Yi Hu1

Gang Wang2

Grace Y. J. Chen1, 2

Xin Fu1

Shao Q. Yao1, 2

1Department ofBiological Sciences

2Department of Chemistry,National University of Singapore,Singapore, Republic of Singapore

Proteome analysis of Saccharomyces cerevisiaeunder metal stress by two-dimensional differentialgel electrophoresis

The defense mechanism by which cells combat metal stress remains poorly under-stood. By utilizing a newly developed technique – the differential gel electrophoresis(DIGE) – we evaluated the biological alterations of metal stress on Saccharomyces cer-evisiae at its translational level. By simultaneously comparing the differential expres-sion profiles of thousands of proteins as results of 15 different metal treatments, wewere able to closely examine the response of a large number of proteins within theyeast proteome towards individual metals, as well as the response of the same pro-teins towards different metals. This, to our knowledge, is the first case which demon-strates the potential of DIGE as a high-throughput tool for large-scale proteome analy-sis. From our studies, where yeast cells were exhaustively treated with exogenousmetals, 20–30% of all proteins detected showed statistically significant changes.According to different effects (up-/downregulation) of protein expression levelsobserved, we were able to tentatively divide the 15 metals into three groups. By massspectrometric analysis, more than 50 protein spots were positively identified, bothquantitatively and qualitatively. One of the proteins was identified to be Cu/Zn super-oxide dismutase (SOD1), and its expression levels as a result of 15 different metal treat-ments was further examined in greater details. Significant changes in SOD1 expressionwere observed throughout all 15 DIGE gels.

Keywords: Differential gel electrophoresis / Metal stress / Proteomics / Saccharomyces cerevi-siae / Two-dimensional gel electrophoresis EL 5369

1 Introduction

Metals are important cellular components of all organ-isms, some of which are cofactors of enzymes involvedin key cellular processes, whereas others are needed toproperly maintain both inter- and intracellular ionic bal-ances [1, 2]. Interestingly, while trace amounts of mostmetals are sufficient for cell survival, exposure to highmetal contents typically is lethal to most, if not all, cells.Heavy metals, on the other hand, are normally toxic tocells even at very low concentrations, thus presenting aserious threat to human health [3]. Over the years, a greatdeal of efforts has been spent on investigating differentmechanisms by which cells react on exposure to exoge-nous metals [3–11]. Our understanding of these biological

phenomena, however, still remains poor. It is widely be-lieved that living organisms use a number of defensemechanisms to counter metal toxicity [4–6]: prokaryotessuch as bacteria use efflux pumps for detoxification byexporting toxic metals outside the cells. In higher organ-isms, however, the exact mechanism remains less clear.One model system studied thus far is that of metallothio-neins, which are thought to remove metals by chelationusing highly abundant cysteine residues present in theseproteins [7]. Most researches thus far have been limited atthe transcriptional level of the organism, whereby differ-ences in the gene expression, as a result of exposure toexogenous metals, were investigated [3, 8–10]. Zhou et al.[8] studied the transcriptional autoregulation of a metal-lothionein gene family related to transcription factor-AMT1 [8], and were able to identify individual genes orgene products associated with metal toxicity. More re-cently, DNA microarray has been applied as a high-through-put method for global analysis of gene expression toexplore the cellular response to metal toxicity [3, 9, 10].By evaluating the global pattern of gene expression in anorganism under the influence of exogenous metals, it pro-vides new insights into the understanding of cellular altera-tions under different biological perturbations. Few reports

Correspondence: Prof. Shao Q. Yao, Department of Chemistryand Department of Biological Sciences, National University ofSingapore, 3 Science Dr 3, Singapore 117543, Republic of Singa-poreE-mail: chmyaosq@nus.edu.sgFax: +65-6779-1691

Abbreviations: DIGE, differential gel electrophoresis; SOD,superoxide dismutase

1458 Electrophoresis 2003, 24, 1458–1470

2003 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 0173-0835/03/0905–1458 $17.50�.50/0

Electrophoresis 2003, 24, 1458–1470 Proteome analysis of S. cerevisiae under metal stress 1459

thus far, however, have dealt with how cells are affected attheir translational levels, whereby the expression of specif-ic clusters of proteins may be up- or downregulated inorder for the organism to cope with sudden influx of exog-enous metals. All evidence gathered thus far indicates thatgene expression profiles at the mRNA level obtained fromDNA microarray often do not correlate well with the relativeprotein abundance in the cell [11]. A detailed proteomeprofile of a cell subjected to different types of exogenousstimuli, such as metal stress, followed by high-throughputfunctional analysis of proteins, thus provides a more directlink of the cell and its intracellular response with differentexternal conditions. This should eventually facilitate ourbetter understanding of different components involved incellular defense mechanisms and how they respond todifferent environments [12].

