Comparative Quantitative Proteomics Analysis of the ABA Response

Article pubs.acs.org/jpr

Comparative Quantitative Proteomics Analysis of the ABA Response of Roots of Drought-Sensitive and Drought-Tolerant Wheat Varieties Identifies Proteomic Signatures of Drought Adaptability Sophie Alvarez,† Swarup Roy Choudhury,† and Sona Pandey* Donald Danforth Plant Science Center, 975 North Warson Road, St. Louis, Missouri 63132, United States S Supporting Information *

ABSTRACT: Wheat is one of the most highly cultivated cereals in the world. Like other cultivated crops, wheat production is significantly affected by abiotic stresses such as drought. Multiple wheat varieties suitable for different geographical regions of the world have been developed that are adapted to different environmental conditions; however, the molecular basis of such adaptations remains unknown in most cases. We have compared the quantitative proteomics profile of the roots of two different wheat varieties, Nesser (drought-tolerant) and Opata (droughtsensitive), in the absence and presence of abscisic acid (ABA, as a proxy for drought). A labeling LC-based quantitative proteomics approach using iTRAQ was applied to elucidate the changes in protein abundance levels. Quantitative differences in protein levels were analyzed for the evaluation of inherent differences between the two varieties as well as the overall and variety-specific effect of ABA on the root proteome. This study reveals the most elaborate ABA-responsive root proteome identified to date in wheat. A large number of proteins exhibited inherently different expression levels between Nesser and Opata. Additionally, significantly higher numbers of proteins were ABA-responsive in Nesser roots compared with Opata roots. Furthermore, several proteins showed variety-specific regulation by ABA, suggesting their role in drought adaptation. KEYWORDS: ABA, drought response, iTRAQ, Nesser, Opata M85, quantitative proteomics, wheat, wheat root proteome

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and their intricate interactions with the environment.3,5,12,13 Stress-tolerant varieties are typically able to implement several concurrent mechanisms to reduce water loss, such as developing larger/deeper root systems, increasing stomatal sensitivity, accumulating osmolytes, and increasing antioxidant activities of various enzymes.14−26 The intricacies of such complex regulatory mechanisms have made it obvious that global analysis of genes, proteins, and metabolites is required to identify the interconnected networks that change in response to drought stress and regulate adaptive responses.3,27−30 Transcriptomics analyses have identified huge gene expression changes in wheat in response to drought.12,13,28,31−33 These data, in combination with the recent availability of the fully sequenced wheat genome,34 are expected to generate critical information for engineering drought tolerance in wheat and to provide a better understanding of the gene networks involved in adaptive responses of previously developed varieties. Several genes such as those encoding for signaling proteins, proteins involved in maintaining water status, and many transcription factors have been identified from wheat that confer drought or abiotic stress tolerance in wheat or in heterologous plant systems upon overexpression. Wheat R2R3Myb family transcription factors MYB30-B, MYB33,

INTRODUCTION Sufficient wheat production is considered to be an important contributor to both global food security and political stability. Approximately 215 million hectares of wheat are planted every year, which makes it the most highly cultivated cereal in the world.1,2 By some estimates, a 60% increase in wheat production is required by the year 2050 to meet the demand of the growing world population.3,4 Similar to many cultivated crops, wheat production is severely affected by various abiotic stresses such as drought, salinity, low temperature, and heat, which results in an estimated 50−60% loss in grain yield annually.3,5,6 Major efforts are underway to engineer abiotic stress tolerance in wheat around the world. However, compared with other cultivated crops such as rice or corn, wheat genetics is relatively complicated due to its hexaploid genome.7 Breeding strategies have been used extensively in the past few decades to improve grain yield as well as tolerance against both biotic and abiotic stresses. These efforts have resulted in the generation of several improved varieties that are adapted to different conditions;8−12 however, the molecular basis of such adaptive phenotypes remains largely unknown. Adaptation to drought or acquisition of drought tolerance is a complex trait and encompasses a number of physiological and biochemical changes. Quantitative trait loci (QTL) analysis has shown that such traits are often controlled by multiple genes © 2014 American Chemical Society

