairs of actin isoforms resulted in an almost constant accumulation of total amount from 4 to 40 dap, whereas the total amount of tubulins steadily decreased during development.
Isoforms of functional families involved in protein folding (chaperones, chaperonins, and PDI) represented almost 20% of all isoforms. These families followed a similar trend of accumulation, with a maximum at about 14 to 21 dap (Fig. 6D); positive correlations involved two to six isoforms, according to the family, and negative correlations were observed between two isoforms of PDI and three isoforms of heat shock 90 family. Only for PPDK were the patterns of all isoforms in the family correlated to each other (see Fig. 7 , no. 9).
Most enzymes involved in carbohydrate metabolism, glycolysis, TCA cycle, and energy present several isoforms localized in different cell compartments that can be implicated in other metabolic pathways. Four software programs were used to predict the subcellular localization of the isoforms detected in our study (Supplemental Table S3) to focus on the isoforms that are likely specifically involved in glycolysis (cytosolic) and the TCA cycle (mitochondria). Thus, seven of the 10 enzymes involved in the glycolytic pathway appeared as 23 cytosolically located isoforms. Most of them did not present sharp changes in their overall accumulation profile during development, resulting in an accumulation maximum in the middle part of development (Fig. 7). Nevertheless, this general trend hides large variation at the level of a few enzyme isoforms. Due to one of the two isoforms with a maximal accumulation at midstages, 3-phosphoglycerate kinase was present at a high level at late stages. A similar trend was observed for phosphoglocomutase. Among the five enolase isoforms, four accumulated steadily during development, whereas the fifth isoform appeared at 14 dap, which is also its maximal level of accumulation stage. Six protein spots that were identified as elements of the pyruvate dehydrogenase complex (pyruvate dehydrogenase and dihydrolipoamide S-acyltransferase) displayed an accumulation profile with a marked decrease from 14 dap onward (Fig. 7, nos. 10A and 10B). Only one-half of the enzymes involved in the TCA cycle were identified as 10 protein spots. The accumulation profile of most isoforms showed a sustained level up to 14 dap, then a decrease, with the remarkable exception of one cytosolic isoform of aconitase that displayed late accumulation. ATP synthases had their maximal accumulation at early stages. Accumulation profiles of enzymes with their detected isoforms in parallel to the known metabolic pathways allowed us to propose a synthetic picture of cell machinery functioning during endosperm development (Fig. 7).
Changes in proteome complement as development proceeds provide clues on coregulation, crucial gene function at specific developmental phases, and relationships between phases. As deduced by the number of reproducible protein spots obtained at any stage that can be matched to the 14-dap endosperm 2-D pattern, the set of abundant gene products undergoes limited changes during development. However, the relative abundance of the same gene products appears to be different among the developmental stages. Actually, among the 632 polypeptides processed in the 14-dap endosperm 2-D map (Méchin et al., 2004 ), 409 were reliably quantified and matched over one or more of the seven stages, which represents the largest proteomic dataset devoted to endosperm development, as compared to other cereals. Among these proteins, 107 remained NI or had NYC function. Both NI and NYC categories probably represent a mix of all other functional categories, as can be suspected from their composite expression profile (data not shown) and scattering in a global clustering analysis (data not shown). The choice of 14 dap as the reference stage for the identification and developmental studies probably resulted in missing proteins specifically accumulating at precocious (coenocytic phase) and late (full-storage synthesis, desiccation) stages of endosperm development, but it ensures that no embryo or pericarp-specific proteins are analyzed at the youngest stages, where no dissection was done. However, for the youngest stages, a slight contribution of embryo to protein profiles cannot be definitely excluded. Nevertheless, the temporal analysis of proteomic data by the clustering method allowed four distinct patterns of accumulation—clusters—to be recognized. Besides the metabolism, protein destination, and protein synthesis functional categories that occurred in all clusters, a few categories or subcategories appeared preferentially bound to one or the other. The early accumulation cluster grouped proteins mainly involved in cellularization, detoxification, degradation, and respiration associated with energy production. As expected, the late accumulation cluster involved the main functions related to storage product synthesis and protein folding. The midstage accumulation and mid-late accumulation clusters shared functional categories (or subcategories) such as glycolysis, protein synthesis, and destination, but differed by the number and relative abundance of the involved proteins. Each cluster corresponds to sets of similarly regulated gene products, which enables discussion on coordinated functioning of cellular processes during each major developmental phase.