To date, several methods have been used for quantitativeanalysis of protein expression, most of which are basedon the conventional two-dimensional gel electrophoresis(2-D GE) [13, 14], which, when coupled with advancedmass spectrometric techniques, has allowed the rapididentification and characterization of thousands of pro-teins in a single gel. It is now possible to study most, ifnot all, proteins of simple organisms such as that of yeast[15]. Despite its wide applications, 2-D GE � MS has anumber of limitations [16]. First is its reproducibility; dueto subtle changes in the experimental conditions, proteinprofiles of the same sample analyzed on separate 2-Dgels are usually not superimposible, making it difficult tofind proteins changed between gels and to quantifychanges in the protein expression. Consequently, manygels of the same sample need to be run in order to ob-tain an “averaged” gel picture which provides a moreaccurate protein expression profile of the sample. As aresult, potential high-throughput analysis of hundreds, oreven thousands, of different biological samples using thistechnique is impractical, if not impossible. More impor-tantly, it also renders this technique difficult to identifyproteins from different biological samples where onlysmall changes in the number of proteins and their expres-sion levels are expected (i.e., normal vs. cancer cells). Sec-ondly, current detection methods of 2-D GE rely on stain-ing methods such as Coomassie blue, colloidal stains, andsilver stain to visualize proteins separated on the gel,which are either not very sensitive, or have limited lineardynamic range [17], thus limiting the technique to thedetection of mostly high-abundant proteins in a proteome.

In order to circumvent some of the problems encounteredin 2-D GE, a number of related technologies have recentlybeen developed [18–20]. Among them, isotope-coded af-finity tagging (ICAT) [18], differential gel exposure (DifExpo)[19], and differential gel electrophoresis (DIGE) [20] have

found increasing applications. Of particular notice is ICAT,which takes advantage of noninvasive isotope tags tolabel proteins via their cysteinyl residues, and comparestheir expression by peptide mapping using MS. Despitebeing promising, the main drawback of ICAT, however,may lie in the fact that each labeled protein needs to beproteolyzed into many peptide fragments before quanti-tation with MS can occur. Consequently, this makes it ex-tremely challenging to analyze the already complex pro-teome profiles.

DifExpo and DIGE are similar in that both methods labelboth treated and untreated protein samples separately,mix them, and then subject them to 2-D electrophoreticseparation on the same gel [19, 20]. Because the sameproteins in two samples are labeled, respectively, withstructurally similar tags such that they still migrate asone spot on the same gel, quantitation of differential pro-tein expression can be made by simply comparing theintensity difference of the two different labels on thesame spot. In the case of DifExpo, two protein sampleswere labeled with radioisotopes by growing the samplesin media containing hazardous isotopic food sources,therefore, they are of limited applications [19]. In order tocircumvent the need to use radioisotopes, Unlu et al. [20]developed a fluorecence-based DIGE. By taking advan-tage of two structurally similar cyanine-based dyes, Cy3and Cy5 (Fig. 1), two pools of protein samples were cova-lently labeled, then mixed, separated and quantified onthe same 2-D gel. Comparing to the conventional 2-Dgel, DIGE has its intrinsic advantages. Since the two sam-ples (treated and untreated) are resolved on the same gel,DIGE circumvents the necessity to obtain the “averaged”gel (as does in the case of conventional 2-D GE) in orderfor differences of protein expression to be readily quanti-fied with high reproducibility, making it an ideal techniquefor potential high-throughput analysis of many differentbiological samples simultaneously. Additionally, with thedetection of as little as 125 pg of a single protein, DIGEoffers greater sensitivity, as well as providing a muchbroader linear dynamic range compared to most conven-tional detection methods [17]. Consequently, it has re-cently been validated as a powerful tool for differentialexpression profiling of proteins [21–23].

