Comprehensive analysis of the Brassica juncea root proteome in response to cadmium exposure by...

RESEARCH ARTICLE

Comprehensive analysis of the Brassica juncea

root proteome in response to cadmium exposure

by complementary proteomic approaches

Sophie Alvarez1*, Bertram M. Berla1*, Jeanne Sheffield1, Rebecca E. Cahoon1,Joseph M. Jez1, 2 and Leslie M. Hicks1

1 Donald Danforth Plant Science Center, St. Louis, MO, USA2 Department of Biology, Washington University, St. Louis, MO, USA

Indian mustard (Brassica juncea L.) is known to both accumulate and tolerate high levels of heavymetals from polluted soils. To gain a comprehensive understanding of the effect of cadmium(Cd) treatment on B. juncea roots, two quantitative proteomics approaches – fluorescence two-dimensional difference gel electrophoresis (2-D DIGE) and multiplexed isobaric tagging tech-nology (iTRAQ) – were implemented. Several proteins involved in sulfur assimilation, redoxhomeostasis, and xenobiotic detoxification were found to be up-regulated. Multiple proteinsinvolved in protein synthesis and processing were down-regulated. While the two proteomicsapproaches identified different sets of proteins, the proteins identified in both datasets areinvolved in similar biological processes. We show that 2-D DIGE and iTRAQ results are com-plementary, that the data obtained independently using the two techniques validate one another,and that the quality of iTRAQ results depends on both the number of biological replicates andthe number of sample injections. This study determined the involvement of enzymes such aspeptide methionine sulfoxide reductase and 2-nitropropane dioxygenase in alternatives redox-regulation mechanisms, as well as O-acetylserine sulfhydrylase, glutathione-S-transferase andglutathione-conjugate membrane transporter, as essential players in the Cd hyperaccumationand tolerance of B. juncea.

Received: May 30, 2008Revised: December 12, 2008

Accepted: December 19, 2008

Keywords:

Brassica juncea / Cadmium / DIGE / iTRAQ / Root proteome

Proteomics 2009, 9, 2419–2431 2419

1 Introduction

Classified by the U.S. Environmental Protection Agencyas a persistent bioaccumulative toxin, cadmium (Cd) isassociated with a range of human health problems,

including effects on the nervous system, reproductiveand developmental problems, and carcinogenic effects [1,2]. Cd is introduced as an environmental contaminant byindustrial activities such as smelting, mining, and petro-leum refining and by application of sewage sludge asfertilizer. Cd use in the industrialized world has declineddrastically in recent years due to environmental concerns.However, Cd contamination of agricultural soils frompast use remains a concern, as food is the primary routeof human absorption. Even low-level exposure can causekidney damage and bone density loss, and is associatedwith carcinogenesis [3, 4].

Correspondence: Dr. Leslie M. Hicks, Proteomics & Mass Spec-trometry Facility, Donald Danforth Plant Science Center, St.Louis, MO 63132, USAE-mail: [email protected]: 11-314-587-1224/-1324

Abbreviations: ATP, adenosine triphosphate; Cd, cadmium; PC,

phytochelatin; ROS, reactive oxygen species; SCX, strong cationexchange * These authors contributed equally to this work.

DOI 10.1002/pmic.200800478

© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

2420 S. Alvarez et al. Proteomics 2009, 9, 2419–2431

Phytoremediation of Cd contamination by plants capableof accumulating high levels of heavy metals may offer a wayto extract toxic substances from contaminated soils at a lowcost [5]. However, phytoremediators face a daunting chal-lenge: heavy metals, including Cd, are toxic to plants [6]. Cddamages the light-harvesting machinery [7, 8], interfereswith movement of potassium, calcium, and abscisic acid inguard cells [9], may deactivate proteins by displacing cationiccofactors [10], and stunts plant growth at sub-lethal levels[11]. An effective phytoremediator must both tolerate andaccumulate high levels of Cd while maintaining rapidgrowth in order to translocate high levels of contaminantinto easily removable above ground tissues.

For remediation of Cd-contaminated soils, Brassica jun-cea L. (Indian mustard) shows promise. The plant can accu-mulate .400 mg Cd/g dry weight in leaves over a 96-hr peri-od, although with some deleterious effects on plant growth[12]. It is possible that the tolerance of these plants could beimproved for phytoremediation applications. Many plantsuse phytochelatins (PCs), sulfur-containing peptides with arepeating g-glutamylcysteine moiety, to reduce Cd toxicity bycomplexation and subsequent sequestration in the vacuole[5, 13]. Plants increase production of PCs in response toheavy metal stress and their over-expression improves heavymetal tolerance [11], while phytochelatin-deficient Arabi-dopsis mutants are hyper-sensitive to Cd [14]. Counter-intuitively, PC overproduction can reduce Cd accumulationin B. juncea [11] and other species [15]. Because greater tol-erance does not improve accumulation, one possibility is thatuptake and translocation capacity, not toxicity, may limitaccumulation. This theory is supported by the observationthat abscisic acid, which causes stomatal closure and thuslimits mass flow through plant vascular tissues, prevents theaccumulation of Cd in B. juncea shoot tissue [13, 16, 17] andincreases Cd tolerance in a Cd-sensitive rice cultivar [17].However, different species sequester Cd in different tissuesand with different chelators [18, 19]; thus, potential remainsfor discovery-driven research to uncover new mechanismsimportant to heavy metal accumulation and tolerance.

The present study aims to determine which B. junceaproteins are differentially expressed in response to Cd-treatment. To achieve this aim, we used two quantitativelabeling proteomics approaches, 2-D DIGE and multi-plexed isobaric tagging technology (iTRAQ). 2-D DIGE is agel-based method that labels samples at the protein leveland iTRAQ is an LC-based method that labels samples atthe peptide level. The complementarity of these two tech-nologies allowed a more comprehensive analysis of the Cdresponse in B. juncea. Through each method, we identifieda number of differentially expressed proteins that functionin metal binding, redox homeostasis, stress response, pro-tein synthesis, and other cellular functions. We also com-pared the results produced by each proteomic strategy andthe reproducibility of protein quantification by thesemethods, a subject of great discussion in proteomics re-search [20–23].

