Proteomic Analysis of Pinus radiata

Proteomic Analysis of Pinus radiata Needles: 2-DE Map and Protein Identification by LC/MS/MS and Substitution-Tolerant Database Searching Luis Valledor,†,‡ Maria A. Castillejo,§,| Christof Lenz,⊥ Roberto Rodrı´guez,†,‡ Maria J. Can ˜ al,*,†,‡ ,| and Jesu ´ s Jorrıń* ´ rea de Fisiologıá Vegetal, Departamento B.O.S., Universidad de Oviedo, EPIPHYSAGE Research Group, A Oviedo, Spain, Instituto Universitario de Biotecnologıá de Asturias (IUBA), Oviedo, Spain, Proteomics Unit, Servicios Centrales de Apoyo a la Investigacioń-SCAI, Universidad de Co´rdoba, Co´rdoba, Spain, Plant Proteomics-Agricultural and Plant Biochemistry Research Group, Departamento de Bioquı´mica y Biologıá Molecular, Universidad de Co´rdoba, Co´rdoba, Spain, and Applied Biosystems Deutschland, Frankfurter Strasse 129-B, Darmstadt, Germany Received October 1, 2007

Pinus radiata is one of the most economically important forest tree species, with a worldwide production of around 370 million m3 of wood per year. Current selection of elite trees to be used in conservation and breeding programes requires the physiological and molecular characterization of available populations. To identify key proteins related to tree growth, productivity and responses to environmental factors, a proteomic approach is being utilized. In this paper, we present the first report of the 2-DE protein reference map of physiologically mature P. radiata needles, as a basis for subsequent differential expression proteomic studies related to growth, development, biomass production and responses to stresses. After TCA/acetone protein extraction of needle tissue, 549 ( 21 well-resolved spots were detected in Coommassie-stained gels within the 5-8 pH and 10-100 kDa Mr ranges. The analytical and biological variance determined for 450 spots were of 31 and 42%, respectively. After LC/MS/MS analysis of in-gel tryptic digested spots, proteins were identified by using the novel Paragon algorithm that tolerates amino acid substitution in the first-pass search. It allowed the confident identification of 115 out of the 150 protein spots subjected to MS, quite unusual high percentage for a poor sequence database, as is the case of P. radiata. Proteins were classified into 12 or 18 groups based on their corresponding cell component or biological process/pathway categories, respectively. Carbohydrate metabolism and photosynthetic enzymes predominate in the 2-DE protein profile of P. radiata needles. Keywords: Needle proteome • Pinus radiata proteomics • 2-DE variability

Introduction Pinus radiata D. Don is one of the most economically important forest tree species in the world, characterized by its fast growth and permissive soil requirements. Forest plantations of P. radiata in New Zealand, Australia, Chile, and Spain produce about 370 million m3 of wood per year.1 Current selection of elite trees in conservation and breeding programs * To whom correspondence should be addressed. Dr. Jesu ´ s Jorrıń, Departamento de Bioquı´mica y Biologıá Molecular, Universidad de Co´rdoba, Campus de Rabanales, Ed. Severo Ochoa, E-14071 Co´rdoba, Spain. E-mail, [email protected]; fax, +34 957 218592. Dr. Maria Jesu ´ s Can ˜ al, Departamento ´ rea de Fisiologıá Vegetal, Universidad de Biologıá de Organismos y Sistemas, A de Oviedo, E-33071, Oviedo, Spain. E-mail, [email protected]; fax, +34 985 104867. † Departamento B.O.S., Universidad de Oviedo. ‡ Instituto Universitario de Biotecnologıá de Asturias (IUBA). § Servicios Centrales de Apoyo a la Investigacioń-SCAI, Universidad de Co´rdoba. | Departamento de Bioquı´mica y Biologıá Molecular, Universidad de Co´rdoba. ⊥ Applied Biosystems Deutschland.

2616 Journal of Proteome Research 2008, 7, 2616–2631 Published on Web 06/05/2008

requires the use of molecular markers related to growth and productivity, as well as to responses to environmental factors. Forest productivity depends on the growth capacity of the tree, which is directly related to carbon fixation rates and energy production in needles.2 Despite their generality and potential implications, the ultimate causes and mechanisms that determine needle activity-forest productivity are still poorly understood.3 The characterization of the proteins present in the needles will provide a basis for future studies to clarify these processes and to assist in the selection and breeding of specific tree genotypes. The selection of P. radiata populations has so far been based on genetic characters4,5 and physiological markers.6 These approaches are useful for establishing pedigrees or selecting individuals based on characterized Quantitative Trait Loci (QTL). The use of protein markers in breeding programs could be very useful for improving selection results. In this respect, proteomics is becoming a powerful technology, successfully used in plant research to investigate different biological processes, from growth and development to responses to biotic 10.1021/pr7006285 CCC: $40.75

 2008 American Chemical Society

2-DE Proteome of Pinus radiata Needle or abiotic stimuli, as well as to understand gene function, characterize particular genotypes, and analyze food traceability and substantial equivalence in transgenic crops.7–12 Most of the proteomic studies have been carried out with herbaceous plant species (models Arabidopsis thaliana and crop rice). In contrast, the number of so far published papers on forest tree proteomics is almost anecdotic.11 This could be related to their difficulty as an experimental system and to the low number of genomic DNA, Expressed Sequence Tags (ESTs) or protein sequences available in public databases. Proteomic approaches have been previously reported in Quercus ilex,12,13 poplar,14,15 Picea spp.16,17 and P. radiata-related maritime pine.18,19 Most of the effort in the Pinus genre has been pointed to QTL mapping20,21 study of specific genes/proteins related to wood formation18,19,22 and vegetative growth.23,24 Actually, there are not any available genomes of Pinus genre, but Loblolly and White pine are in process under the Conifer Genome Network (http://pinegenome.org/), and some ESTs collections are available. A total of 359 503 Pinus ESTs are found in databases (dbEST, NCBI). With respect to P. radiata, only 164 EST entries are found (Nucleotide Blast and dbEST, NCBI/ BLASTN Taxa Pinaceae, Pinus), mostly obtained from cDNAAFLPs, but most of them are unannotated. Only 158 nonredundant protein sequences are deposited in public databases (Protein Blast, NCBI/BLASTP Taxa Pinaceae, Pinus) (date of search 11/20/07). In this paper, we present the first report of the 2-DE protein reference map of physiologically mature P. radiata needles, as a basis for subsequent differential expression proteomic studies related to characterizing and identifying proteins related to genotypes, plant growth, development, biomass production and responses to stresses. For such a comparative study, it is necessary to determine the average analytical and biological variability inherent to the methodology and the system employed using high number of replicates. Between 550 and 600 well-resolved spots were detected in Coomassie-stained gels within the 5-8 pH and 10-100 kDa Mr ranges. The analytical and biological variability, determined for a set of representative 450 different spots, was of, repectively, 31 and 42%. The scarcity of tree protein sequence entries in the available databases, including Pinus radiata, limits the identification of proteins, for example, by mass spectra. In this work, we used a liquid chromatography-tandem mass spectrometry analysis on ingel protein digests, and the commercial Protein Pilot software containing the novel Paragon algorithm, developed by Applied Biosystems. This software tolerates amino acid substitutions, and efficiently consolidates partly redundant protein entries from multiple related tree species matched under these circumstances. Out of the 150 spots subjected to LC-MS/MS analysis, 100 unique, nonredundant proteins were identified, showing the convenience of protein hybrid identification approaches as compared with PMF, MS/MS or de novo sequencing strategies.