To date, DIGE has been successfully used to profilechanges in protein expression between cancer cells andnormal cells [22, 23]. In these experiments, one kind ofsamples was investigated at one time. We report herethe first use of DIGE as a proteomic tool for high-through-put analysis of proteins expression profiles from many dif-ferent biological samples. Saccharomyces cerevisiae waschosen as the model organism, and its proteome profiles,upon treatments of 15 different metals, analyzed. In each P

rote

om

ics

and

2-D

E

1460 Y. Hu et al. Electrophoresis 2003, 24, 1458–1470

Figure 1. Methodology of proteome analysis by DIGE.

DIGE gel, an untreated control sample was run (as aninternal standard), together with a metal-treated sample,on the same 2-D gel. Upon separation and image analy-sis, spots corresponding to differentially expressed pro-teins as a result of different metal treatments were readily

matched, quantitated and identified by comparing withthe same spots in the control sample on the same gel. Inall, a total of 15 gels were needed for the analysis of thesame number of biological samples. To our knowledge,this represents the first work to explore the high-through-put feature of DIGE, and use it to correlate changes in theyeast proteome under different metal stresses. It thusprovides the first clues for better understandings of cellu-lar defense mechanisms in eukaryotes against differentexternal environments.

2 Materials and methods

2.1 Dye synthesis

N-Hydroxysuccinimide (NHS) esters of both Cy3 and Cy5dyes were synthesized and characterized as previouslydescribed [20, 24, 25]. Briefly, Cy3 (0.44 g, 0.76 mmol),synthesized as described [24], was dissolved in DMF(5 mL) followed by addition of N-hydroxysuccinimide(106 mg, 0.92 mmol) and 1,3-dicyclohexylcarbodiimide(DCC; 190 mg, 0.92 mmol). The reaction mixture wasallowed to stir for 12 h at room temperature (RT). Theproduct, NHS ester of Cy3, was obtained by purificationwith flash column to give a red solid; yield: 0.42 g (83%).Cy5 (0.22 g, 0.37 mmol), synthesized as previouslydescribed [24], was dissolved in DMF (3 mL) followed byaddition of N-hydroxysuccinimide (52 mg, 0.45 mmol)and DCC (93 mg, 0.45 mmol). The reaction mixture wasallowed to stir for 12 h at RT. The product, NHS ester ofCy5, was obtained by purification with flash column togive a blue solid; yield: 0.17 g (66%). The dyes were typi-cally dissolved in DMSO as 10 mM stock solutions andkept at �20�C.

2.2 Strains and culture conditions

The Saccharomyces cerevisiae strain BY4741 (MATahis3delta1 leu2delta0 met15delta0 ura3delta0) [26] usedin this study was obtained from ATCC (Cat. No. 201388).Strains were grown using conditions recommended bythe manufacturer. Briefly, the strain was grown in YPD(2% tryptone, 2% glucose, 1% yeast extract) mediumcontaining different concentrations of metal salts (Fig. 2).All metal salts were autoclaved before being added intothe medium. Survival tests were done by taking aliquotsfrom the mid-log phase culture (A600 = 0.4) and spottingthem onto the YPD plates containing different concentra-tion gradients of metal salts. The concentration of metalsalts, where 50% of viable cells were observed (i.e., LD50),was determined based on previous work [1]. Plates wereincubated for 3–5 days at 30�C. Culture conditions wereas previously described [27].

Electrophoresis 2003, 24, 1458–1470 Proteome analysis of S. cerevisiae under metal stress 1461

a)

Group Metal salts Concen-tration(mM)(LD50)

CV � 100% CV � 50%

Down-regula-tion

Folddiffer-ences

Up-regula-tion

Folddiffer-ences

Ratio Down-regula-tion

Folddiffer-ences

Up-regula-tion

Folddiffer-ences

Ratioa)