Cellularization, Cell Division, and Cell Wall Deposition
Sustained Accumulation of Ado-Met Synthase
Oxidative Stress Response
An important number of proteins involved in proteolysis (16 of 26) had their maximal level of accumulation at the earliest stages, most of which were components of the ubiquitin-proteasome complex (13 of 16). The function of this complex is to target and degrade specific proteins. As a first step, polyubiquitinylation of substrates is achieved through the action of three enzymes: E1, a ubiquitin-activating enzyme, E2, a ubiquitin-conjugating enzyme, and E3, a ubiquitin ligase that determines the specificity of the substrate. The polyubiquitinated protein is then processed by the 26S proteasome, which consists of a core 20S protease capped at each of its ends by a regulatory 19S complex. This degradation pathway plays an important role in various aspects of plant growth and development (Vierstra, 2003
Folding of nascent polypeptides into functional proteins is controlled by a number of molecular chaperones and protein-folding catalysts. Our analysis revealed 12 different isoforms of PDI, an endoplasmic reticulum-located protein that catalyzes the formation, isomerization, and reduction/oxidation of disulfide bonds (Houston et al., 2005
As compared to glycolysis for which 7 of 10 enzymatic functions were identified, we detected at most one-half of the enzymes involved in the TCA cycle. To check whether the pH 4 to 7 range used for protein separation could be responsible for such bias, we examined the function of proteins with a pI above 7 in Brassica napus grain filling (Hajduch et al., 2006
A striking observation is that glycolysis enzymes were grouped in the first three clusters, indicating relatively lower accumulation at the late stage, whereas the proportion of TCA enzymes is the highest in the early accumulation cluster and remains approximately constant afterward. The observed evolution of the global accumulation patterns of glycolysis enzymes is consistent with the absence of significant change in glycolytic intermediates from 12 to 42 dap as noted by Rolletschek et al. (2005)
These variations in accumulation profiles of glycolysis and TCA enzymes might be related to the heterogeneity of oxygen distribution in the developing kernel demonstrated by Rolletschek et al. (2005)
Thus, reserve synthesis takes place in the low-oxygen condition, which is likely to limit the respiration pathway to glycolysis. As highlighted by Rolletschek et al.(2005)
A main feature resulting from this proteomic analysis is the special time course of PPDK. It was nearly absent in early development, whereas its abundance is massively increased from 21 dap onward. Although this enzyme is classically involved in C4 photosynthesis, it was previously reported to be abundant in other cereal-developing kernels (Meyer et al., 1982
Thus, the PPi-ATP balance is strongly dependent on the PPDK reaction, which may suggest a third role for PPDK. In maize kernels, expression of PPDK during the late accumulation of reserve in the endosperm could be linked to the balance of starch versus protein synthesis because it provides a means to increase PPi in the cytosol. In potato (Solanum tuberosum) tubers, this was shown to favor Susy activity and subsequently starch synthesis through the AGPase located in the amyloplast. A similar hypothesis was proposed from PPDK mutants in rice (Kang et al., 2005
In addition to the possible restriction of starch accumulation, the onset of PPDK activity would favor PEP accumulation, which, together with pyruvate, plays a central role in amino acid metabolism. It should also be noted that, among the TCA cycle enzymes, aconitase increased twice at the end of endosperm development, mainly through the appearance of a late stage specific isoform. It can be assumed that aconitase operates at full rate to synthesize isocitrate, which could produce
Thus, PPDK may play a central role in storage product composition, both through the PEP-pyruvate balance and the production of PPi. Maize cytosolic PPDK is encoded by two genes, one of which (CyPPDK1) is activated by the Opaque-2 transcription factor (Maddaloni et al., 1996 The data on Opaque-2 are in favor of the role of PPDK on storage protein composition, which could be explained, in a first step, through the regulation of the relative accumulation of Ala-Phe balance. However, the suggested additional role on the starch-protein balance could reinforce the effect. This second effect could be tested by several means: comparison of PPDK accumulation in high- or low-protein lines or in recombinant inbred lines (quantitative trait loci approach), analysis of PPDK mutator insertion mutants, and search for associations between natural genetic variability of PPDK genes and seed starch and protein content in a large set of inbred lines. Actually, search for colocalization between PPDK loci and quantitative trait loci for seed protein and starch give positive results in the maize database (www.maizegdb.org). To conclude, examination of endosperm proteomic data along development in light of the physiological and biochemical function of the proteins leads to a very consistent interpretation of the observed changes and enables introduction of new testable hypotheses concerning the critical role of some processes. The importance of anoxia in understanding the observed shift of the biochemical pathways appears essential and a pivotal role of PPDK is put forward.