Material and Methods Plant Material. Mature needles (12 months old) were taken from P. radiata plantations at La Reigada (Asturias, Spain; 43°26′46′′N, 6°00′42′′W elevation 500 m) (Supplementary Figure 1). Needles were collected from 12 different trees from a homogeneous population (La Reigada) during spring outgrowth, washed with tap water, dried with filter paper, and then frozen in liquid nitrogen. Samples were stored at -80 °C until protein extraction was done.

research articles Protein Extraction. Needles (2 g fresh weight per sample) were ground to a fine powder with liquid nitrogen using a mortar. The powder was suspended in 8 mL of 10% (w/v) trichloroacetic acid (TCA)/acetone solution containing 0.07% (w/v) dithiothreitol (DTT). The mixture was filtered through Miracloth (pore size of 25 µm) to eliminate cell debris, and the filtrate was sonicated 2 × 30 s (6 W). Proteins were allowed to precipitate at -20 °C for 1 h; and the precipitate was recovered after centrifugation at 35 000g for 30 min. The pellet was cleaned with 8 mL of cold (-20 °C) acetone containing 0.07% (w/v) DTT and sonicated twice more, 30 s each, keeping the extract at -20 °C for 30 min, and then centrifuged at 20 000g. The cleaning process was repeated once. The final pellet was air-dried and solubilized in 400 µL of 8 M urea, 2% (w/v) 3-[(3cholamidopropyl)dimethylammonio]propanesulfonate (CHAPS), 20 mM DTT, 0.5% (v/v) Biolytes pH range 3-10 (Bio-Rad), and 0.0001% (w/v) bromophenol blue. Insoluble material was removed by centrifugation at 20 000g for 15 min. The protein concentration was determined using the RC-DC Protein Assay (Bio-Rad), with ovalbumin as a standard, according to the instruction kit. Samples were stored at -80 °C until isoelectrofocusing (IEF). 2-DE. Immobilized pH gradients (IPG) strips employed in small analytical gels (7 cm, 3-10 pH gradient; Bio-Rad) were passively rehydrated for 12 h with 150 µg of protein in 125 µL of IEF solubilization buffer. Large IPG strips (17 cm, 5-8 pH linear gradient; Bio-Rad) were passively rehydrated for 16 h with 500 µg of protein in 300 µL of IEF solubilization buffer. The strips were loaded onto a Bio-Rad Protean IEF Cell system and proteins were electrofocused at 20 °C first using a gradually increasing voltage (250-4000 V) and then reaching 10 000 Vh for 7 cm IPG strips. Seventeen centimeter strips were electrofocused with a first step of a gradual increase in the voltage (250-10 000 V) and then reaching 40 000 Vh. Strips were immediately equilibrated according to Go¨rg et al.26 Seconddimension SDS-PAGE was performed on 13% polyacrylamide gels using the Miniprotean (7 cm IPG strips) or Protean Dodeca Cell (17 cm IPG strips) systems both from Bio-Rad. Gels were run at 80 V until the dye reached the bottom of the gel. Staining and Image Analysis. Gels were stained twice with CBB G-250 (Bio-Rad) for 20 h following the method described by Mathesius et al.27 Images were acquired with a GS-800 calibrated densitometer (Bio-Rad) and analyzed with PDQuest 7.1 software (Bio-Rad) using 10-fold over background as a minimum criterion for presence/absence for the guided protein spot detection method. Spot-by-spot visual validation of automated analysis was done thereafter to increase the reliability of the matching.28 Normalized spot volumes (individual spot intensity/normalization factor) calculated for each gel based on total quantity in valid spots were determined and used for statistical calculations of protein expression levels. Experimental pI was determined using a 5-8 linear scale over the total length of the IPG strip. Mr values were calculated by mobility comparisons with protein standards markers (SDS Molecular weight standards, Broad range, Bio-Rad) run in a separate lane in the gel. Protein spot quantification was performed using a calibration curve method. Spot volumes were linear to protein quantity over the range from 30 to 3600 ng (Y ) 85.192X, r2 ) 0.9982; where Y is the volume and X the amount of protein in ng).12 Experiment Design: Analytical and Biological Variance. Analytical and biological variance was calculated from 450 spots. In the first case, 10 independent protein extractions and Journal of Proteome Research • Vol. 7, No. 7, 2008 2617

research articles 2-DE gels from needles of the same length taken from the same branch of one tree were carried out. For the biological variance determination, 12 independent protein extractions and 2-DE gels from needles of 12 different trees were performed. IPG strips from each analysis were simultaneously focused, and the second-dimension run in a batch in a Dodeca Cell (Bio-Rad), striving to keep the variation between batches at a minimum. All gels were scanned immediately after CBB-250 destaining to minimize any possibility of fading. Mean values of spot quantity, as well as SD and CV were determined for each spot. In Situ Digestion of Proteins and LC-MS/MS Analysis. Stained protein spots were automatically excised from the gel employing Investigator ProPic robotic workstation (Genomic Solutions) and destained by two washes at 37 °C for 30 min with 100 µL of 100 mM ammonium bicarbonate/50% (v/v) acetonitrile (ACN). Gel spots were washed twice in 20 µL of 25 mM ammonium bicarbonate and then dehydrated with 20 µL of 25 mM ammonium bicarbonate/50% (v/v) ACN followed by a wash with 20 µL of ACN. Gel pieces were fully dried in a SpeedVac. For digestion, the gel pieces were rehydrated in 20 µL of 25 mM ammonium bicarbonate solution containing 12.5 ng/µL trypsin (sequencing grade, Promega) and incubated on ice for 45 min. The supernatant was discarded, 10 µL of 25 mM ammonium bicarbonate was added to the gel, and then, this was heated for 2 × 5 min in a microwave oven at 200 W. The peptides were extracted in 0.5 µL of 10% trifluoroacetic acid (TFA), with frequent vortexing for 15 min. Samples were evaporated to dryness and stored at 4 °C until MS analysis. Samples were reconstituted into 20 µL of loading buffer (2% ACN (v/v) vs 0.5% (v/v) formic acid) and analyzed by liquid chromatography/tandem mass spectrometry. Digest peptides were concentrated and desalted on a C18 trap column (PepMap C18, Dionex) using a Tempo 1D nanoLC system (Applied Biosystems). Peptide separation was achieved on a reversed phase C18 column (PepMap C18, 75 µm i.d., 15 cm) using an 18 min linear gradient of 5-35% (v/v) ACN versus 0.1% (v/v) aqueous formic acid. The eluent was analyzed on hybrid triple quadrupole/linear ion trap mass spectrometer (4000 Q TRAP LC/MS/MS System, Applied Biosystems) equipped with a heated Desolvation Chamber Interface set to 150 °C and operated under Analyst 1.4.1 software. Up to five peptide precursor ions detected by a linear ion trap MS scan were first subjected to a high resolution MS scan to determine charge state and molecular weight. Suitable precursors were then fragmented by Enhanced Product Ion Scans (EPI).29 In this scan mode, precursors are selected in Q1, fragmented by collision with nitrogen in the Q2 collision cell, and mass analyzed in the Q3 linear ion trap. The collision energy was dynamically adjusted according to the charge state and Mr of the precursors. The resulting spectra have been shown to be suitable for de novo sequence analysis.12 The average cycle time for this experiment was 3.5 s. Identification of Proteins from LC/MS/MS Data. Because of the poor protein and DNA sequence database coverage for Pinus, proteins were identified from the LC/MS/MS data using a novel approach that employs the recently introduced Paragon algorithm present in the commercial ProteinPilot software (Applied Biosystems).25 In short, Paragon derives short sequence tags from all MS/MS spectra in an LC/MS/MS experiment. The density of these sequence tags on a given stretch of protein sequence in the database is then graded using the novel concept of Sequence Temperature Values (STVs). Sequence stretches with a high STV, that is, a high density of sequence 2618

Journal of Proteome Research • Vol. 7, No. 7, 2008

Valledor et al. tags assigned, are then used for an exhaustive search for possible peptide sequences, including variable modifications, seemingly nonenzymatic cleavages and amino acid substitutions. For each spot, the list of detected proteins was then consolidated using the ProGroup algorithm incorporated in the ProteinPilot software. The ProGroup algorithm consolidates and visualizes redundancy of evidence between different protein sequence entries matched to the same set of peptide sequences. A protein hit is only reported if it has at least one unique, high scoring peptide sequence assigned to it that is not implemented in other protein hits. This approach efficiently consolidates the large number of similar protein entries from different species expected in the given scenario, without obscuring valuable information about the existence of potential homologues.

Results and Discussion 2-DE Map, Analytical and Biological Variability. The P. radiata needle 2-DE reference map was established using proteins extracted from needles of 12 individuals. We know that only a limited fraction of the whole proteome, determined by the employed TCA-acetone precipitation extraction protocol30 and the 2-DE separation technique,31 can be studied. Small analytical, Coomassie-stained 2-D gels (7 cm) revealed that most of the spots were concentrated in the 5-8 pH and 10-100 kDa Mr ranges (Supplementary Figures 1 and 2), a pattern typically observed in leaf tissue from other plants.12 26 Following CBB staining of the preparative gels (17 cm), 532 ( 25 (analytical set) and 549 ( 21 (biological set) protein spots were detected by digital image analysis and later visual spotby-spot validation of the match (Figure 1a). Spot volumes were determined for each 2-DE gel and then translated into protein quantity by using a calibration curve obtained with different amounts of protein standards.12 The different protein spots are in the range amount of 50-4500 ng, which are within the detection limits and dynamic range reported for Coomassie staining.32 The total amount of the whole set of spots gave values of 138 µg, which represents 27.6% of the loaded proteins (500 µg), as determined by the RC-DC Protein Assay. 2-DE has been criticized for its low reproducibility when a direct comparison of different gels is performed.33–35 These kinds of comparisons should only be performed after the definition of analytical and biological variations, which support gel-to-gel spot comparison statistics.12 The origin of the analytical variation is related both to experiment procedures (protein extraction, IEF, SDS-PAGE, gel staining-destaining)33–35 and hardware/software accuracy (image acquisition and analysis),36,37 contributing to the variation in gel patterns and spot quantification. Biological variation is caused, among other factors, by environments and microenvironments. For the determination of the analytical variance, 10 independent protein extracts from homogeneous needles of the same branch of one tree were used, while for the biological variations, 12 different protein extracts, each one from a different tree, were used. Appropriate design of both the experimental and statistical aspects of a proteomics study is a prerequisite for establishing significant differences in the protein map between samples corresponding to different genotypes or populations, developmental stages, or environmental conditions. In our study, and as previously modeled,39 differences in the SE (standard error) were found depending on the number of replicates, from 111 and 115 ng (two analytical and biological replicates) to 58 and 59 ng (10 analytical and 12 biological gel replicates) (Figure 2).