2 Materials and methods

2.1 Chemicals and reagents

B. juncea (accession: PI 173874) seeds were a gift fromRobert Alan Stebbins of the USDA-ARS at the North CentralRegional Plant Introduction Station at Iowa State University(Ames, Iowa). HPLC-grade methanol and ACN were pur-chased from Fisher (Pittsburgh, PA). Formic acid andammonium formate were purchased from Acros (MorrisPlains, NJ). Unless otherwise indicated, all other chemicalsand reagents were purchased from Sigma-Aldrich (St. Louis,MO).

2.2 Plant growth conditions and protein extraction

B. juncea seeds were surface sterilized then germinated inliquid suspension media containing 0.256 Murashige-Skoog basal salt mixture (MP Biomedicals, Solon, OH)supplemented with 2% w/v sucrose and 16 Gamborg’svitamin solution. Plants were grown in a growth chamberunder 100 mmol/m2/s light, 16 h light/8 h dark cycle, and100% humidity at 247C. CdCl2 (250 mM) was added to theseedlings 13 days after planting. The roots of all plantswere harvested after 3 additional days of growth, flash-fro-zen in liquid nitrogen, and stored at –807C. At the time ofharvest, the Cd-treated plants showed signs of stressincluding reduced size and leaf chlorosis. Control andtreated groups consisted of three biological replicategroups of five plants per growth vessel for the DIGEexperiment and for the iTRAQ experiment.

Total protein was extracted from the root tissue of con-trol and Cd-treated plants with phenol. Briefly, the tissuewas ground into a fine powder under liquid nitrogen andan aliquot of 0.3 g was extracted with 600 mL of Tris-buf-fered phenol (pH 8.8) and 600 mL of 0.1 M Tris-HCl with10 mM EDTA, 0.4% v/v 2-mercaptoethanol, and 0.9 Msucrose. Samples were vortexed for 30 min, centrifuged for10 min at 50006g, and the phenol phase was collected. Anadditional 400 mL of phenol was added to the aqueousphase and centrifugation was repeated. The back-extractedphenol phase was then removed and combined with theprevious collection. The combined phenol layer was pre-cipitated overnight in five volumes of 0.1 M ammoniumacetate in 100% methanol at –207C. The protein was col-lected by centrifugation at 20 0006g and washed twicewith 0.1 M ammonium acetate in 100% methanol, oncewith 80% acetone, and once with 70% methanol. The pro-tein pellet was briefly air-dried and then resuspended ineither DIGE solubilization buffer (7 M urea, 2 M thiourea,4% w/v CHAPS, 0.2% w/v SDS, 10 mM Tris, pH 8.5) or0.5 M bicine (pH 8.4) with 0.09% w/v SDS for iTRAQlabeling. Protein concentration was determined using theCB-X protein assay (Genotech, St. Louis, MO) according tothe manufacturer’s protocol.


Proteomics 2009, 9, 2419–2431 2421

2.3 2-D DIGE

The 2-D DIGE gels of control and Cd-treated B. juncea rootprotein were prepared in triplicate. Protein samples werelabeled according to the manufacturer’s protocol for mini-mal labeling (GE Healthcare, Piscataway, NJ). Briefly, pro-tein concentrations were adjusted to 5 mg/mL by addingsolubilization buffer. CyDyes (1 mL of 600 pmol/mL indimethyl formamide) were added to 15 mL (75 mg) proteinaliquots and incubated on ice in the dark for 30 min. Foreach replicate reaction, one sample was labeled with Cy3,the other with Cy5 and the internal standard with Cy2. Twocontrol and two Cd-treated samples were labeled with Cy5and Cy3, respectively. For the third replicate, the CyDyeswere swapped in order to correct for the bias introduced bythe different fluorescence characteristics of acrylamide atthe different wavelengths of excitation of Cy3 and 5. Theinternal standard labeled with Cy2 consisted of a pooledsample comprised of equal amounts of each control andCd-treated samples used for each replicate. The reactionswere terminated by adding 1.5 mL of 10 mM lysine andincubating for an additional 10 min. Each labeled proteinsample was diluted with 43 mL of DeStreak RehydrationSolution (GE Healthcare). For each replicate, the controland Cd-treated samples and the pooled internal standardswere then combined and the 180 mL sample containing225 mg of protein was loaded onto Bio-Rad (Hercules, CA)ReadyStrip IPG Strips (11 cm, pH 3–10 nonlinear) viapassive rehydration. The IEF was carried out using a Bio-Rad Protean IEF cell at 250 V for 0.5 h, 500 V for 1 h,8000 V for 2.5 h, and then the voltage was held at 8000 Vover 35 000 Vh at 207C. Prior to SDS-PAGE, the focusedIPG strips were equilibrated for 10 min each in Bio-RadReadyPrep 2-D Starter Kit Equilibration buffers I and II.SDS-PAGE was performed on Bio-Rad Criterion pre-castgels (8–16% Tris-HCl, 1.0 mm) in a Bio-Rad CriterionDodeca Cell run at 60 V for 15 min, and then 200 V for1 h. The gels were fixed in 10% methanol/7% acetic acidfor 1 h.

2.4 2-D Image analysis

Gel images were acquired using a Typhoon 9410 variablemode imager (Amersham Biosciences, Pittsburgh, PA). Eachgel was imaged sequentially at excitation/emission filterwavelengths (in nm) of 488/520 for Cy2, 532/580 for Cy3,and 633/670 for Cy5. Image analysis was carried out usingSameSpots software (Nonlinear Dynamics, Durham, NC).The software was used to perform gel alignment, spot aver-aging and normalization, and multivariate statistics to deter-mine which protein spots changed in abundance in responseto Cd-treatment.