Received: November 26, 2013 Published: January 30, 2014 1688

dx.doi.org/10.1021/pr401165b | J. Proteome Res. 2014, 13, 1688−1701

Journal of Proteome Research

Article

were compared to pinpoint inherent differences in the root proteome of Opata and Nesser and the differences in their ABA responsiveness. This study reports the most elaborate root proteome of wheat to date and identifies key proteins that change in response to ABA, implying their role during drought or other abiotic stresses. Furthermore, these results provide critical insights into the possible molecular mechanisms responsible for differential drought sensitivities in two wheat varieties adapted to different environmental conditions.

TaPIMP1 and TaASR1; a WRKY family transcription factor TaWRKY10, an ERF family transcription factor, TaERF3, as well as many heat shock transcription factors have been shown to enhance drought tolerance in transgenic Arabidopsis, tobacco, and wheat upon overexpression.35−43 Similarly, overexpression of signaling proteins such as wheat SnRK2.8, wheat PI4K, and an α-amylase inhibitor as well as aquaporins confers salt and drought tolerance in Arabidopsis.44−53 Furthermore, virus-induced silencing of proteins such as ERA1 (enhanced response to ABA1) and Sal1 (inositol polyphosphate 1-phosphatase) has also lead to better water use efficiency and improved drought tolerance in wheat.54 Because gene expression changes ultimately result in changes in the expression levels or activities of various proteins, a comprehensive understanding of the drought-responsive proteomic changes is also warranted to fully appreciate the scope of such global analyses. Several recent studies have exploited proteomics approaches to uncover the drought response of wheat varieties, but in most of these cases, gelbased approaches were used that resulted in the identification of only a handful of proteins.27,55−62 In instances where shotgun proteomics approaches were used, significantly more proteins were identified;30 however, the unavailability of the wheat genome sequence till recently affected the accurate mapping of many of the identified proteins. Furthermore, the use of different cell or tissue types, specific drought treatment conditions, and duration has resulted in essentially nonoverlapping sets of proteins from individual studies.7 In an attempt to elucidate the proteomic basis of droughtadaptation and -response, the proteomes of two different wheat varieties, Opata M85 and Nesser, were compared using an iTRAQ-based labeling quantitative proteomics approach under control and stress conditions. Opata is an elite CIMMYT (International Maize and Wheat Improvement Center) wheat variety that has been used as a parent of the ITMI (International Triticeae Mapping Initiative) mapping population and also in the production of synthetically derived hexaploid wheat. Opata is a relatively drought-sensitive variety.63−65 Nesser is a relatively drought-tolerant variety grown extensively in North Africa, as it has adapted to semiarid conditions.64,65 To evaluate and compare the effect of drought stress, the plants were treated with abscisic acid (ABA). ABA is a key phytohormone produced in response to drought and is involved in coordinating various signaling and metabolic pathways during drought stress.66−68 Root tissue was harvested from treated and nontreated plants for proteomic identification as roots are the first sensors of water stress in soil. Moreover, roots are specifically suited for proteome analysis because they do not exhibit an over-representation of highly abundant proteins such as Rubisco found in green tissue or seed-storage proteins present in seeds that tend to mask the identification of many relatively low-abundant proteins. Our objective was to identify early changes in protein abundance in response to ABA (and by proxy, drought) in two different wheat varieties that have clearly different drought responses. The use of 4-plex iTRAQ permitted the simultaneous labeling and detection of the four samples used in this study: Opata-control was labeled with reagent 114, Opata-ABA-treated was labeled with reagent 115, Nesser-control was labeled with reagent 116, and NesserABA-treated was labeled with reagent 117. Quantitative differences in the protein abundance were analyzed to establish a comprehensive database of wheat root proteome and its regulation by ABA. Moreover, the protein abundance changes