Sampling The Institut National de la Recherche Agronomique maize (Zea mays) inbred line F2 is becoming a reference genotype for many maize studies in Europe. F2 plants were grown in the field at Clermont-Ferrand (France, 63) in 2002. Plants were manually selfed in August. Three ears were harvested at each of the following developmental stages: 4, 7, 10, 14, 21, 30, and 40 dap to cover the main phases of endosperm development. At every stage, fertilized kernels from the middle part of each ear were sampled (Fig. 1A). Starting from 14 dap (as soon as kernels were developed sufficiently to allow easy dissection), kernels were dissected to remove the embryo and pericarp. At every stage, three samples were prepared by mixing an equal number of kernels from the three cobs. Samples were frozen in liquid nitrogen and stored at –80°C until protein extraction.
Total proteins of each of the 21 samples (seven developmental stages and three repetitions per stage) described above were extracted according to Damerval et al. (1986)
Protein resolubilization was performed according to Méchin et al. (2003) Total protein content of each of the 21 samples was evaluated using the 2-D Quant kit (Amersham Biosciences).
Isoelectric focusing (IEF) was performed using 24-cm immobilized pH gradient (IPG) strips (Amersham Biosciences). Solubilized proteins were applied on an IPG strip for in-gel rehydration. Focusing was achieved using a Protean IEF cell (Bio-Rad). Active rehydration was performed at 22°C during 12 h at 50 V; then the focusing itself was achieved. For improved sample entry, the voltage was increased step by step from 50 to 10,000 V (0.5 h at 200 V, 0.5 h at 500 V, 1 h at 1,000 V, then 10,000 V for a total of 84,000 Vh). After IEF, strips were equilibrated to improve protein transfer to the 2-D gel. The equilibrated strips were sealed at the top of the 1-mm-thick 2-D gel (24 x 24 cm) with the help of 1% low-melting agarose in SDS electrophoresis buffer (Tris 25 mM, Gly 0.2 M, and SDS 0.1%). Continuous gels (11% T, 2.67% C gels with piperazine diacrylyl as cross-linking agent) were used. Separation was carried out at 20 V for 1 h and subsequently at a maximum of 30 mA/gel, 120 V overnight, until bromphenol blue front had reached the end of the gel.
Following SDS-PAGE, gels were stained with silver nitrate, according to the procedure described in Méchin et al. (2003)
Our objective in this work was to analyze the developmental patterns of proteins involved in cell functioning and not to look at the accumulation of storage proteins. Zeins, the maize main storage proteins, progressively accumulate during endosperm development and thus gradually appear on the basic (pI > 7) side of the gels in a Mr range of 27 to 10 kD (Consoli and Damerval, 2001
A total of 632 protein spots were manually excised from the 14-dap endosperm Coomassie Blue-stained gel (Méchin et al., 2004 HPLC was performed with Ultimate liquid chromatography system combined with Famos autosample and Switchos II microcolumn switching for preconcentration (LC Packings). The sample was loaded on the column (PEPMAP C18, 5 µm, 75 µm i.d., 15 cm; LC Packings) using a preconcentration step on a micro precolumn cartridge (300 µm i.d., 5 mm). Five microliters of sample were loaded on a precolumn at 5 µL/min. After 3 min, the precolumn was connected with the separating column and the gradient was started at 200 nL/min. Buffers were 0.1% HCOOH, 3% ACN (A), and 0.1% HCOOH, 95% ACN (B). A linear gradient from 5% to 30% B for 25 min was applied. Including the regeneration step, one run was 60 min in length. The LCQ deca xp+ (Thermofinnigan) was used with a nano electrospray interface. Ionization (1.2–1.4 kV ionization potential) was performed with liquid junction and noncoated capillary probe (New Objective). Peptide ions were analyzed by the nth-dependent method as follows: (1) full MS scan (mass-to-charge ratio 500–1,500); (2) ZoomScan (scan of the two major ions with higher resolution); and (3) MS/MS of these two ions.