Means and SD were calculated from three independentsets of harvests and compared between control and Cd-treat-ment. The number of detected spots showing differenceswith a p-value of ,0.05 was then determined.

2.5 In-gel trypsin digestion

After imaging, the gels were stained with SYPRO Ruby pro-tein gel stain (Molecular Probes, Eugene, OR) according tothe manufacturer’s protocol so that spots could be visualizedby the GelPix protein spot excision system (Genetix, Boston,MA). Spots identified as significantly differentially expressedin response to Cd treatment by SameSpots were excised onthe GelPix. A 50 mM ammonium bicarbonate/50% ACN so-lution was used to destain the gel spots prior to digestion by12-h incubation at 377C in 30 mL of 5.9 ng/mL trypsin (Pro-mega, Madison, WI). Peptide extraction was performed intwo rounds; first with 2% ACN, 1% formic acid, and thenwith 60% ACN. Before nano-LC-MS/MS, samples were lyo-philized, and then dissolved in 8 mL of 5% ACN and 0.1%formic acid.

2.6 The iTRAQ labeling and strong-cation exchange

Control and Cd-treated B. juncea root protein were pre-pared in triplicate for the iTRAQ experiment. To preparesamples for iTRAQ labeling, phenol extracted proteinfrom each of the treated and control plant groups wasdissolved in 0.5 M bicine (pH 8.4), 0.09% SDS. A 105-mLaliquot containing 98.8 mg of each sample was reducedand oxidized using iTRAQ kit reagents (Applied Biosys-tems, Foster City, CA), according to the manufacturer’sprotocol. Samples were diluted to 200 mL with MilliQwater and digested for 16 h at 377C with 10 mg trypsin.After lyophilization, the samples were dissolved in 30 mLof 0.5% SDS in 0.5 M bicine, pH 8.4 and labeled withiTRAQ isobaric reagents according to the manufacturer’sinstructions. This alternate dissolution buffer was usedbecause some extracted proteins were insoluble in thestandard iTRAQ dissolution buffer (0.5 M triethylammo-nium bicarbonate, pH 8.5). Control samples were labeledwith reagent 117; treated samples were labeled withreagent 116. The six labeled digests were pooled intopairs each containing a control and treated sample lead-ing to three replicates. The samples were lyophilized andthen resolubilized in 225 mL of 30% ACN, 5 mM ammo-nium formate (pH 2.7) for separation by strong-cationexchange (SCX). For SCX, 220-mL injections of iTRAQ-labeled pooled peptides were fractionated on a SystemGold HPLC (Beckman Coulter, Fullerton, CA) equippedwith a Polysulfoethyl column (4.6 mm id6100 mm,5 mm, 200 Å, The Nest Group, Southborough, MA) flow-ing at 1 mL/min. The gradient was from 100% solvent A(30% ACN, 5 mM ammonium formate, pH 2.7) to 25%solvent B (30% ACN, 1 M ammonium formate, pH 3) in15 min, then increased from 25–60% B in 5 min, andfinally holding at 60% B for 5 min. Fractions were col-lected every 0.5 min, lyophilized, and dissolved in 20 mLof 5% ACN, 0.1% formic acid. Fractions collected from6–23.5 min were analyzed by nano-LC-MS/MS.



2.7 Protein identification by nano-LC-MS/MS

Protein digests extracted from excised DIGE gel spots andSCX fractions of iTRAQ-labeled total protein digests weresubjected to nano-LC-ESI-MS/MS analysis. Nano-LC wasperformed with an LC Packings Ultimate system (San Fran-cisco, CA) equipped with a Dionex C18 PepMap100 column(75-mm id) (Sunnyvale, CA) flowing at 180 nL/min. Peptides(5-mL injections) were resolved on a gradient that started at95% solvent A (5% ACN, 0.1% formic acid) and 5% solvent B(95% ACN, 0.075% formic acid in MilliQ water) for 3 min,then increasing from 5–25% B over 5 min, from 25–60% Bover the next 32 min, and from 60–95% B over the final5 min. To increase both proteome and protein coverage, eachreplicate (A, B and C) of iTRAQ-labeled SCX fractions wasinjected three times each. The MS analysis was performedon an ABI QSTAR XL (Applied Biosystems/MDS Sciex)hybrid QTOF MS/MS mass spectrometer equipped with ananoelectrospray source (Protana XYZ manipulator). Posi-tive mode nanoelectrospray was generated from fused-silicaPicoTip emitters with a 10-mm aperture (New Objective,Woburn, MA) at 2.5 kV. The m/z response of the instrumentwas calibrated daily with manufacturer’s standards. TOFmass and product ion spectra were acquired using informa-tion-dependent data acquisition (IDA) in Analyst QS v1.1with the following parameters: mass ranges for TOF MS andMS/MS were m/z 300–2000 and 70–2000, respectively. Everysecond, a TOF MS precursor ion spectrum was accumulated,followed by three product ion spectra, each for 3 s. Theswitching from TOF MS to MS/MS is triggered by the massrange of peptides (m/z 300–2000), precursor charge state (2–4) and ion intensity (.50 counts). The DP, DP2, and FP set-tings were 60, 10, and 230, respectively, and rolling collisionenergy was used.

2.8 2-D gel protein database search

The peptide MS/MS spectra were processed using AnalystQS software v1.1 (Applied Biosystems) and searched againstthe NCBI Arabidopsis Protein database (138 417 sequences,July 21, 2008) using an in-house version of MASCOT v2.20(Matrix Science Inc, Boston, MA) [24]. The Arabidopsis pro-tein database was used due to the high degree of homologybetween Arabidopsis and B. juncea, which lacks a sequencedgenome. The following parameters were selected: trypticpeptides with �1 missed cleavage site; precursor and MS/MS fragment ion mass tolerance of 2.0 and 0.8 Da, respec-tively; fixed carbamidomethylation of cysteine; and variableoxidation of methionine. Positive identification was deter-mined based on the following criteria:�2 peptide sequences;protein sequence coverage; total MASCOT and individualion scores (http://www.matrixscience.com/help/ scoring_help.html); and MS/MS spectral quality judged by a full-length y-ion series of peptides comprising at least six con-secutive amino-acid sequence tags with no missed cleavages.