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MATERIALS AND METHODS

Plant Growth and Treatment Conditions

Wheat seeds of Opata and Nesser varieties were obtained from CYMMIT, Mexico, grown, and bulked-up in greenhouses in a 16/8 h light/dark cycle, 21 °C, 300 W/m2 light intensity and 40% humidity regime. Major physiological traits such as plant height, days to tiller, days to head emergence, and days to full maturity were counted for both of the varieties grown under identical, well-watered conditions. Parameters of water loss including stomatal conductance and transpiration rate were measured with a portable photosynthetic system, LI6400XT (Li-COR, Lincoln, NE), on 4 week old leaves. The conditions in the leaf chamber were calibrated to 500 μmol m−2 s−1 photosynthetic photo flux density, 400 μmol mol−1 CO2, 23 °C, and 60% relative humidity, similar to those in the greenhouse where plants were grown. Measurements were conducted on the second open leaf from the top. For evaluating root-growth parameters, seeds were germinated on wet filter papers. Germinated seedlings were rolled into germination paper and grown under sufficient-water or limited-water conditions for an additional 10 days. Root phenotypes were recorded essentially according to previously published methods.9 For proteomic profiling, seeds were germinated and grown in 12 × 30 cm flats with roots immersed in liquid growth media (0.25X MS salt) for 10 days. For stress treatment, the regular growth media was replaced with media containing ABA (100 μM) or EtOH (solvent control, equimolar amount), and intact plants were treated for 6 h. Roots were harvested at the end of the treatment, washed with distilled water, and flash-frozen in liquid N2. Three biological replicates were used for proteomic analysis. Protein Extraction and iTRAQ Labeling

Total protein was extracted from the control and ABA-treated root tissue of two genotypes of wheat, Opata and Nesser, as previously described.69 The protein pellet was air-dried and resuspended in 8 M urea in 500 mM TEAB (triethylammonium bicarbonate). Protein concentration was determined using the CB-X protein assay (G-Biosciences, St. Louis, MO) according to the manufacturer’s protocol. Three biological replicates were performed for each sample. Protein samples (100 μg each) were reduced using 10 mM TCEP (tris(2-carboxyethyl) phosphine) for 30 min at room temperature and alkylated using iodoacetamide (55 mM) for 30 min in the dark at room temperature. Samples were diluted to 1.5 M urea using TEAB (500 mM), digested for 16 h at 37 °C with trypsin (10 μg), and cleaned up using solid-phase extraction. The samples were acidified with 0.5% TFA (trifluoroacetic acid) and loaded on Sep-Pak Vac C18 (Waters, Milford, MA). After four washes with 0.1% TFA, the peptides were eluted using 0.1%TFA in 80% ACN. Eluates were dried down and redissolved in 30 μL of 25 mM TEAB, pH 8.5. Peptides from the 12 samples (three biological 1689



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(K) as fixed modifications; and carbamidomethyl modification of cysteines, methionine oxidation and iTRAQ (Y) selected as variable modifications. The mass error tolerance for precursor ions was set to 15 ppm and 0.08 Da for fragment ions. Automatic isotope correction was carried out using the values supplied with the AB Sciex reagents. ProteoIQ (Premier Biosoft, Palo Alto, CA) was used to filter the peptides and protein ratios. The protein and peptide probabilities were set at 50 and 60%, respectively. Only proteins with at least two peptides with a Mascot score of at least 25 and detected in at least two replicates were further used. Normalization was performed using the total labeling intensity of each species. The decoy database search calculated the false-positive rate of protein identification at 1%. The functional annotation was performed using Blast2GO,71 which indicates the possible localization (labeled C), function (labeled F), and pathway (labeled P) for each protein. The same protein accession number can be associated with one or more localization, function, and pathway (Supplemental Table 1 in the Supporting Information). The protein ratios for each replicate for the comparison of Opata and Nesser under control conditions, Opata under control and treated conditions, and Nesser under control and treated conditions were averaged as shown in Supplemental Figure 1 in the Supporting Information. The standard deviation and coefficient of variation were also calculated.