SEQUEST software (Thermofinnigan) was used to interpret MS/MS. Identification was performed with SEQUEST using protein sequences databases downloaded from the National Center for Biotechnology Information (NCBI; http://www.ncbi.nlm.nih.gov) and maize EST databases from Plant GDB (http://www.plantgdb.org). Peptides identified by SEQUEST were filtered according to their charge state, cross-correlation score (Xcorr > 1.7 for n + 1 and > 2.2 for n + 2), normalized difference in correlation score (
Among 409 protein spots retained for quantitative variation analysis during development, 121 were NI or considered as NYC function in our previous analysis (Méchin et al., 2004
Subcellular localization of proteins presented in Figure 7 were predicted using four different programs: TargetP (Emanuelsson et al., 2000
Each developmental stage was represented by three well-defined 2-D gels corresponding to the three independent protein extracts. For every stage, the image of the best-resolved gel was defined as a reference and the spots were automatically matched against this image; the quality of the matches was visually checked; a synthetic image was then built, which included all the spots that were present in at least two of the three repeats. The synthetic image obtained from the 14-dap stage was then used as a pivotal reference for matching against (1) the 14-dap endosperm Coomassie Blue-stained gel image used for the 2-D map (Méchin et al., 2004
A total of 504 spots could be unambiguously matched between the 14-dap stage synthetic image and the 2-D map image. For every spot, a coefficient of variation was calculated at every stage. Three classes of quantification quality were defined: A (RSD
SAS package (Procedure GLM) was used to examine properties of individual spot volumes during development. For every spot, the mean normalized volume was then computed at each stage. Missing data at intermediary stages were estimated as linear interpolation between the adjacent stages.
Hierarchical clustering analysis was performed on standardized spot volumes using Gene Cluster software (M. Eisen; http://rana.stanford.edu/software) using centered correlation and the average linkage procedure. The resulting tree was visualized using the associated TreeView software, which gives the opportunity to visualize and export lists of individuals (in our case, protein spots) belonging to specific clusters. Homogeneity of clusters for distribution of functional categories was tested by
The following materials are available in the online version of this article.
We are grateful to Dr. Michael Hodges and Pr. Graham Noctor for their helpful and critical feedback on a draft of this manuscript. We also thank two anonymous reviewers for constructive comments on an earlier version of the manuscript. Received November 7, 2006; accepted January 9, 2007; published January 19, 2007.
1 This work was supported by the European Community in the context of the Zeastar European program (QLRT–2000–00020) and by a grant within the same program (to V.M.).  The authors responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) are: Valérie Méchin (mechin{at}grignon.inra.fr) and Catherine Damerval (damerval{at}moulon.inra.fr).
[W] The online version of this article contains Web-only data.
[OA] Open Access articles can be viewed online without a subscription. www.plantphysiol.org/cgi/doi/10.1104/pp.106.092148 * Corresponding author; e-mail mechin{at}grignon.inra.fr; fax 33–1–30–81–53–73.
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Plant Physiol 131: 1104–1123 This article has been cited by other articles:
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TOP
ABSTRACT
RESULTS
-tubulin isoforms (ID, from left to right, 532, 551, 562, 561, and 560). White, Four 
-tubulin isoforms (ID, from left to right, 543, 539, 542, and 541). D, PDI, 12 isoforms (ID, from left to right, 247, 567, 597, 201, 485, 487, 486, 484, 488, 480, 481, and 482).
Cn 
0.2), and the tryptic nature of each peptide. 