Copper sulfate 1 147 �6.0 175 �5.9 0.84 242 �2.2 299 �2.1 0.81Calcium chloride 200 131 �5.9 160 �5.9 0.81 238 �2.1 289 �2.1 0.82Cadmium chloride 0.025 139 �6.0 173 �5.9 0.80 322 �2.1 365 �2.1 0.88Cobalt chloride 1.2 95 �6.0 160 �5.9 0.59 243 �2.1 389 �2.1 0.62

A Chromium chloride 8 190 �6.0 209 �5.9 0.91 326 �2.1 369 �2.1 0.88Silver nitrate 0.4 126 �5.9 220 �5.9 0.57 263 �2.2 379 �2.2 0.69Lithium chloride 40 86 �5.9 192 �6.0 0.45 207 �2.1 345 �2.1 0.60Manganese chloride 2 123 �6.0 206 �5.9 0.60 235 �2.1 354 �2.1 0.66Zinc chloride 4 145 �6.0 247 �5.9 0.59 268 �2.1 423 �2.2 0.64

Magnesium chloride 150 155 �6.0 165 �6.0 0.94 277 �2.1 302 �2.1 0.92

BPotassium chloride 600 241 �5.9 186 �6.0 1.29 349 �2.2 335 �2.1 1.04Sodium chloride 400 158 �5.9 143 �5.9 1.10 285 �2.1 303 �2.1 0.94Nickel chloride 2 154 �5.9 149 �6.0 1.03 286 �2.1 305 �2.1 0.94

C Rubidium chloride 5 218 �5.9 170 �5.9 1.28 362 �2.1 316 �2.1 1.15Ferric chloride 8 291 �6.0 194 �5.9 1.50 428 �2.1 341 �2.1 1.26

a) Ratio is the number of downregulation over that of upregulation (CV � 50%). Metals were classi-fied based on this ratio: A (� 0.85); B (0.85–1.15); C (� 1.15).

Figure 2. Quantitative analysis of metal stress in Saccharomyces cerevisiae. (a) Tabular results of theanalysis. CV, coefficient of variation between the metal salts and the normal conditions of the spotvolumes. The spot volumes were normalized by the total spot volume of the gel. The fold differenceswere based on the normalized spot volumes. (b) Graphical representation of up- and downregulationpatterns of yeast proteome in different metals. Series 1, CV � 100% and downregulation; Series 2,CV � 100% and upregulation; Series 3, CV � 50% and downregulation; Series 4, CV � 50% andupregulation.

1462 Y. Hu et al. Electrophoresis 2003, 24, 1458–1470

2.3 Sample preparation and protein labeling

From the results of survival tests, a working concentrationfor each metal salt was chosen (Fig. 2). Autoclaved metalsalts were added to aliquots of mid-log phase culture(A600 = 0.4), and the culture was further grown until longphase. Cell pellets were harvested and sonicated in asample buffer containing 7 M urea, 2 M thiourea, 4%CHAPS and protease inhibitors. Supernatants were col-lected after ultracentrifugation at 75 000 rpm for 1 h at4�C. Tris-HCl (5 mM) was used to adjust the pH to8.0–9.0. The final concentration of Tris in the sample wasestimated to be 2–3 mM. The optimal dye-protein ratiowas experimentally determined to be �200 pmol dyes to50 �g proteins, so as to ensure that only 1–2% of lysineson proteins in the sample were labeled [20]. The untreatedprotein sample was labeled with Cy3, as an internal stan-dard in each gel and across different gels, together withdifferently treated samples labeled with Cy5, for 30 min,followed by quenching with 1.5 �M of hydroxylamine. Thelabeled samples (Cy3-labeled for untreated; Cy-5 labeledfor treated) were then combined with 300 �L rehydrationbuffer (final concentration: 8 M urea, 2% CHAPS, 1% v/vIPG buffer, 4 mg/mL DTT, trace amount of bromophenolblue).