replicates of each sample) were labeled with three sets of 4-plex iTRAQ isobaric reagents according to the manufacturer’s instructions (AB Sciex, Framingham, MA). Opata-control, ABA-treated Opata, Nesser-control, and ABA-treated Nesser samples were labeled with reagent 114, 115, 116, and 117, respectively. The three biological replicates were processed at the same time. The four labeled digests were pooled into one sample for each of the three replicates. The samples were lyophilized and dissolved in 100 μL of 20% ACN, 5 mM ammonium formate (pH 2.7) for separation by strong cation exchange (SCX). For SCX, 100 μL injections of iTRAQ-labeled pooled peptides were fractionated as previously described.69 LC−MS/MS Separation and Data Analysis

iTRAQ-labeled samples were analyzed using an LTQ-Orbitrap Velos mass spectrometer (Thermo Fisher Scientific, Rockford, IL) coupled to an Eksigent nanoLC Ultra (AB Sciex). Each sample (5 μL) was injected onto the LC−MS/MS system as previously described.70 The samples were first loaded onto a trap column (C18 PepMap100, 300 μm × 1 mm, 5 μm, 100 Å, Thermo Scientific Dionex, Sunnyvale, CA) at a flow rate of 4 μL/min for 5 min. Peptide separation was carried out on a C18 column (Acclaim PepMap C18, 15 cm ×75 μm × 3 μm, 100 Å, Thermo Scientific Dionex) at a flow rate of 0.26 μL/min. Peptides from iTRAQ samples were separated using a 80 min linear gradient ranging from 2 to 40% B (mobile phase A, 0.1% formic acid in water; mobile phase B, 0.1% formic acid in ACN). The mass spectrometer was operated in positive ionization mode. The MS survey scan was performed in the FT (Fourier transform) cell from a mass range of 300 to 1700 m/z. The resolution was set to 60 000 at 400 m/z, and the automatic gain control (AGC) was set to 1 000 000 ions. Higher-energy collisional dissociation (HCD) fragmentation was used for MS/MS, and the 10 most intense signals in the survey scan were fragmented. A resolution of 7500 was used in the Orbitrap with an isolation window of 3 m/z, a target value of 100 000 ions, and a maximum accumulation time of 1 s. Fragmentation was performed with normalized collision energies of 40% and activation times of 0.1 ms. Dynamic exclusion was performed with a repeat count of 1 and an exclusion duration of 75 s. A minimum MS signal for triggering MS/MS was set to 5000 counts. The proteins were identified using a database combining sequences of Triticum from NCBInr (19 486 sequences), the Brachypodium protein database v1.0 (32 255 sequences) (http://mips.helmholtz-muenchen.de/plant/Brachypodium/ download/index.jsp), and a translated database of the low copynumber genome (LCG) assemblies of Triticum aestivum (20 051 sequences) (ftp://ftpmips.helmholtz-muenchen.de/plants/ wheat/UK_454/). The search was done using a merged database of the forward and reversed sequences to estimate the false discovery rate. To eliminate peptides matching to common contaminants, the database also included the sequences from cRAP database (http://www.thegpm.org/ crap/index.html). Data processing was automated using Mascot Daemon (Matrix Science, London, U.K.). Raw mass spectral data were first processed using Mascot Distiller v2.4 (Matrix Science), and the resultant peak lists were searched against the database using Mascot Server v2.4 (Matrix Science). For data processing, the MS/MS peak picking settings include a special reporter ion window from m/z 113.5 to m/z 117.5. All searches were performed using the following settings: trypsin as cleavage enzyme; two missed cleavages; iTRAQ (N terminal), iTRAQ

RNA Extraction, cDNA Synthesis, and Quantitative Real-Time PCR (qRT-PCR)

Root tissue obtained from the control and ABA-treated 10-dayold wheat seedlings was used as a source of total RNA. Growth and treatment conditions were the same as those for seedlings grown for proteomics experiments. RNA isolation and qRTPCR reactions were performed as described.72 Sequences of gene-specific primers used in this study are available in Supplemental Table 6 in the Supporting Information.