2.9 The iTRAQ protein database search and

quantification

Protein Pilot v2.0.1 (Applied Biosystems) was used to simul-taneously identify and quantify proteins in the iTRAQexperiments. Searches were against the NCBI Arabidopsisprotein database (138 417 sequences, July 21, 2008) con-catenated with a decoy database containing the randomizedsequences of the original database. As peptides from eachreplicate were each injected three times, the spectra fromthese triplicates were combined for searching. The search wasperformed in “Thorough ID” mode with a 95% confidencelevel (protscore = 1.30) and specifying iTRAQ peptide label-ing, Cys oxidized by methyl methanethiosulfonate (MMTS),and trypsin digestion. Protein Pilot groups proteins from thedatabase which match shared similar peptides into‘progroups.’ In comparing protein ID and quantificationacross biological replicates, progroups that contained at leastone common accession were matched manually. Progroupswith protscore�2 (i.e. p .0.01) were accepted from the anal-ysis, with a false positive rate ,1.2%, calculated as the per-centage of progroups which matched randomized accessionsin the concatenated database. Because of technical issuesrelated to the chromatographic separation when fractionsfrom the third replicate (C) were running, the MS spectra anddatabase searching results revealed to be of poor quality andcould not be used in the final analysis. Correction factors forsample bias were 0.9248 for A and 0.8045 for B. To assess theefficacy of multiple injections in the number of total proteinsidentified, proteins identified based on 1, 2, or 3 injectionswere compared using the same criteria to match progroups.In cases where proteins were identified in one replicate butnot the other, we manually inspected sequences between ho-mologues identified in different replicates to find progroupsthat might actually give evidence of the same protein. Con-fidence in quantifications was assessed using the p-value(,0.05) and an error factor (,2) generated by Protein Pilot.

3 Results

3.1 Identification of protein abundance changes

induced by Cd-treatment via DIGE

Proteins extracted from the roots of control and Cd-treated B.juncea plants were labeled separately with spectrally resolv-able DIGE labels, then combined and separated by 2-DE(Fig. 1, left). Three biological replicates were analyzed. Afterimaging, SameSpots software was used to detect proteinlevels that were significantly up- or down-regulated by Cd-treatment in the three replicate gels. Sixty-nine spots weredifferentially expressed in response to Cd-treatment, with 43decreased and 26 increased in abundance. The spots werethen excised, digested and analyzed by nano-LC-MS/MS(Fig. 1, left). A total of 102 proteins were identified in the 69spots.


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Figure 1. Experimental schemeof the DIGE and iTRAQ analysesperformed on the control andCd-treated Brassica juncearoots.

Several proteins were identified in multiple spots as differentisoforms of the same protein. Seventy-six different proteinswere identified and only 37 spots that yielded a single con-fident identification were used for the quantitative data anal-ysis (Table 1). Proteins were identified from both primarymetabolism (e.g. carbohydrate, protein, and amino-acid me-tabolism) and energy metabolism. We also identified pro-teins involved in thiol metabolism (cysteine and methioninesynthesis), oxidative stress (ascorbate peroxidase andmethionine-sulfoxide reductase), and detoxification enzymes(GST) (Table 1; peptide details in Supporting InformationTable 1). Several isoforms of the same protein (#150 and 151;#76 and 81; # 83, 86 and 95; #139, 184 and 185 in Table 1)

were identified in adjacent spots; often these changes were inthe same direction, but sometimes the altered expressionwas in the opposite direction. These adjacent spots likelycontained different PTM forms of the protein, whose relativeabundance was altered by Cd-treatment (Fig. 2).

3.2 Identification of protein abundance changes

induced by Cd-treatment via iTRAQ

After extraction of the proteins by the same procedure usedfor DIGE, the samples were digested and the peptides fromeach sample were labeled with different isobaric tags (Fig. 1,right). Three biological replicates were labeled, samples were



Table 1. Proteins identified as differentially expressed using the DIGE method

Spot MASCOTscore

Peptidesmatched

gi Accessionnumber

Name Fold-change

Biological process

150 280 7 2244749 Hydroxymethyltransferase 0.8* Amino acid metabolism151 399 7 2244749 Hydroxymethyltransferase 0.8* Amino acid metabolism74 144 4 3080435 Alanine:glyoxylate aminotransferase

2 homolog0.7* Amino acid metabolism

165 277 5 693690 Aspartate aminotransferase 0.8** Amino acid metabolism88 328 6 693690 Aspartate aminotransferase 0.7*** Amino acid metabolism73 94 2 1419388 Stromal ascorbate peroxidase 0.7* Antioxidant mechanism

168 239 6 16173 L-Ascorbate peroxidase 0.8* Antioxidant mechanism105 93 3 7340648 Monodehydroascorbate reductase

(NADH)-like protein1.4* Antioxidant mechanism

102 99 2 7576184 Peptide methionine sulfoxidereductase-like protein

1.4** Antioxidant mechanism

75 490 9 4589962 Putative fructokinase 0.7* Carbohydrate metabolism126 465 11 6721173 Glyceraldehyde-3-phosphate

dehydrogenase C subunit (GapC)0.8** Carbohydrate metabolism

76 150 4 7329685 Transketolase-like protein 1.5* Carbohydrate metabolism81 103 3 7329685 Transketolase-like protein 1.5* Carbohydrate metabolism83 236 4 7529717 Fructose bisphosphate aldolase-like protein 0.7* Carbohydrate metabolism86 633 10 7529717 Fructose bisphosphate aldolase-like protein 0.7* Carbohydrate metabolism95 461 8 7529717 Fructose bisphosphate aldolase-like protein 1.5** Carbohydrate metabolism98 113 2 7572929 Alpha-galactosidase-like protein 1.5* Carbohydrate metabolism