2-DE Proteome of Pinus radiata Needle

research articles

Figure 1. 2-DE P. radiata needle proteome map. A real (a) and a master (b) gels are shown. The relative Mr is given on the left, while the pI is given as the top of the figure. A total of 150 numbered spots were chosen for protein identification. Journal of Proteome Research • Vol. 7, No. 7, 2008 2619

research articles

Figure 2. Effect of the number of replicates on the standard error of mean spot quantity estimation after 2-10 analytical replicates and 2-12 biological replicates.

Figure 3. Distribution of the coefficient of variation of 450 selected spots.

In view of this, and for future comparative proteomic studies, seven biological replicates are proposed. Spot amount mean values and SD, as well as the CVs of 450 independent spots were determined for both the analytical procedure and the biological system (Supporting Information Table S1). These spots were chosen with the aim of obtaining a representative set of the gel. This data set covers a wide range of pI (5-8), Mr (10-100 kDa), and protein amount from 34 ng. The CVs for each protein spot were then averaged to give a cumulative CV of the analytical or biological data set. The distribution of CVs follows different patterns between analytical and biological variance data sets (Figure 3). These patterns can be explained as being the result of the addiction of biological to analytical variance, displacing the main distribution range from 0-25% to 25-50%. The average CV of the 450 quantified spots was determined to be 31% for the analytical and 42% for the biological variance. These values are similar to those reported for other tree species like those in holm oak seedlings from the same provenance, with 29% for the analytical and 44% for the biological variance,13 but slightly higher than those reported by Asirvatham et al.38 in greenhouse-grown Medicago truncatula (analytical and biological CVs of 16%, which are replicates of the same protein extract, and 24% respectively). Compared to M. truncatula, a higher CV value is not striking, given the fact that our study used mature trees and field 2620


Valledor et al. samples, whereas the M. truncatula plants were grown under controlled conditions. With regard to the variable spots between individuals, those with biological CV significantly higher than those corresponding to the analytical CV, were analyzed. Twenty-one percent of spots were found to be variable among the 12 sampled genotypes. Costa et al.18 revealed that 31% of spots were significantly different after analyzing needles of three different genotypes of maritime pine, being the observed variation lower than previously reported in other forest trees. The relationship between the amount of protein spots and their quantitative variability was also investigated. A significant positive correlation (P < 0.01) was found when computing the linear correlation between the protein amount and SD (r ) 0.774) (Figure 4a). When analyzing the relation between CV and protein amount, pI or MW, no significant correlation was found (Figure 4b, Supporting Information Figure S3c-f) Similar results have been reported in previous studies with maize or holm oak,40,12 indicating, first, that abundant proteins have a higher range of variation (SD), and, second, the range of relative variation (CV) does not depend on their abundance. The relation larger spot-larger variance has been previously observed, and can be explained due to a scalar phenomenon, well-known when the spot abundance is a count (number of pixels × intensity).41 The position of proteins on the gel, defined by a particular molecular weight and pI, is not related to an increased variability. Identification of Proteins. Pinus needle genomes and proteomes have not been extensively characterized. The major scientific advances in these areas are mainly focused on wood formation or stress-related situations.18,19,42 We have randomly selected 150 spots (Figure 1b, Table 1), present in all replicates, to determine its identities. Under these circumstances, protein sequence entries for similar proteins expressed in closely related species can be used, provided that a reasonable amount of sequence homology by amino acid substitution or deletion can be tolerated. Under these circumstances analysis of LC/MS/MS data obtained from enzymatic protein digests by de novo sequencing and BLAST similarity searching of the resulting candidate sequences43 is used. This approach has been established and validated for forest tree species such as holm oak (Q. ilex).12,13 It is limited, however, by the scoring algorithm employed to evaluate the BLAST results for multiple candidate sequences derived from multiple MS/MS spectra in an LC/MS/MS experiment. In spite of improvements,44 redundancy, both on the side of the candidate sequences and on the side of the protein sequences used, is detrimental to the scoring of matches. The Paragon algorithm employed in this study at “Thorough” settings takes a different approach. The initial step of pairing MS/ MS data to sequence is achieved by generating short sequence tags de novo. If a number of sequence tags can be matched to a given protein sequence stretch in the database, then it is assigned a high Sequence Temperature Value (STV), and a large number of hypothetical peptide sequence around this stretch is evaluated. The equally important step of scoring the derived peptide sequence hypotheses, however, is not achieved by BLAST similarity searching, but by matching observed fragmentation patters against theoretically predicted fragmentation patterns.25 This approach to scoring peptide hits is largely independent of the number of MS/MS spectra or the number of potential peptide/ protein sequences used and, as such, is much more robust in a scenario that requires a high degree of feature variability on the peptide sequences detected. Figure 5 shows an example from spot

research articles


Figure 4. Analysis of the quantitative variation. Relationship between mean of protein amount and standard deviation of needle samples (a) and coefficient of variation of protein amount (b).

38, where the sequence detected by the Paragon algorithm is not present in the protein sequence database used. Two similar entries from A. thaliana and Phaseolus vulgaris can be associated with the sequence detected by the substitution of two and three amino acids, respectively. Another challenge to identifying proteins versus databases with entries from related species is the occurrence of multiple similar protein hits reported. As the identity of the inferred protein sequence and the database sequence can no longer be assumed, many more similar protein sequence entries from different species containing slight differences from the inferred sequence have to be considered, often significantly increasing the number of potential protein hits assigned. To solve this dilemma, the ProGroup algorithm that is built into the ProteinPilot software was used. ProGroup uses the concept of the “Unused Score” to evaluate the degree of redundancy between different protein entries matched to the same set of peptide-MS/MS evidence. If an additional matched protein sequence is not evidenced by a hitherto unused, unique peptide sequence, then its “Unused Score” is small. As a consequence, it is not reported as a unique protein, but is folded into a protein group together with other redundant entries. A protein entry that is substantiated by hitherto unused, unique peptide evidence is assigned a significant “Unused Score”. Contrary to the first entry, it is reported as a separate protein group. Figure 6 again shows an example from spot 38. The two protein sequence entries from A. thaliana and P. vulgaris can be matched to both shared and unique evidence, respectively. As a result, both are assigned significant “Unused Scores”, and are listed as separate entries in the ProteinPilot results list to show that there is unique evidence pointing to each protein entry. It should be noted, though, that this does not necessarily indicate the presence of two Hsp70 isoforms in spot 38. A simpler and probably more correct explanation is that the actual Hsp70 sequence from Pinus present in spot 38 is a compound that shows a similarity to both database entries. In the final results list in Table 2, redundant protein sequence entries corresponding to the same functional protein from different species have been collapsed to the highest scoring entry for the sake of clarity. Complete identification results (theoretical and experimental Mrs and pIs, identified peptide sequences and scores) are presented in Supplementary Table S2. A total of 150 spots, corresponding to the set in which variation was studied, were subjected to LC-MS/MS analysis to determine their identities (Table 2). The overall identification success rate was 77%, corresponding to 115 spots. It should be noticed that in 25 spots more than one protein was

identified. This comigration of proteins is possible if both present the same experimental pI and Mr. A total of 100 nonredundant proteins were identified. In some cases the proteins identified could be mostly degradation products, according to the difference between experimental and theoretical Mrs and pIs, as in the case of RubisCO (13 spots) or RubisCO activase (6 spots). This was previously observed in other species like Q. ilex, Picea glauca and P. pinaster.12,13,16,19 In other cases, these differences may be caused by post-translational modifications, different isoforms derived from different genes of a multigene family, or artifactual chemical modifications of proteins, such as carbamoylation during sample preparation.45 Ontological Classification of Identified Proteins. The first step toward interpreting obtained proteomic expression data is to group proteins. Proteins were classified into 12 or 18 groups based on corresponding cell component or biological process categories, respectively. Interestingly, and according to their location (Figure 7a), 77 of the 100 nonredundant protein entities correspond to four cell components: chloroplast (28 proteins), cytosol (15 proteins), intracellular (20 proteins), and mitochondrion (14 proteins), as it corresponds to fully developed and photosynthetically active needles. Ninety-two proteins corresponded to enzymes related to photoshynthesis, glycolysis, pyruvate pathways and tricarboxylic acid cycle (Figure 7b), 13 to protein translation, folding and degradation, 9 to nitrogen fixation and amino acid metabolism, and 15 to the stress-related and RedOx maintenance groups (Table 2 and Supplementary Figures S4-S18). The large subunit of RubisCO, present in 13 spots, was the major protein of the subproteome. The Mr and pI values of observed spots varied greatly and were very different from the value reported for this enzyme (52,76 kDa), maybe as the result of RubisCO proteolysis18 or the sample preparation process. Other proteins involved in photosynthesis were also observed including, among others, RubisCO activase, Glyceraldehyde-3-phosphate dehydrogenase, RubisCO small subunit, and so forth. (Table 2; Supporting Information Figures S4 and 5). These proteins are also present in other forest trees, constituting the major group of green tissues.12,18,19

Conclusions A 2-DE reference map of P. radiata needle is provided. This map which covers pI ranges from 5 to 8, and 10-100 kDa Mr contains around 550 spots. An analysis of the analytical and Journal of Proteome Research • Vol. 7, No. 7, 2008 2621

research articles

Valledor et al.