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RESULTS AND DISCUSSION

Comparative Growth and Physiological Phenotypes of Opata and Nesser Wheat Varieties under Greenhouse Conditions

Opata and Nesser varieties are adapted to different geographical regions of the world. To establish that their different growth and adaptation rates are not only due to their natural growth environments and to evaluate whether there are any differences in their growth and phenotypes when grown under laboratory conditions, we germinated and grew seeds of both of these varieties in our greenhouses and assessed their overall growth, development, inflorescence (head) formation, and maturity as well as a subset of physiological traits. In addition, we grew plants by cigar roll method9 to assess the comparative root phenotypes such as length of primary root, number of seminal and lateral roots, and lateral root density under both wellwatered and limited-water conditions. These experiments were performed to ascertain that the differences we observe in proteome under laboratory conditions are physiologically relevant. In the greenhouse, both wheat varieties grew relatively similarly during the first week of growth. After ∼10 days, faster growth was obvious in Nesser plants. Initial head emergence was observed at 41 ± 3 days for Nesser and 53 ± 5 days for Opata, whereas fully emerged heads were visible at 50 ± 4 and 65 ± 5 days in Nesser and Opata, respectively (Figure 1A). 1690



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When plants were allowed to dry after full kernel maturity as their natural growth progression (85 ± 5 days), Opata plants dried completely by 100 days whereas Nesser plants still maintained some green tissue (Figure 1B). Because Nesser is adapted to semiarid conditions, we measured the stomatal conductance and rate of transpiration of both of these varieties under well-watered conditions using 4 week old plants. Nesser leaves consistently exhibited significantly higher rates of stomatal conductance and transpiration compared with Opata leaves (Figure 1C). Because our focus is on the comparative root proteomics at young-seedling stage, we evaluated the root phenotypes of Nesser and Opata. The seeds were germinated on moist filter papers, transferred to germination paper rolls, and grown for 10 days under two different watering regimes essentially according to Placido et al.9 Data were recorded at the 6th and 10th days of growth. Data for seminal and lateral roots were recorded only at days 6 and 10, respectively. In general, Nesser plants exhibited significantly improved growth under limited water conditions with bigger plants and higher lateral root density (Table 1). Even under well-watered conditions, clear phenotypic differences were obvious between the two cultivars in this stage of growth. On the basis of the observation that the roots exhibited clear phenotypic differences as early as after 10 days of growth, we chose to use 10-day-old wheat roots for proteomics analysis. To identify early signatures of differential response to stress, we treated seedlings with ABA as a proxy for drought for 6 h, as we have found this treatment to effectively and sufficiently up-regulate many stress-marker proteins.69,70 Wheat Root Proteome

Many studies have recently focused on identification of proteomic changes in crops including wheat in response to various abiotic and biotic stresses.7 While some have used specific organelles such as chloroplast and mitochondria for proteomic analysis, a majority of studies have used flag leaves, seeds, or kernels, while the root tissue remains highly underrepresented in wheat proteomics.30,55−59,61,73,74 This is the first comprehensive report of the root proteome from wheat. The use of iTRAQ-labeling approach and LC− MS/MS to compare the protein expression levels of Opata and Nesser roots in response to ABA treatment identified a total of 1656 proteins (Supplemental Table 1 in the Supporting Information) with a minimum of two unique peptides and with relative quantification information. This is the largest number of proteins identified for wheat root from one single study to date. The use of LC-based proteomics approaches is parsed in wheat because of the lack of genomic sequence information and the high homology in protein sequences due to the polyploid complexity of the wheat genome. A recent publication reported the sequencing of the 17 gigabase pair hexaploid genome of bread wheat (Triticum aestivum) using 454 pyrosequencing.34 The genomic sequence assembly made publicly accessible (ftp://ftpmips.helmholtz-muenchen.de/ plants/wheat/UK_454/) was used here for the protein database search in addition to the wheat entries available in NCBInr and the Brachypodium annotation version 1.0 (http:// mips.helmholtz-muenchen.de/plant/Brachypodium/ download/index.jsp).75 The optimization of the bioinformatics step for the quantitative analysis of wheat proteome is important, as recently demonstrated by a wheat leaf iTRAQ experiment comparing the use of different databases (including the NCBInr plant entries and the Brachypodium annotation also