139 120 2 984052 Thioglucoside glucohydrolase 1.3* Defense mechanisms184 187 6 984052 Thioglucoside glucohydrolase 0.9* Defense mechanisms185 128 2 984052 Thioglucoside glucohydrolase 0.9* Defense mechanisms93 123 3 9758561 Vegetative storage protein-like 0.7* Defense mechanisms

171 669 12 13548325 H1-transporting ATP synthase betachain-like protein

1.2* Energy metabolism

176 247 6 14334734 Putative NADH-ubiquinone oxireductase 0.8* Energy metabolism148 554 11 6652886 26S proteasome AAA-ATPase subunit RPT5a 0.8* Protein degradation92 241 5 2511594 Multicatalytic endopeptidase complex,

proteasome precursor1.4** Protein degradation

94 170 4 166662 10 kDa chaperonin 0.7* Protein synthesis/processing44 124 2 3420055 Cyclophilin 0.6* Protein synthesis/processing38 331 7 6056373 Elongation factor EF-2 0.6* Protein synthesis/processing

137 130 3 6630450 Putative TPR-repeat protein 0.8* Protein synthesis/processing122 260 6 11994556 S-adenosyl methionine synthase 0.8* Secondary metabolism167 95 3 2286069 Beta-glucosidase 0.8* Secondary metabolism182 391 8 4883604 Putative s-adenosylmethionine synthetase 0.9* Secondary metabolism65 82 3 2738248 Cobalamin-independent methionine synthase 1.5* Thiol metabolism87 196 3 804950 O-acetylserine sulfhydrylase

(cysteine synthase)1.5** Thiol metabolism

99 225 5 2289095 WD-40 repeat protein 0.7* Signaling pathway1 82 2 166723 Glutathione S-transferase 3.2** Xenobiotic detoxification

13 100 2 166723 Glutathione S-transferase 2.1* Xenobiotic detoxification

The MASCOT score and number of matched peptides are indicated. The asterisks indicate significant changes in protein abundance(*p ,0.05; **p ,0.01; ***p ,0.001). The proteins were categorized according to their annotated biological process.

pooled appropriately, and the peptides separated by SCX.The fractions from each replicate were then injected threetimes into the nano-LC-MS/MS system and data analysis wasperformed using Protein Pilot (Fig. 1, right). The advantageof multiple LC-MS/MS injections of a single sample wasevaluated by comparing the number of proteins identified in

replicate A by one, two, or three injections (Fig. 3). With oneinjection, an average of 334 progroups was identified. Whencombining the data from two of the three injections, anaverage of 449 progroups were identified, translating to anaverage of 118 new progroups and 331 common to bothinjections. We observed that progroups defined by only one


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Figure 2. The 2-D gel proteomic map of B. juncea roots comparingcontrol and cadmium treatment. (A) Spots with a significantintensity change and with one single protein identification areindicated. (B) Enlargement of panels 1 and 2 from the internalstandard, the Cd-treated and the control of the three replicate gels.

Figure 3. Venn diagram showing the reproducibility of the iden-tification and quantification between the A and B iTRAQ repli-cates. A total of 585 proteins were identified, with 365 identifiedin both biological replicates. One-hundred-thirty proteins out of365 were quantified in the same way (quantified and agreed, Q &A). The rest were either quantified but with opposite changes(quantified and disagreed, Q & D) or quantified but with no sig-nificant differences in both replicates and one of the replicate(quantified and not significant, Q & NS), or not quantifiable (NQ).

injection could be rearranged into new progroups with thenew peptide information obtained by the second and thirdinjection. When combining all three injections, 59 addi-tional proteins to the 445 proteins common to two injec-tions were identified, showing 50% diminished returns overthe increase from two injections, yet bringing the totalnumber of progroups identified to 504 for biological repli-cate A.

As mentioned in Section 2, the third biological replicate ofthe iTRAQ experiment could not be used in the final analysisbecause of technical issues that affected the quality of the data.After merging all the data from the multiple injections andcomparing the proteins identified from both biological repli-cates A and B, 585 proteins or progroups were confidentlyidentified. Of those, 365 were reproducibly identified in A andB (Fig. 4). The progroups sharing the same set of peptides, butwith a different top protein hit between replicates A and B,were considered identifications of the same protein. Of the365 progroups, 338 were quantifiable (i.e. with at least threelabeled peptide spectra) in both replicates and 152 displayedsignificant differences (p-value ,0.05). Of those 152, 65 wereup-regulated following Cd exposure, 65 were down-regulatedand 22 showed opposite changes in both replicates (Fig. 4).Quantification for 186 different progroups of the 338 quanti-fiable was not used for the analysis because of high variabilityin the labeling quantification (p-value .0.05). Of these 186,the ratios of 124 quantified progroups were not significant inone of the replicates and the ratios of 62 were not significant inboth replicates. Finally, 27 progroups were not quantifiable inat least one replicate because less than three peptide spectramatched uniquely to that progroup. These quantification dis-agreements, essentially caused by both biological and techni-cal variation, show the importance of running replicates iniTRAQ experiments in order to discriminate the changesspecifically related to the treatment. The iTRAQ experimentidentified and quantified 130 proteins differentially expressedin response to Cd-treatment (Supporting InformationTable 2). The proteins up-regulated by Cd-treatment are

Figure 4. Histogram showing the number of proteins identifiedwith 1, 2 and 3 injections of iTRAQ fractions from replicate A.