Table 1. Analysis of Variance in the Level of Protein Expression among Needle Samples from the Same Tree (analytical variance) or Different Trees (biological variance) of 150 Spots Subjected to MS Identificationa analytical variance spot

exp.Mr (kDa)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74

25.95 28.37 29.27 27.19 33.44 33.75 30.26 32.26 38.08 37.13 40.32 42.79 46.27 47.26 56.47 60.63 67.24 78.65 67.56 77.19 67.61 17.58 19.23 12.91 18.31 24.88 31.00 34.01 35.02 34.84 36.57 43.26 51.11 50.41 62.38 62.61 77.18 74.31 71.28 22.06 13.68 14.83 16.10 13.62 24.82 26.01 32.47 32.58 31.12 42.52 43.42 47.68 44.34 44.86 44.19 50.62 50.88 62.28 22.04 29.71 28.63 27.70 34.23 30.31 31.81 30.33 37.69 43.12 47.38 48.31 58.23 49.27 58.13 65.59

2622

biological variance

Exp. pI

average spot amount (ng)

SD of spot amount (ng)

CV for spot amount (ng)



CV for spot amount

5.28 5.46 5.52 5.57 5.45 5.53 5.56 5.56 5.48 5.61 5.45 5.50 5.50 5.54 5.51 5.59 5.28 5.34 5.56 5.57 5.62 5.61 5.68 5.72 5.77 5.76 5.62 5.65 5.68 5.77 5.79 5.64 5.67 5.75 5.71 5.77 5.66 5.74 5.78 5.84 6.01 6.05 6.06 6.11 5.91 5.92 5.93 6.03 6.05 5.83 6.08 5.82 5.95 5.98 6.10 5.84 5.96 5.83 6.21 6.16 6.29 6.33 6.23 6.27 6.29 6.37 6.16 6.25 6.15 6.24 6.19 6.21 6.38 6.20

529 486 86 76 1212 265 348 3078 272 143 1385 75 68 107 41 581 718 810 102 269 299 434 34 85 363 67 2599 129 172 140 145 546 786 3569 353 141 215 191 215 114 25 276 295 417 4356 529 486 91 121 406 276 318 1147 798 247 818 85 1073 2324 246 89 154 41 90 138 51 49 183 205 297 425 142 429 163

143 76 19 8 239 81 93 440 73 19 199 9 16 19 3 103 157 427 21 44 38 440 16 26 86 15 390 15 48 28 32 40 113 523 46 19 26 22 24 21 8 56 54 182 3122 25 27 0.02 0.02 0.05 0.03 0.05 0.17 0.14 0.03 0.11 0.03 0.13 0.69 0.03 0.01 0.03 0.01 0.02 0.02 0.02 0.01 0.04 0.04 0.07 0.08 0.03 0.07 0.03

26.99 15.59 22.39 11.13 19.71 30.52 26.63 14.31 26.68 13.63 14.34 11.32 23.43 17.33 7.13 17.64 21.92 52.71 20.13 16.53 12.83 101.46 46.05 30.87 23.80 22.93 15.02 11.93 27.87 20.14 21.95 7.31 14.41 14.66 13.06 13.26 11.90 11.63 11.41 18.65 31.54 20.37 18.21 43.51 71.67 16.01 31.32 21.26 15.41 11.25 11.10 16.87 14.55 17.89 13.03 13.60 40.25 12.20 29.68 13.29 14.63 17.19 36.09 21.62 17.83 29.35 18.67 20.06 20.31 23.58 19.93 17.82 15.34 26.99

205 428 81 58 637 760 179 2534 191 103 1053 116 49 76 34 230 370 282 114 138 181 435 86 163 190 75 878 104 136 199 170 280 446 1801 225 158 119 102 104 124 111 410 292 668 5050 183 78 78 98 120 243 131 82 601 504 346 1107 349 604 1470 227 38 142 53 85 102 71 38 96 105 94 263 99 197

60 59 14 24 260 557 88 402 47 14 205 25 9 12 5 61 69 127 39 20 27 199 37 29 54 30 893 22 35 36 63 27 127 319 45 25 15 17 24 35 62 43 44 130 1467 44 8 8 24 23 34 28 37 92 69 187 555 242 95 198 30 16 19 19 23 40 31 12 23 28 26 29 21 62

29.40 13.82 17.76 41.54 40.82 73.28 49.27 15.85 24.54 13.86 19.47 21.47 18.17 15.67 13.60 26.62 18.72 44.99 34.13 14.40 14.74 45.83 42.65 18.01 28.25 39.78 101.71 21.14 25.37 18.13 36.89 9.66 28.46 17.71 20.22 16.10 12.94 16.33 23.23 27.95 55.72 10.44 14.95 19.52 29.05 23.93 10.79 24.14 18.88 13.86 21.36 44.88 15.27 13.76 54.12 50.09 69.42 15.65 13.48 13.13 42.69 13.25 36.50 26.78 39.36 44.15 31.87 23.83 27.12 28.10 11.20 21.39 31.61 24.32


research articles

2-DE Proteome of Pinus radiata Needle Table 1. Continued analytical variance spot

exp.Mr (kDa)

75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150

65.36 72.43 71.45 21.49 13.46 13.64 24.60 31.60 37.82 38.88 34.70 41.22 40.80 45.72 43.65 45.17 43.77 45.98 56.86 72.24 78.52 26.17 28.17 26.56 28.74 30.76 32.97 39.36 40.10 37.54 41.03 43.31 51.59 54.40 47.59 60.99 61.10 60.13 65.46 18.92 20.47 14.13 14.16 28.54 25.20 28.74 25.81 32.98 32.62 39.30 35.68 40.28 42.34 44.33 43.75 43.66 51.48 49.76 63.80 61.19 30.80 30.88 37.33 38.49 35.75 38.27 37.19 41.50 40.78 41.29 41.23 43.91 44.09 51.58 61.47 61.47

biological variance

Exp. pI



CV for spot amount (ng)



CV for spot amount

6.32 6.30 6.38 6.48 6.59 6.51 6.63 6.49 6.50 6.54 6.61 6.45 6.63 6.46 6.48 6.53 6.53 6.59 6.51 6.63 6.64 6.72 6.77 6.82 6.85 6.69 6.93 6.68 6.78 6.83 6.80 6.69 6.67 6.83 6.86 6.68 6.73 6.79 6.69 6.95 6.95 7.26 7.05 7.22 7.23 7.16 7.06 6.98 7.07 6.96 7.21 7.21 6.95 7.01 7.04 7.23 7.17 7.22 7.01 7.10 7.35 6.65 7.30 7.41 7.55 7.61 6.67 7.38 7.50 7.57 6.69 7.39 7.48 7.28 7.57 7.60

65 198 46 66 11895 2374 3068 46 122 64 38 83 174 144 98 80 129 68 168 81 80 1068 411 166 540 974 383 84 549 58 595 2072 105 114 500 1444 1081 3685 46 774 121 5501 80 1097 92 105 96 117 111 176 1022 817 530 529 187 53 50 66 106 377 80 139 433 71 378 762 113 102 104 144 599 85 129 42 396 258

0.01 0.03 0.01 0.02 2.03 0.44 0.32 0.02 0.01 0.01 0.02 0.02 0.03 0.02 0.02 0.02 0.02 0.01 0.02 0.02 23 852 65 34 52 96 190 66 200 10 49 211 30 16 96 644 562 1353 5 101 32 534 41 143 20 20 19 91 89 23 114 64 150 89 31 8 14 34 15 67 17 89 74 19 75 738 43 14 12 46 239 9 22 8 58 56