Figure 1. Growth and physiological response of Opata and Nesser wheat varieties. (A) Representative picture of 50-day-old wheat plants of Opata and Nesser varieties. Head emergence is obvious in Nesser at 50 days but not in Opata. (B) Representative picture of 100 day old wheat plants of Opata and Nesser varieties. The Opata plants are completely dried out by this time, whereas significant amount of green tissue remains in Nesser plants. (C) Net stomatal conductance measured as water vapor transfer sec−1 and net transpiration rate measured as the amount of water loss sec−1 were determined on individual 2nd open leaf of 4 week old wheat varieties (Opata and Nesser) using a Li-COR 6400 gas exchange system. Twenty biological replicates for each variety and five measurements for each leaf were used for data analysis. Error bars represent standard errors (±SE). Asterisks (*) indicate statistically significant differences compared with Opata variety (* = P < 0.001; Student’s t test).

Overall, more heads were formed in Nesser plants compared with Opata, which also resulted in higher seed yield per plant in Nesser. Nesser plants produced on an average seven heads per plant compared with an average five heads per plant for Opata. This led to ∼10 g of seeds per plant from Nesser versus ∼8 g of seeds per plant for Opata under the greenhouse conditions. 1691



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Table 1. Wheat Varieties Opata M85 (Opata) and Nesser Were Grown in Germination Paper Using the Cigar Roll Method under Well-Watered (100 mL/day) and Limited-Watered (50 mL/day) Environments, and Root- and Shoot-Related Traits Were Measured at Days 6 and 10a Opata parameter Shoot Length (cm) well-watered limited-watered Primary Root Length (cm) well-watered limited-watered Seminal Root Number/Plant well-watered limited-watered Lateral Root Number/Plant well-watered limited-watered Lateral Root Density/cm well-watered limited-watered

Nesser

6 day

10 day

6 day

10 day

8.05 ± 0.55 4.89 ± 0.64**

13.89 ± 0.80 6.56 ± 1.1***

7.81 ± 0.51 7.12 ± 0.36

14.35 ± 0.53 10.08 ± 0.58***

13.89 ± 0.49 11.60 ± 0.59**

23.68 ± 0.80 17.84 ± 1.77**

14.60 ± 0.49 13.25 ± 0.46

26.31 ± 0.53 19.07 ± 1.63***

4.08 ± 0.26 4.00 ± 0.21

4.67 ± 0.14 4.67 ± 0.14 2.67 ± 1.54 0.33 ± 0.16*

9.56 ± 2.37 0.58 ± 0.23**

0.12 ± 0.07 0.01 ± 0.01*

0.36 ± 0.09 0.04 ± 0.01**

a

Ten seedlings were measured for each variety under each of the conditions. The experiment was repeated three times and data were averaged (SE). Asterisks (*) indicate statistically significant differences under limited watered condition compared with well-watered control (* = P < 0.05; ** = P < 0.01; *** = P < 0.001; Student’s t test).