Table 2. Selected proteins identified as differentially expressed by the iTRAQ method

Pro-Group

Prot-Score

%Coverage

Accessionnumber

Name Foldchange

Standarderror

Biological process

46 13.61 36 gi)5263328 Similar to gb)Z11499 protein disulfideisomerase

0.89 0.07 Antioxidant mechanism

150 6 23 gi)7269360 Superoxide dismutase (EC 1.15.1.1)(Fe)(fragment)

1.30 0.02 Antioxidant mechanism

203 4.08 34 gi)9759401 2-Nitropropane dioxygenase-like protein 2.20 0.48 Antioxidant mechanism209 4.65 13 gi)3395433 Peroxidase 3.01 0.63 Antioxidant mechanism212 4.26 14 gi)7529712 Monodehydroascorbate reductase

(NADH)-like protein1.33 0.00 Antioxidant mechanism

304 2.18 24 gi)9758439 Peroxidase 0.67 0.05 Antioxidant mechanism420 2 33 gi)5689611 Copper/zinc superoxide dismutase 0.86 0.08 Antioxidant mechanism

3 46.43 65 gi)6478919 Thioglucosidase 3D precursor 1.09 0.08 Defense mechanism87 9.15 17 gi)9279645 Jasmonate inducible protein;

myrosinase binding protein-like1.68 0.20 Defense mechanism

98 10.16 43 gi)15241841 Glycosyl hydrolase family 1 protein 1.26 0.31 Defense mechanism132 6.13 24 gi)6694745 Myrosinase-binding protein-like protein 1.13 0.15 Defense mechanism151 6.11 32 gi)3482931 Germin-like protein 5.03 0.98 Defense mechanism191 4.64 12 gi)79324051 Jacalin lectin family protein 1.62 0.18 Defense mechanism219 4.32 15 gi)9279641 Jasmonate inducible protein;

myrosinase binding protein-like1.64 0.03 Defense mechanism

240 3.98 21 gi)871992 Thioglucosidase 0.86 0.00 Defense mechanism382 2.26 14 gi)3402676 Putative myrosinase-binding protein 0.47 0.06 Defense mechanism181 5.4 12 gi)75318687 Multidrug resistance protein 4

(P-glycoprotein 4)1.38 0.32 Transport

90 8.85 41 gi)2584721 Sulfite reductase 1.12 0.04 Thiol metabolism100 8.18 36 gi)110740313 Cytosolic O-acetylserine(thiol)lyase 1.29 0.23 Thiol metabolism29 16.19 70 gi)3201613 Glutathione S-transferase 2.18 0.47 Xenobiotic detoxification

122 7.38 24 gi)8052535 Similar to ATGSTU22 (Arabidopsisthaliana Glutathione S-transferase(class tau) 22) [BLAST]

2.24 0.37 Xenobiotic detoxification

137 8.71 46 gi)6056409 Glutathione S-transferase 1.25 0.05 Xenobiotic detoxification140 6.38 38 gi)7268107 Selenium-binding protein like 1.83 0.06 Xenobiotic detoxification211 6.75 31 gi)8052534 Similar to ATGSTU21 (Arabidopsis

thaliana Glutathione S-transferase(class tau) 21) [BLAST]

2.61 0.77 Xenobiotic detoxification

Protscore (estimate of confidence for protein identifications equal to –log (1-% confidence)) and percentage of coverage are indicated.Reported proteins had differential expression (p ,0.05) in both replicates and identifications were 99% confident (protscore .2). The foldchange reported is the mean of the fold-change from the two replicates of iTRAQ runs.

associated with sulfur metabolism, defense/stress respon-ses, and metabolic “housekeeping”. Down-regulated proteinsare mostly associated with protein synthesis/processing orcarbohydrate metabolism. A selection of proteins identified,which are elaborated in Section 4, is presented in Table 2.

3.3 Comparison of Cd-affected proteins determined

from DIGE and iTRAQ approaches

When compared to DIGE, iTRAQ identified twofold moreproteins differentially expressed, with 130 progroups versus69 spots from the gel-based method. Twenty-one percent (16/76) of the proteins identified by DIGE were also identified byiTRAQ. Because of multiple protein identifications from in-dividual 2-D-gel spots, only 37 of those 102 proteins yielded

useful quantitative data. Thus, the proteins identified by bothDIGE and iTRAQ were reduced to a 12% overlap. Twentyproteins were identified only using the DIGE approach. Onlya few of the common identifications showed similar regula-tion between DIGE and iTRAQ, for example the O-acetyl-serine sulfhydrylase (#87 in Table 1, #100 in Table 2) and themonodehydroascorbate reductase (NADH)-like protein(#105 in Table 1, #212 in Table 2). Although there are differ-ences in specific proteins identified between the two ap-proaches, the metabolic processes in which the proteins areinvolved are similar and for the most part show the samechange in expression levels (Fig. 5). The main metabolicsystems commonly identified by both proteomics ap-proaches were the antioxidant response mechanism, xeno-biotic detoxification, sulfur metabolism, stress-defense


Proteomics 2009, 9, 2419–2431 2427

Figure 5. Distribution of thedown-and up-regulated proteinidentified in DIGE and iTRAQbased on their biological func-tion.

mechanisms, carbohydrate metabolism, and protein synthe-sis/processing. Both approaches mainly showed a down-regulation of proteins involved in protein synthesis/proces-sing and an up-regulation of proteins involved in sulfur me-tabolism and xenobiotic detoxification (Fig. 5). However, theproteins involved in defense mechanism showed an oppositepattern, with a major up-regulation according to the iTRAQdata and with most of them down-regulated according to theDIGE approach. In addition, proteins involved in additionalcategories were only identified by iTRAQ (Fig. 5). Thoseproteins were either membrane proteins involved in energymetabolism and transport or normally-low abundance pro-teins such as nucleic acid-binding proteins or signaling pro-teins (Supporting Information Table 2).

4 Discussion

The proteins identified as differently expressed throughboth proteomic approaches were merged in order todetermine a comprehensive list of mechanisms alteredby Cd-treatment. The discussion will focus on four majorevents that seem to take place after Cd-treatment. Theexpression changes were in sulfur compound synthesis,redox homeostasis, myrosinase-mediated defense andprotein processing. Although those mechanisms werepreviously reported to be common among various abioticstresses in plants [25], the use of the proteomics ap-proaches led to the identification of new candidate pro-teins that are essential players in the Cd-stress responsein B. juncea. Below, we discuss the involvement of theproteins in the metabolism adaptation of B. juncea in re-sponse to Cd exposure. We also discuss the differencesobserved when using gel-based and LC-based proteomicapproaches.