15.59 16.02 15.86 34.91 17.10 18.61 10.53 51.33 7.54 10.24 59.14 21.57 17.61 11.97 19.06 25.58 18.38 14.26 13.74 20.28 29.08 79.74 15.76 20.32 9.71 9.88 49.64 78.89 36.37 17.76 8.23 10.17 28.07 13.81 19.25 44.61 52.01 36.71 11.12 13.07 26.09 9.70 51.37 13.07 21.59 18.94 20.13 77.67 79.50 13.26 11.20 7.82 28.31 16.88 16.83 14.41 28.97 50.63 13.87 17.84 20.70 64.20 17.17 27.01 19.86 96.94 37.96 13.90 11.96 32.15 39.94 10.16 16.98 18.53 14.67 21.80

84 42 106 49 98 8112 734 2225 116 84 67 49 78 110 72 68 79 277 53 92 49 1383 270 177 297 715 462 71 317 32 497 816 55 65 250 636 511 1302 26 815 165 1572 69 505 39 26 38 246 291 110 667 520 151 251 57 37 84 58 74 235 92 223 260 68 387 290 66 76 106 138 324 74 76 44 224 220

21 7 22 17 36 1281 439 313 40 13 18 31 22 30 25 31 10 189 14 17 16 418 35 51 83 71 158 20 121 10 63 376 11 10 113 140 190 580 11 142 56 386 64 112 45 25 23 93 72 16 100 129 60 135 53 19 44 10 13 53 29 64 35 17 99 135 23 21 41 59 63 14 29 17 69 81

16.15 20.75 34.07 36.94 15.79 59.88 14.05 34.51 15.91 26.25 62.54 28.49 27.47 34.52 45.36 12.28 68.51 25.62 18.89 19.53 32.25 30.24 12.92 29.13 27.90 9.99 34.12 27.46 38.25 32.26 12.60 46.13 20.74 15.78 45.27 21.97 37.25 44.55 41.17 17.46 33.99 24.56 92.51 22.19 116.04 96.49 61.36 37.63 24.55 14.70 15.06 24.84 39.83 53.67 93.03 51.04 52.25 17.36 17.85 22.48 31.01 28.68 13.40 24.55 25.48 46.39 35.73 27.69 38.43 43.07 19.39 19.42 38.11 39.03 30.75 36.96

a Average value of protein amount calculated from average normalized protein spot volume of biological variance analysis. Spot number corresponds to those indicated in Figure 1.

biological variance was performed to ensure the reproducibility of the observations, and to establish the statistical bases of ulterior comparative proteomic studies. Variance for individual spots ranged from 7 to 100%, with average values of 31%

(analytical) and 42% (biological). The modeling of the SE of each spot, as recent reported with theoretical data,39 can be used to propose the number of seven biological replicates for future comparative proteomic studies. Journal of Proteome Research • Vol. 7, No. 7, 2008 2623

research articles

Valledor et al.

Figure 5. MS/MS spectrum of m/z 649.342+ recorded from spot 38. The detected sequence NAADSTIYSVEK can be matched by amino acid substitution to both trm Q9SZJ3 from A. thaliana N(S>A)AD(T>S)TIYSVEK and to sp Q01899 from P. vulgaris N(S>A)AD(T>S)TIYS(I>V)EK.

Figure 6. Venn diagram showing the shared sequence evidence between trm Q9SZJ3 from A. thaliana and sp Q01899 from P. vulgaris in spot 38. The annotated sequences detected by the Paragon algorithm approximate the actual Pinus sequence present in the sample.

The commercial Protein Pilot software containing the new Paragon algorithm developed by Applied Biosystems was 2624


used; it uses the recently introduced concept of Sequence Temperature Values (STVs), and tolerates sequence homol-

54

97

95

87

60

109

72

18, 19, 21

33, 34, 51, 54, 55, 56

Phosphoribulokinase. chloroplast precursor (EC 2.7.1.19)

Triosephosphate isomerase, chloroplast precursor (EC 5.3.1.1) Fructose-bisphosphate aldolase. chloroplast precursor (EC 4.1.2.13) Transketolase 1 (EC 2.2.1.1) Ribulose-5-phosphate-3epimerase (EC 5.1.3.1)

Ribulose-1.5-bisphosphate carboxylase large subunit, precursor (EC 4.1.1.39) Ribulose bisphosphate carboxylase small chain, precursor (EC 4.1.1.39) Rubisco activase, fragment (EC 6.3.4.-) RuBisCO subunit binding protein beta subunit, chloroplast. putative NADP-dependent glyceraldehyde-3phosphate dehydrogenase, fragment (EC 1.2.1.13) Glyceraldehyde-3-phosphate dehydrogenase B, chloroplast precursor (EC 1.2.1.13)

45

41, 44, 79, 117

Ribulose 1.5-bisphosphate carboxylase large subunit, fragment (EC 4.1.1.39)

Photosystem II MSP protein (Oxygen-evolving enhancer 1). precursor Photosystem II PsbP protein (Oxygen-evolving enhancer 2) Photosystem II PsbO protein (Oxygen-evolving enhancer 33), putative Ferredoxin-NADP+ reductase (EC 1.18.1.2) Ferredoxin-NADP(H) oxidoreductase, putative (EC 1.18.1.2) ATP synthase subunit alpha (EC 3.6.3.14) ATPase synthase subunit beta (EC 3.6.3.14) ATP synthase subunit epsilon (EC 3.6.3.14)

protein

46, 65, 82, 96, 101, 110, 111, 112, 113, 123, 131, 136, 140

42

16, 35

134, 148, 150

125, 139

125

5, 8

81

8

spot(s)

Table 2. Simplified List of Identified Proteinsa

S. oleracea

A. thaliana

Capsicum annuum

O. sativa

Fragaria x ananassa

Pisum sativum

Scenedesmus vacuolatus

O. sativa

Pinus halepensis

P. thunbergii

P. radiata

Pinus longaeva

Picea abies

Nicotiana sylvestris

Pinus thunbergii

Oryza sativa

A. thaliana

A. thaliana

Pinus monticola

Spinacia oleracea

reference organism Mr

pI

theoreticalc Mr

14933

59519

53381

40665

40326

35128

2735

8.06

5.73

6.11

7.98

8.32

5.55

9.53

14825

60625, 62375

61190, 51575, 61465

35680, 35750

35680

33435, 32255

24600

sp P09559

trm Q9SAU2

trm O78327

sp Q40677

sp Q9M4S8

sp P12859

trm Q8VXQ9

trm Q6ZFJ9

trm Q9M431

sp P10053

45007

80105

42005

33526

48097

33574

63798

25108

19312

5.82

6.16

6.39

7.65

7.58

6.73

5.77

8.44

8.8

44860

28170

78515

40795

29705

47585

49270

51105, 50410, 43415, 44860, 44190, 50620 78645, 67560, 67605

13680, 13620, 13455, 14160

Chloroplast: Photosynthesis: Carbohidrate Pathways trm Q7YNE7 51700 6.09 26005, 31810, 31595, 26165, 32965, 60990, 61095, 60125, 65455, 32615, 51480, 30880, 38270 sp P24679 52762 6.19 17580, 24820

sp O47036

trm Q9SAQ0

sp P41602

trm Q6ZFJ3

trm Q9FKW6

trm Q94JV7

sp Q7M1Y9

pI

5.98

6.77

6.64

6.63

6.16

6.86

6.31

5.67, 5.75, 6.18, 5.98, 6.10, 5.84 5.34, 5.56, 5.52

6.01, 6.11, 6.59, 7.05

5.61, 5.91

5.92, 6.29, 6.49, 6.72, 6.93, 6.68, 6.73, 6.79, 6.69, 7.07, 7.17, 6.65, 7.61

6.05

5.59, 5.71

7.10, 7.28, 7.60

7.21, 7.55

7.21

5.45, 5.56

6.63

5.56

experimental

Chloroplast: Photosynthesis: Electron Transfer Chain sp P12359 35171 5.58 32255

accesion numberb

10.85

7.18

3.17

14.45

16.79

2

14.06

6.85

2.72

10.41

10.11

5.35

10.32

14.42

10.84

5.58

6.93

*

5.48

ProPilot scored

25.9

13.2

10.1

24.5

40.4

15.6

31.1

32.7

25.1

21.3

50.1

23.4

17.8

40.7

27.6

24.4

32.2

8

34.3

Sequence coveraged

Protein binding. Ribulose-phosphate 3-epimerase activity Phosphoribulokinase activity

transketolase activity

Fructose-bisphosphate aldolase activity

Glyceraldehyde-3phosphate dehydrogenase (phosphorylating) activity Glyceraldehyde-3phosphate dehydrogenase (phosphorylating) activity Triose-phosphate isomerase activity