used in our study, as well as a combined cereals database and the wheat D-genome progenitor Aegilops tauschii). Such integrated analyses increase not only the quantity of output information in terms of proteome coverage but also the quality of the peptide identification and quantification.76 The combination of the three databases we used for the search resulted in the best protein coverage of the wheat root proteome (Figure 2) with a total of 1656 proteins identified. Each individual database identified unique proteins with 19.6% from the wheat orthogonal assembly, 22.4% from the Brachypodium database, and 26.4% from NCBInr. However, if searched separately, the Brachypodium database identified the highest number of proteins with 836, followed by the wheat orthogonal assembly with 804 and only 580 from NCBInr. The better performance of the Brachypodium database was also previously reported.76 It is also noticeable that the highest overlap between the databases is between the wheat orthogonal assembly and the Brachypodium database with 22.9% of the proteins identified. This is due to the fact that the wheat orthogonal assembly was constructed using genes from Brachypodium among other grasses.34 What is not shown in this Figure is the volume of sequence redundancy detected from each database search because the results were filtered out to include only the top protein with the best score as described in the methods. Of the proteins identified with Triticum NCBInr and the Brachypodium databases, 35 and 28% were redundant, respectively, for example, with the same set or subset of peptides matching the protein sequence. Less than 1% of the proteins identified were redundant with the wheat orthogonal assembly, which shows that the database was thoroughly filtered out for redundancy and constructed conservatively and thus more likely to be missing information regarding protein isoforms from the different genomes found in wheat. Although protein identification was performed here at the peptide level in a shotgun proteomics fashion, the multisearch database approach used led to the identification of the most representative proteome of wheat published to date.

Figure 2. Venn diagram of the proteins identified in wheat roots. The diagram shows the overlap between the results from three different databases: the wheat orthogonal assembly, the Brachypodium database, and the wheat NCBInr.

Figure 3. Distribution of proteins according to their cellular localization. The 1656 wheat root proteins identified in this study were classified according to their known or predicted cellular localization using Blast2Go (http://www.blast2go.com) program. 1692



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Figure 4. Functional distribution of the 1656 wheat root proteins. Proteins identified in wheat root were classified into 118 categories, further divided into 4 main categories for proteins involved in primary metabolism, secondary metabolism, genetic and environmental information processing, and cellular processes.

The 1656 proteins identified were annotated using Blast2GO according to the cellular localization, protein function and functional pathway (Supplemental Table 1 in the Supporting Information). The cellular localization annotation of the

proteins is summarized in Figure 3, which shows their unbiased distribution in different compartments. Twenty-four percent of the proteins were associated with membranes, 16% percent were cytosolic, and 7% were associated with the cell wall. 1693



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normal distribution. The median of the CV distribution was different for each ratio with 0.108, 0.094, and 0.059 for NesserCon/Opata-Con, Opata-ABA/Opata-Con, and Nesser-ABA/ Nesser-Con, respectively. The median of the CV calculated in this study is in the same range as the one reproducibly estimated for the iTRAQ-labeling experiment using the same laboratory conditions described in a previous study.77 According to the protein abundance changes observed between the iTRAQ ratios from these comparisons (NesserCon/Opata-Con; Opata-ABA/Opata-Con, and Nesser-ABA/ Nesser-Con), 1572 proteins exhibited a differential change in at least two replicates, as summarized in Table 2. These proteins were further classified into four main groups based on their variety and/or ABA dependency. Proteins that show differential abundance between the two varieties but no change in response to ABA (144) are cultivarspecific proteins but ABA-nonresponsive (group A). The ABAresponsive proteins were further classified into different subgroups: cultivar-independent ABA-responsive proteins (556) that similarly respond to ABA in both cultivars (group B); cultivar-dependent, ABA-responsive proteins (872) that show ABA-dependent abundance change in only one cultivar (group C); and proteins that exhibit opposite changes between the two cultivars (group D). Further classification of proteins based on similar, opposite, or cultivar-specific ABA-responsiveness is also shown in Table 2. To narrow down the number of proteins that change between the two genotypes and in response to ABA, only proteins with a biological variation of

Comparative Quantitative Proteomics Analysis of the ABA Response

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