4.1 Cd-induction of thiol metabolism: relation to

heavy metal detoxification

Sulfur-containing compounds play a critical role in protect-ing plants against heavy metals by providing a sequestrationsystem. Previous reports have shown induction of enzymesinvolved in sulfate assimilation by aluminum and Cd-treat-ment in rice and Arabidopsis roots [26, 27]. In our study, twodistinct proteomic approaches identified an increased abun-dance of proteins involved in sulfur metabolism. Sulfitereductase (#90 in Table 2) and O-acetylserine sulfhydrylase(#87 in Table 1, #100 in Table 2), which are enzymes involvedin the reduction of sulfate to cysteine (Fig. 6), were over-expressed in the Cd-treated samples. One isoform of cobala-min-independent methionine synthase (#65 in Table 1),which converts homocysteine to methionine, was also up-regulated in response to Cd exposure (Fig. 6). The inductionof enzymes involved in assimilation of sulfate into cysteineand methionine suggests that sulfate uptake also likelyincreases in response to Cd, as shown previously in the rootsof B. juncea and maize [28]. Under Cd stress, sulfate avail-ability for synthesis of heavy metal binding peptides likeglutathione and phytochelatins [5, 13] may limit Cd tolerance[29]. Although neither glutathione synthetase nor phytoche-latin synthase was detected in our study, others have foundmore than a 1000-fold increase in B. juncea root phytochela-tin levels without a concomitant increase of the synthaseprotein [30]. Induction of several GST (#1 and 13 in Table 1;#29, 122, 137 and 211 in Table 2), observed by both DIGEand iTRAQ, suggests increased glutathione-mediated heavymetal detoxification in response to Cd-treatment. Theobserved increase in Cd detoxification capacity via sulfurcompounds offers one explanation for the Cd tolerance of B.juncea, and a potential avenue for improving Cd uptake ca-pacity by engineering plants with increased sulfate uptakeand assimilation capacity (Fig. 6).



Figure 6. Summary of the biological functions affected by Cd-treatment in B. juncea revealed by proteomics approaches. Five main cate-gories of metabolism were identified with significant protein expression changes using DIGE and iTRAQ proteomics approaches. Most ofthe proteins involved in thiol metabolism, defense mechanism, xenobiotic detoxification, and antioxidant mechanism showed an increasein protein abundance (1). Within the housekeeping metabolism, the abundance of proteins involved in ATP production (energy metabo-lism) increased (1) while abundance of proteins acting in protein synthesis/processing decreased (–).

4.2 Cd-related induction of oxidative stress and

alteration of redox homeostasis

Each proteomic approach in this study found both decreasesand increases of various reactive-oxygen species (ROS)-scavenging enzymes following Cd exposure (Fig. 5). Withinthe peroxidases, several isoforms were up-regulated whileothers were down-regulated in response to Cd exposure.DIGE revealed that the protein abundance of two ascorbateperoxidases isozymes involved in the ascorbate-glutathionecycle decreased (#73 and 168 in Table 1). The iTRAQ showedthat one peroxidase isoform was down-regulated (#304 inTable 2), while another isozyme was up-regulated (#209 inTable 2). Both peroxidases identified using iTRAQ weredetermined to be secreted peroxidases, each containing a sig-nal peptide sequence according to SignalP 3.0 [31]. Becausethe expression of ascorbate peroxidases involved in ROSdetoxification is down-regulated and because most of theidentified peroxidases are cell wall-localized, their function inresponse to Cd is unlikely to be in ROS detoxification in re-sponse to Cd-induced oxidative stress shown to be occurringin Brassica [32]. The decrease in peroxidase expression indi-cates likely the increase in H2O2 levels. In addition to perox-

idases, iTRAQ also showed Cd regulation of specific super-oxide dismutase isozymes (SOD; #150 and 420 in Table 2).SOD convert superoxide to molecular oxygen and H2O2

(Fig. 6). While a Fe-containing SOD increased in abundanceby 1.3 times in response to Cd, a Cu/Zn-SOD decreasedslightly in abundance. Oxidative stress and hormonal treat-ments elicit differential expression of genes encoding Fe- andCu/Zn-SOD in tobacco leaves, suggesting that Fe-SODresponds more effectively than Cu/Zn-SOD to oxidativestresses [33]. The Cd-induced ROS accumulation may speci-fically induce Fe-SOD. Alternatively, Cd may limit Cu and Znuptake, as shown in rice leaves [34], thus limiting Cu/Zn-SODproduction and inducing Fe-SOD production. Interestingly, agermin-like protein was also identified with the iTRAQmethod (#151 in Table 2) and showed a 5.0-fold abundanceincrease in Cd-treated plants compared to control plants. Al-though germin-like proteins are similar to oxalate oxidases insequence, some germins display Mn-SOD activity instead ofoxalate oxidase activity [35]. If this is the case, it will be the firstreport of Mn-SOD response to Cd in roots of B. juncea.

The Cd-mediated repression of peroxidases and the shiftsin SOD abundances indicate the possible accumulation ofH2O2(Fig. 6). However, except for the monodehydroascorbate


Proteomics 2009, 9, 2419–2431 2429

reductase (#105 in Table 1 and #212 in Table 2), there is noevidence that any other antioxidant enzymes from the ascor-bate-glutathione cycle are up-regulated, as shown previouslyin B. juncea [27]. However, alternatives enzymes involved inredox homeostasis at the molecular level showed an increasedexpression in response to Cd stress (Fig. 6). First, the peptidemethionine sulfoxide reductase (#102 in Table 1), an enzymeinvolved in redox homeostasis of ROS-damaged proteins out-side of the ascorbate-glutathione cycle, was up-regulated. Thisenzyme repairs oxidatively damaged proteins by reducingmethionine sulfoxides back to methionine [36]. In E. coli, thisreductase is required for Cd tolerance [37], but no previousreports have shown the involvement of methionine sulfoxidereductase in plant Cd tolerance. Another enzyme that wasinduced in Cd-treated plants was a 2-nitropropane dioxygen-ase (#203 in Table 2). This enzyme, which was 2.2-foldinduced, catalyses the oxidative denitrification of nitroalkanesto the corresponding aldehydes, while producing H2O2 andNO2

- in Neurospora crassa [38]. This protein was also pre-viously found to increase in response to Cd in Arabidopsisroots [26], possibly indicating a role for this enzyme in detox-ifying nitro-compounds that accumulate in response to Cd.