Unfolded protein binding

ATP binding

Ribulose-bisphosphate carboxylase activity



Nucleotidyltransferase activity ATPase activity. rotational mechanism

Hydrolase

Ferredoxin-NADP+ reductase activity Ferredoxin-NADP+ reductase activity

Protein binding

Protein binding

Molecular Functione


research articles

Journal of Proteome Research • Vol. 7, No. 7, 2008 2625

2626


ATP synthase subunit beta, mitochondrial precursor (EC 3.6.3.14)

Inorganic pyrophosphatase (EC 3.6.1.1)

At1g67280/F1N21_10

Putative NAD-dependent formate dehydrogenase, fragment (EC 1.2.1.2)

66

29, 47

147

2-oxoglutarate dehydrogenase E2 subunit (EC 2.3.1.61) Malate dehydrogenase, mitochondrial precursor (EC 1.1.1.37) Nodule-enhanced malate dehydrogenase (EC 1.1.1.37)

NADP-dependent malate dehydrogenase (EC 1.1.1.40) Aldehyde dehydrogenase RF2B, mitochondrial (EC 1.2.1.3) Putative glyoxylase I (EC 4.4.1.5) Putative glyoxalase (EC 4.4.1.5) Isocitrate dehydrogenase (EC 1.1.1.42)

Aldose reductase, putative (EC 1.1.1.21) Pyruvate dehydrogenase E1 component subunit alpha, mitochondrial. precursor (EC 1.2.4.1) Malate Dehydrogenase (EC 1.1.1.37)

36, 58

83, 84

102, 137

108

109

28, 47

9

110

69

103, 105

129

104

71, 73, 93

76

P. pinaster

A. thaliana

O. sativa

Nicotiana plumbaginifolia

P. sativum

Citrullus lanatus

A. thaliana

Cucumis sativus

O. sativa

O. sativa

Zea mays

Mr

pI

theoreticalc Mr

trm Q8VYG4

trm Q9XE59

trm O82159

47428

61257

42669

5.61

5.42

5.7

58225, 58125, 56860

72425

44330, 43745

6.68, 7.30

6.83

6.86

5.65, 5.93

5.48

6.68

6.15

6.78, 6.80

7.04

24159

5.56

30330

trm Q8VX85

27369

9.01

44090

Glyoxysome: Glyoxylate Cycle trm Q8W593 39167 6.97 35015, 32470

trm Q6YVH9

7.48

5.68, 5.93

6.37

5.77, 5.83

39355, 37730

54395

47585

34010, 32470

38080

60990

47375

40095, 41025

43745

Mitochondria: Electron Transfer sp P17614 59856 5.95 62605., 62680

7.61

8.88

9.21

6

4.99

5.82

6.29

6.66

6.18

7.62

6.50, 6.54

41846

36200

49939

46176

29567

32186

58598

45703

35526

43228

6.83

6.19, 6.38, 6.51

6.30

7.01, 7.04

37820, 38875

trm O81609

sp P17783

trm Q9ZRQ1

trm Q8RW69

trm Q75GB0

trm Q6ES23

trm Q8RUR9

trm Q8H0N9

trm Q6RY59

sp P52903

pI 6.45, 6.56, 6.68, 6.78, 6.98 6.95, 7.38, 7.57, 6.69

experimental

Cytosol: Glycolysis trm Q8LPC3 36200 8.88 41220, 43650, 39355, 40095, 32975 sp P34924 36529 6.67 42335, 41500, 41285, 41230

accesion numberb

Mitochondria: Piruvate Pathways and Tricarboxylic Acid Cycle trm Q60EL7 34428 6.37 37535

Panicum maximum

Pinus pinaster

S. tuberose

O. sativa

A. thaliana

Solanum tuberosum

Populus nigra

Phosphoglycerate kinase (EC 2.7.2.3) Phosphoglycerate mutase (EC 5.4.2.1) Enolase (EC 4.2.1.11)

128, 129

127, 142, 144, 145

Fructose-bisphosphate Physcomitrella patens aldolase (EC 4.1.2.13) Glyceraldehyde-3-phosphate P. sylvestris dehydrogenase, cytosolic (EC 1.2.1.12)

protein

reference organism

86, 89, 102, 103, 122

spot(s)

Table 2. Continued

8.21

7.18

2.74

16.62

6.35

8.55

4.38

3.69

1.3

1.97

4

2.03

6.62

5.52

3.84

10.81

5.53

13.76

3.84

8.55

ProPilot scored

24.2

19.7

12.6

28

23.6

24.2

16

23.4

7.6

17.6

5.6

17.5

34.6

13.6

13.1

29.5

12.3

44.1

17.1

24.2

Sequence coveraged

Lactoylglutathione lyase activity Oxidoreductase activity

Hydrogen ion transporting ATP synthase activity. rotational mechanism

Acyltransferase activity

Acyltransferase activity

Lactoylglutathione lyase activity Lactoylglutathione lyase activity Isocitrate dehydrogenase (NADP+) activity Acyltransferase activity

Malate dehydrogenase activity Malate dehydrogenase activity Oxidoreductase activity

Oxidoreductase activity Oxidoreductase activity

Fructose-bisphosphate aldolase activity Glyceraldehyde-3-phosphate dehydrogenase (phosphorylating) activity Phosphoglycerate kinase activity Phosphoglycerate mutase activity Phosphopyruvate hydratase activity

Molecular Functione

research articles Valledor et al.

Glutamate-1-semialdehyde 2.1-aminomutase, chloroplast precursor (EC 5.4.3.8)

UTP-glucose-1-phosphate uridylyltransferase 1 (EC 2.7.7.9)

35

Beta-carbonic anhydrase (EC 4.2.1.1) Carbonic anhydrase (EC 4.2.1.1) Chorismate synthase 1, chloroplast precursor (EC 4.2.3.5) Cysteine synthase (2.5.1.47)

88

142

146

118, 120

65

93

146

85

91, 106

51

111

129, 130

100

Glutamine synthetase (EC 6.3.1.2) Glutamine synthetase, cytosolic isozyme (EC 6.3.1.2) Glutamate synthase, fragment (EC 1.4.7.1) Aspartate aminotransferase, chloroplast (EC 2.6.1.1) Alanine aminotransferase (EC 2.6.1.2)

3-oxoacyl-[acyl-carrier-protein]A. thaliana synthase I, chloroplast precursor (EC 2.3.1.41) Lipoxygenase (EC S. tuberosum 1.13.11.12) 3-beta hydroxysteroid O. sativa dehydrogenase/ isomerase, putative (EC 1.1.1.-) Alcohol dehydrogenase, Pinus banksiana fragment (EC 1.1.1.1) Aldehyde dehydrogenase O. sativa (EC 1.2.1.3)

131

38, 122

Type IIIa membrane protein cp-wap13 (EC 2.4.1.-)

68

A. thaliana

L. esculentum

Populus alba

Lycopersicon esculentum

O. sativa

Nicotiana tabacum

O. sativa

Panicum miliaceum

P. sylvestris

P. sylvestris

P. sylvestris

Vigna unguiculata

S. tuberosum

Alpha-1.4-glucan-protein synthase (EC 2.4.1.186)

51

Glycine max

Malate dehydrogenase, glyoxysomal precursor (EC 1.1.1.37)

protein

reference organism

137

spot(s)

Table 2. Continued

59320

40558

31275

6.33

5.68

9.13

5.51

61095

43745, 43660

30755

74310, 32975

sp P57751

sp Q40147

6.08

5.71

Nucleotide Metabolism 51919 5.72 62375

7.39

7.22, 7.16

6.29

6.51

7.39

6.61

6.53., 6.69

6.46

43910

28540, 28735

31810

56860

43910

34700

43765, 43310

Porfirin Biosynthesys 51413 6.53 45720

5.67

6.77

8.35

6.19

8.01

8.62

6.19

6.21

7.38

30739

47722

33768

34452

53582

50280

58853

39571

41500

trm Q6V3A7

sp Q42884

trm Q69MC9

trm Q8W183

trm Q84UX4

trm Q42425

trm Q43081

sp P52783

6.73

7.04, 7.23

6.69

5.74, 6.38

7.17

Lipid Metabolism 50413 8.29 51480 96964

6.25

6.24

43120

39422

Nitrogen Fixation and Aminoacid Metabolism trm Q9ZS52 39283 5.37 43415

trm Q9LLR2

trm Q43027

trm Q94HJ5

trm Q9SC16

sp P52410

trm O24548

sp Q8RU27

6.08

Mr

Cell Wall Biosynthesis 41602 5.71 43415

pI

experimental

7.30

Mr

theoreticalc

Glyoxysome: Glyoxylate Cycle sp P37228 37395 7.55 37330

accesion numberb pI

12.82

1.92

2.93

3.61

2

1.82

2.04

5.85

1.43

19.92

5.73

4.01

4.01

4.62

13.09

2.7

2.75

3.12

3.07

ProPilot scored

36.4

5.4

15.8

8.4

4.9

6.5

9.1

25.4

8.7

48.7

24.5

6.4

13.6

26.3

9.4

4.9

15.3

12.6

16.4

Sequence coveraged

UTP:glucose-1-phosphate uridylyltransferase activity

Cysteine synthase activity

1-aminocyclopropane1-carboxylate synthase activity Carbonate dehydratase activity Carbonate dehydratase activity

Glutamate synthase (ferredoxin) activity Transaminase activity

Glutamate-ammonia ligase activity Glutamate-ammonia ligase activity

Oxidoreductase activity

Catalytic activity

Catalytic activity

Lipoxygenase activity

Catalytic activity

Alpha-1.4-glucanprotein synthase (UDP-forming) activity alpha-1.4-glucanprotein synthase (UDP-forming) activity


Molecular Functione


research articles


2628


RNA binding protein Elongation factor Tu. chloroplast precursor (EC 3.6.5.3) Elongation factor Tu (EC 3.6.5.3) Ribosomal protein S10, fragment (EC 6.1.1.11) Seryl-tRNA synthetase (EC 6.1.1.11) Chaperonin CPN60-1, mitochondrial precursor Chaperonin 60 alpha subunit Chloroplast chaperonin 21

129 70

Chalcone synthase (EC 2.3.1.74) Phenylcoumaran benzylic ether reductase homologue TP5 (EC 1.1.1.-) Rubber elongation factor (EC 2.5.1.20)

92

Actin

Annexin p34

Catalase isozyme 2 (EC 1.11.1.6)

20, 52, 80, 29

122

149, 150

75, 132

141

99

15

39

32

dnaK-type molecular chaperone, putative E3 ubiquitin-protein ligase BRE1-like 1 (EC 3.6.2.-) FtsH protease (VAR2) (EC 3.4.24.-) 26S protease regulatory subunit 6A homologue (EC 3.4.25.1) ATP-dependent Clp protease subunit ClpR4. putative (EC 3.4.21.92)

38

62

17

75

113

79

90

Deoxyuridine triphosphatase, putative (EC 3.6.1.23)

UTP-glucose-1-phosphate uridylyltransferase 2 (EC 2.7.7.9) UDP-sulfoquinovose synthase. putative (EC 3.13.1.1) Adenylate kinase B (EC 2.7.4.3)

protein

23

135

131, 148

36

spot(s)

Table 2. Continued

N. plumbaginifolia

S. tuberosum

Picea rubens

Hevea brasiliensis

Tsuga heterophylla

P. pinaster

A. thaliana

L. esculentum

A. thaliana

A. thaliana

O. sativa

Vitis vinifera

Canavalia lineata

Z. mays

Zea mays

Lactuca sativa

O. sativa

A. thaliana P. sativum

O. sativa

O. sativa

O. sativa

A. thaliana

reference organism pI

35845

26674

52871

5.38

7.65

8.51

Mr

19225

30800

51480, 51575

Nucleotide Metabolism 51738 5.8 62605

Mr

theoreticalc

sp P49316

trm Q9M3H3

trm Q9SPI7

sp P15252

trm Q9M524

trm Q8GUU5

trm Q8LB10

sp P54776

trm O80860

trm Q8RXD6

trm Q6Z7L1

trm Q6B4V4

trm Q9ZTV1

sp P29185

trm O82108

trm Q9FUT9

trm Q8W2C4

7.57, 7.60

pI

5.57, 5.68, 5.82, 6.51

6.32, 7.22

6.67

Redox Maintenance 56897 6.75 61470, 61465

65355, 49755

37190

6.98

5.04

6.93

Cytoskeleton 41590 5.3 77185, 35015, 47680, 13635 35845 5.38 32975

14590

33298

6.59

5.51

5.78

5.64

5.74

6.33

5.28

6.32

6.69

6.59

6.53

Secondary Metabolism 43115 5.95 45980

40315

71280

43260

74310

27700

67235

65355

65455

13455

45170

6.85

9.84

4.94

5.99

6.46

5.47

8.96

5.23

5.68

7.93

8.19

6.04

7.04 6.24

5.68

7.35

7.17, 7.28

5.77

experimental

28740

33440

47505

74157

99718

72897

26396

61439

61211

51709

21191

48423

Protein Translation. Folding and Degradation trm Q9FM47 42407 6.02 43745 sp O24310 53050 6.62 48310

trm Q7Y196

sp Q08480

trm Q60E66

trm Q9M9P3

accesion numberb

6.01

3.7

11.4

2.45

6.84

3.02

1.62

7

7.2

*

11.86

2

11.95

4.01

2.03

7.05

3.2

4 7.84

3.7

10.35

2.87

4.68

ProPilot scored

11

6.1

47.2

22.6

25.1

20.3

11.1

18.2

12.4

*

27.8

13.5

27.7

13.2

7.2

51.3

14.8

5.9 20.3

6.1

42.8

14.6

20.9

Sequence coveraged

Catalase activity

Ca-dependent phospholipid binding

ATP, Protein binding

acyltransferase activity Transcription repressor activity

Endopeptidase Clp activity

Metalloendopeptidase activity Hydrolase activity

Structural constituent of ribosome Aminoacyl-tRNA ligase activity Unfolded protein binding Unfolded protein binding Unfolded protein binding Unfolded protein binding Protein binding

GTP binding

Nucleotide binding GTP binding

Nucleobase. nucleoside. nucleotide kinase activity hydrolase activity

Catalytic activity

Nucleotidyltransferase activity

Molecular Functione

research articles Valledor et al.

Heat shock cognate 70 kDa protein Heat shock protein 70 like protein Noncell-autonomous heat shock cognate protein 70 Stromal 70 kDa heat shock-related protein, chloroplast precursor Aspartic protease inhibitor 1, precursor (EC 3.4.23.-) Cysteine protease inhibitor 1, precursor (EC 3.4.23.-) Stress-induced protein sti1-like protein (EC 3.1.3.-)

37

AT3g53350/F4P12_50 Hypothetical protein Putative oxidoreductase, zinc-binding OSJNBa0008A08.11 protein

143 10 50

trm Q8LJP9

O. sativa

A. thaliana A. thaliana O. sativa

N. tabacum

A. thaliana

S. tuberosum

S. tuberosum

P. sativum

Cucurbita maxima

A. thaliana

Petunia x hybrida

pI

5.31

6

5.51

8.55

5.22

5.1

Signaling 33857 7.1

63706

96964

24545

75515

71435

71173

trm Q7XT99

38234

6.03

40275

Process 40775 37130 42520

40275

72240

32975

32975

78645

77185

74310

7.21

7.50 5.61 5.83

7.21

6.63

6.98

6.98

5.34

5.57

5.74

5.66

5.46

5.28

6.29

5.77

Stress Related Proteins 71226 5.11 77180

28370

25945

31810

18305

5.76

6.21

6.11

8.28

8.2

6.27

5.43

Mr

experimental

22035

22119

28534

28937

32155

52137

Redox Maintenance 18842 4.92 24880

Mr

theoreticalc

Unknown Biological trm Q8VYU8 45709 5.67 trm Q8L9723 37456 8.14 trm Q7EYM8 39582 7.63

sp P40691

trm Q9STH1

sp P20347

sp Q41480

sp Q02028

trm Q8GSN3

trm Q9SZJ3

sp P09189

trm Q944B7

Brassica juncea P. pinaster

trm Q8RVF8

trm Q84NN4

trm Q5YJK8

sp O23970

accesion numberb

N. tabacum

O. sativa

Hyacinthus orientalis

Helianthus annuus

reference organism pI

10.13

* 2 8.3

8.09

8.24

13.09

4.95

9.81

16.99

13.68

12.31

5.52

4.23

2.2

1.36

4.08

1.75

ProPilot scored

28.5

* 7.2 23.3

30.3

12.5

9.4

17.6

24.6

29.4

29.6

22.5

14.4

17.1

17.3

8.3

21.2

14.4

Sequence coveraged

Oxidoreductase activity Oxidoreductase activity


Endopeptidase inhibitor activity Endopeptidase inhibitor activity Binding

ATP. unfolded protein binding

ATP. unfolded protein binding ATP binding

ATP binding

Cu-Zn superoxide dismutase activity

Transferase activity

Peroxidase activity

Glutathione peroxidase activity Oxidoreductase activity

Molecular Functione

a Proteins were classified according to Kyoto Encyclopedia of Genes and Genomes. b Accesion number according to: Swiss-Prot (sp); trmEMBL (trm). c Mr (in kDa) and pI of the homologous protein were calculated with a tool available at http://www.expasy.ch/tools/pi_tool.html. d Asterisk (*) represents proteins identified by MALDI-TOF-TOF. e Molecular function terms was inferred from QuickGO (http:// www.ebi.ac.uk/ego/).