4.3 Cd-induction of myrosinase-based defense

mechanisms

Several myrosinases (thioglucoside glucohydrolases) andassociated proteins were identified by both proteomicapproaches. DIGE identified one induced (#139 in Table 1)and two repressed (#184, 185) myrosinase isoforms, whileiTRAQ found two induced (#3 and 98 in Table 2) and onerepressed (#240). The iTRAQ experiment also identifiedseveral myrosinase-binding proteins (MBP): four out of fivewere induced (#87, 132, 191, 219 and 382 in Table 2). Myr-osinases catalyze the degradation of glucosinolates intocompounds with biological activities against pests andpathogens [39], but also can respond to requirements for thenitrogen and sulfur contained in glucosinolates. An earlierreport showed that a lower-level, more brief Cd exposure(than used in this study) did not affect myrosinase (MYR1)expression at the transcriptional level, suggesting that glu-cosinolates did not act as a sulfur source for glutathione orphytochelatins under this stress [40]. This inconsistency maybe explained by the different substrate specificity of myr-osinase isoenzymes. Only a few studies address the charac-terization of different myrosinase isoenzymes and very littleis still known. The work from James and Rossiter [41]showed that two myrosinases could degrade different gluco-sinolates at different rates. The substrate specificity of themyrosinases could also be affected by associated factors suchas the myrosinase-binding protein that were also identifiedby iTRAQ. In our study, Cd-treatment induced specificmyrosinases expression and myrosinase-binding proteins inthe aim of degrading specific glucosinolates, which will beused as sulfur sink or an undetermined function (Fig. 6).

4.4 Cd-induced repression of protein metabolism

The DIGE and iTRAQ approaches showed that protein me-tabolism, including proteins involved in protein synthesisand processing, was down-regulated overall in the Cd-treatedplants (Fig. 5). The widespread down-regulation of proteinsinvolved in protein synthesis and processing, which aremajor consumers of adenosintriphosphate (ATP) and nutri-ents, may indicate a slowing of cellular processes in theroots. In maize, it has been shown previously that rootgrowth is stunted and abnormal during Cd exposure, moreso than that of shoots [42]. In maize, .80% of Cd is stored inshoots [18], so this allocation of resources to growth of theshoot may serve to increase cadmium sink capacity relativeto uptake capacity to avoid deleterious or lethal levels of con-taminant in the shoot. Alternatively, down-regulation ofribosomal protein synthesis may indicate a redeployment ofresources to meet the greater demand for amino acids in thenon-ribosomal peptide synthesis of glutathione and phy-tochelatins for Cd complexation (Fig. 6). One member of theABC (ATP-binding cassette) membrane transporters family(multidrug resistance protein) known to transport con-jugates of glutathione in vacuoles [43] was found to be up-regulated (#181 in Table 2) by the iTRAQ method, indicatingthat ATP resources are critical for the vacuolar sequestrationof Cd-glutathione conjugate.

4.5 DIGE and iTRAQ: complementary proteomics

approaches

DIGE and iTRAQ are known in the proteomics field aspowerful techniques in studying comprehensive proteinchanges. In DIGE, the pooling of samples into one analysishas the aim of reducing experimental variation and raisingthe confidence of protein changes by running higher num-ber of biological replicates within fewer gels. While iTRAQ,an LC-based approach, was supposed to progressivelyreplace the gel-based DIGE, comparison studies of bothtechniques have revealed that these two techniques arecomplementary [23]. In our study, only 12% of the proteinsdifferentially expressed in DIGE were also identified as dif-ferentially expressed in iTRAQ. The complementary dataresult from the differences in sample preparation and ana-lytical methods used by the two approaches. The iTRAQmethod provides a more comprehensive map of the proteincontent of a sample because all the proteins present inrequisite amounts will be systematically identified andquantified. Secondly, while DIGE separates only solubleproteins included in a pH range of 3 to 11, iTRAQ canidentify proteins outside the pH range of commonly usedgels and distinguish between proteins that would co-migrate on a gel. We identified a large number of mem-brane proteins as well as low abundance proteins such astranscription factors with the iTRAQ method. Furthermore,from the proteins reproducibly identified between the twotechniques, quantitative disagreements were found between



DIGE and iTRAQ. There are several reasons why DIGE andiTRAQ might quantify a protein differently. DIGE candetect differential expression of post-translationally mod-ified proteins as well as different isoforms of gene familiesby resolving spots at different pI and MW. However, theseisoforms may not be distinguished with iTRAQ, sincelabeling is completed at the peptide level and most peptidesequences are identical among a group of isoforms. Thus,common peptides shared by different isoforms will begrouped into the same progroup by ProteinPilot and willnot be used for protein quantification. This can lead tomisinterpretation of protein changes and mask specificchanges in isoform abundance resulting from treatment.Based on the present study, iTRAQ appears to be a morerobust technique, able to detect and quantify a much largernumber of proteins, but DIGE provided useful com-plementary information to the iTRAQ experiment. DIGEwill be more suitable for certain types of analyses, such asthose focusing on PTM. These different strengths under-score the importance of technical diversity in revealing thecomplexity of biological systems.

This work was funded by the Donald Danforth Plant ScienceCenter and by a grant from the National Science Foundation(MCB-0824492 to J.M.J and L.M.H.).

The authors have declared no conflict of interest.

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