126

Auxin-induced protein PCNT115

126

94

122

122

18

20

38

59

3

1

65

25

Glutathione peroxidase 1 (EC 1.11.1.9) Peroxiredoxin, fragment (EC 1.11.1.15) Thioredoxin-like protein CDSP32, putative (EC 1.11.1.15) Thioredoxin peroxidase (EC 1.11.1.15) Dehydroascorbate reductase (EC 1.8.5.1) Cu-Zn-superoxide dismutase (EC 1.15.1.1)

protein

26

spot(s)

Table 2. Continued


research articles


research articles

Valledor et al.

Figure 7. Classification of expressed proteins. For each different protein in Table 2, the gene ontology classification was determined for subcellular component (a) and biological process/metabolic pathway (b). Relative proportions of each ontology term are expressed in percentage values.

ogy due to substitution, allowing a robust scoring of peptide and protein hits. We identified 100 proteins out of the 150 spots subjected to LC-MS/MS analysis, quite unusual high percentage for species almost absent in databases. They were classified into 12 or 18 groups based on their corresponding cell component or biological process/pathway categories, respectively. Carbohydrate metabolism and photosynthetic enzymes dominated the 2-DE protein profile of P. radiata needles.

Acknowledgment. Financial support has come from AGL 2004-00810 Spanish National Project. Spanish M.E.C. supported FPU fellowship of L.V. Supporting Information Available: Figures of plantation of P. radiata, 2-DE of P. radiata, relationship between standard deviation/coefficient of variation and the mean of protein amount, molecular weight and isoelectric point, proteins identified which to enzymes related to photoshynthesis, glycolysis, pyruvate pathways and tricarboxylic acid cycle, and tables of the complete analytical and biological variance data sets and identification of proteins present in selected spots. 2630


This material is available free of charge via the Internet at http://pubs.acs.org.

References (1) Carneiro, C. M.; Brown, D. FAO advisory committee on paper and wood products. Global outlook for plantations, 1999. (2) Ryan, M. G.; Binkley, D.; Fownes, J. H. Adv. Ecol. Res. 1997, 27, 213–262. (3) Martinez-Vilalta, J.; Vanderklein, D.; Mencuccini, M. Oecologia 2007, 150, 529–544. (4) Devey, M. E.; Bell, J. C.; Uren, T. L.; Moran, G. F. Genome 2002, 45, 984–989. (5) Fisher, P. J.; Richardson, T. E.; Gardner, R. C. TAG 1998, 96, 969– 979. (6) Kumar, S.; Richardson, T. E. Mol. Breed. 2005, 15, 55–64. (7) Thiellement, H.; Bahrman, N.; Damerval, C.; Plomion, C.; et al. Electrophoresis 1999, 20, 2013–2026. (8) Thiellement, H.; Zivy, M.; Plomion, C. J. Chromatogr., B 2002, 782, 137–149. (9) Rossignol, M.; Peltier, J. B.; Mock, H. P.; Matros, A.; et al. Proteomics 2006, 6, 5529–5548. (10) Jorrıń, J. V.; Rubiales, D.; Dumas-Gaudot, E.; Recorbet, G.; et al. Euphytica 2006, 147, 37–47. (11) Jorrıń, J. V.; Maldonado, A.; Castillejo, M. A. Plant proteome analysis: a 2006 update. Proteomics 2007, 7, 2947–2962.

research articles

2-DE Proteome of Pinus radiata Needle (12) Jorge, I.; Navarro, R. M.; Lenz, C.; Ariza, D.; et al. Proteomics 2005, 5, 222–234. (13) Jorge, I.; Navarro, R. M.; Lenz, C.; Ariza, D.; et al. Proteomics 2006, S1, S207–S214. (14) Renaut, J.; Lutts, S.; Hoffmann, L.; Hausman, J. F. Plant Biology 2004, 6, 81–90. (15) Plomion, C.; Lalanne, C.; Claverol, S.; Meddour, H.; et al. Proteomics 2006, 6, 6509–6527. (16) Lippert, D.; Zhuang, J.; Ralph, S.; Ellis, D. E.; et al. Proteomics 2005, 5, 461–473. (17) Lippert, D.; Chowrira, S.; Ralph, S. G.; Zhuang, J.; et al. Proteomics 2007, 7, 248–270. (18) Costa, P.; Pionneau, C.; Bauw, G.; Dubos, C.; et al. Electrophoresis 1999, 20, 1098–1108. (19) Gion, J. M.; Lalanne, C.; LeProvost, G.; Ferry-Dumazet, H.; et al. Proteomics 2005, 5, 3731–3751. (20) Yin, T. M.; Wang, X. R.; Andersson, B.; Lerceteau-Koumlhler, E. Theor. Appl. Genet. 2003, 106, 1075–1083. (21) Komulainen, P.; Brown, G. R.; Mikkonen, M.; Karhu, A.; GarciaGil, M. R.; O’Malley, D.; Lee, B.; Neale, D. B.; Savolainen, O. Theor. Appl. Genet. 2003, 107, 667–678. (22) Pot, D.; McMillan, L.; Echt, C.; Ke Oribistm, G.; Garnier-Gere, P.; Cato, S.; Plomion, C. New Phytol. 2005, 167, 101–112. (23) Diego, L. B.; Berdasco, M.; Fraga, M. F.; Can ˜ al, M. J.; Rodriguez, R.; Castresana, C. J. Exp. Bot. 2004, 55, 1597–1599. (24) Mouradov, A.; Glassick, T.; Hamdorf, B.; Murphy, L.; Fowler, B.; et al. Proc. Natl. Acad. Sci. U.S.A. 1998, 95, 6537–6542. (25) Shilov, I. V.; Seymour, S. L.; Patel, A. A.; Loboda, A.; et al. Mol. Cell. Proteomics 2007, 6, 1638–1655. (26) Go¨rg, A.; Postel, W.; Weser, J.; Gu ¨ nther, S.; et al. Electrophoresis 1987, 8, 45–51. (27) Mathesius, U.; Keijzers, G.; Natera, S. H. A.; Weinman, J. J.; et al. Proteomics 2001, 1, 1424–1440.

(28) Chich, J. F.; David, O.; Villers, F.; Schaeffer, B.; Lutomski, D.; Huet, S. J. Chromatogr., B 2007, 849, 261–272. (29) Hager, J. W.; Le Blanc, J. C. Y. Rapid Commun. Mass Spectrom. 2003, 17, 1056–1064. (30) Damerval, C.; de Vienne, D.; Zivy, M.; Thiellement, H. Electrophoresis 1986, 7, 52–54. (31) Gygi, S. P.; Corthals, G. L.; Zhang, Y.; Rochon, Y.; Aebersold, R. Proc. Natl. Acad. Sci. U.S.A. 2000, 97, 9390–9395. (32) Neuhoff, V.; Stamm, R.; Eisbl, H. Electrophoresis 1985, 6, 427–448. (33) Ruebelt, M. C.; Leimgruber, N. K.; Lipp, M.; Reynolds, T. L. J. Agric. Food Chem. 2006, 54, 2154–2161. (34) Saravanan, R. S.; Rose, J. K. C. Proteomics 2004, 4, 2522–2532. (35) Hunt, S.; Thomas, M. R.; Sebastian, L. T.; Pedersen, S. K.; et al. J. Proteome Res. 2005, 4, 809–819. (36) Wheelock, A. M.; Buckpitt, A. R. Electrophoresis 2005, 26, 4508– 4520. (37) Wheelock, A. M.; Goto, S. Expert Rev. Proteomics 2006, 129–142. (38) Asirvatham, V.; Watson, B.; Summer, L. Proteomics 2002, 2, 960– 968. (39) Horgan, G. W. J. Proteome Res. 2007, 6, 2884–2887. (40) de Vienne, D.; Bost, B.; Fie´ve, J.; Zivy, M.; et al. Plant Physiol. Biochem. 2001, 39, 271–283. (41) Gustafsson, R.,C.; Glasbey, C. A.; Blomberg, A.; Rudemo, M. Proteomics 2004, 4, 3791–3799. (42) Dubos, C.; Plomion, C. Plant Mol. Biol. 2003, 51, 249–262. (43) Liska, A.; Shevchenko, A. Proteomics 2003, 3, 19–28. (44) Habermann, B.; Oegema, J.; Sunyaev, S.; Shevchenko, A. Mol. Cell. Proteomics 2004, 3, 238–249. (45) Porubleva, L.; van der Velden, K.; Kothari, S.; Oliver, D. J.; et al. Electrophoresis 2001, 22, 1724–1738.

PR7006285


Proteomic Analysis of Pinus radiata

Recommend Documents