2.4 2-D DIGE, imaging and data analysis

Unless otherwise indicated, all 2-D experiments were per-formed on an Ettan IPGphor isoelectric focusing andEttan DALT gel system (Amersham, USA), using stan-dard protocols recommended by the same vendor [28].50 �g each of the untreated and treated samples (or100 �g unlabeled control sample for silver stain) in therehydration buffer were applied to the immobilized pHgradient strip (pH 4–7, 18 cm; Amersham, Cleveland,OH, USA), passively rehydrated for 1 h, followed by 12 hof active rehydration (20�C, 50 V). The total voltage-hourin IEF was 32 kVh. After IEF, the strips were equilibrated inequilibration buffer 1 (6 M urea, 2% SDS, 0.375 M Tris(pH 8.8), 20% glycerol, 130 mM dithiothreitol; 15 min),then in equilibration buffer 2 (6 M urea, 2% SDS, 0.375 M

Tris (pH 8.8), 20% glycerol, 135 mM iodoacetamide;15 min), before being applied directly to a 12% SDS-PAGE gel, based on the manufacturer’s protocols. TheSDS electrophoresis was run first at 2.5 W/gel (for30 min), then 17 W/gel until the dye front was 1 mm fromthe bottom of the gel. Each gel was repeated at leastonce. Similar results were obtained. Gel images were col-lected using a Typhoon 9200 (Amersham) fluorescencegel scanner. A 580-nm band-pass filter, which transmitslight between 565 nm and 595 nm and has a transmissionpeak centered at 580 nm, was used for collection of Cy3

images. A 670-nm band-pass filter, which transmits lightsbetween 655 nm and 685 nm and has a transmissionpeak centered at 670 nm, was used for collection of Cy5images. Quantitative spot analysis was obtained with thesoftware Progenesis (Amersham), following the soft-ware’s manual. Volumes representing the sum of pixelintensities within the spots were normalized with the totalspot volume of the gel. All values of CV and fold differ-ences were based on normalized spot volumes. For thesilver stain experiment, 100–400 �g of the untreated pro-tein sample was separated by 2-D gel, as describedabove, and stained using methods described previously[29].

2.5 MALDI-TOF-MS

Spots of interest were excised manually from the gel,transferred to a PCR tube, extracted and then digestedwith trypsin according to methods described previously[30]. A MALDI-TOF mass spectrometer (Voyager-DESTR ; Applied Biosystems, Foster City, CA, USA) wasused to obtain the corresponding mass spectra ofdigested proteins by the delayed-extraction reflectionmode. The MALDI-TOF-MS data was used for proteinidentification by searching the NCBI database usingMascot search engine at http://www.matrixscience.com

3 Results and discussion

3.1 Metal survival test

According to a previous work of Pearce et al. [1], we firsttested the viability of the yeast strain on agar plates con-taining different concentrations of metal salts. The con-centration of each metal at which 50% of yeast cellswere viable (i.e., LD50) was then chosen as the workingconcentration for all subsequent studies (Fig. 2). By sub-jecting the yeast cells to the maximum length of exposureunder the highest “allowable” amount of a metal, wesought to ensure that maximum changes of the yeast pro-teome can be obtained upon treatments with different ex-ogenous metals. It should be noted that, in order to un-ambiguously identify those proteins directly involved inthe cellular defense mechanism against a certain metaltreatment, time- and dosage-dependent, as well as otherexperiments will be necessary (work in progress) beforeany reasonable conclusion can be made. In this particularstudy, our focus was primarily on investigating the globalresponse of the yeast proteome upon exhaustive treat-ments with different types of exogenous metals, with thegoal of testing the feasibility of DIGE as a potentially high-throughput tool for differential profiling of protein expres-sion.

Electrophoresis 2003, 24, 1458–1470 Proteome analysis of S. cerevisiae under metal stress 1463

3.2 Comparison of protein profiles of DIGEimages and silver-stained images

To test whether there were significant changes be-tween (i) the two fluorescently labeled gels, and (ii) thelabeled vs. unlabeled gels, we first took an untreated,control yeast sample and labeled it separately withCy3 and Cy5, respectively, mixed and resolved on thesame 2-D gel. Following the fluorescence scanning,two images obtained from the two detection channels,one in Cy3 and the other in Cy5 channel, were super-imposed; all spots on the gel showed up as yellowspots by simple visual inspection (data not shown),thus validating the basic principle and the high repro-ducibility of DIGE. We then took the same unlabeledyeast sample, subjected it to separation on a 2-D gel,followed by detection of proteins with silver stain. Bysimple visual inspection, we compared the fluorescentlylabeled vs. the silver-stained gels (Fig. 3a vs. 3c); theimage pattern of the labeled sample, albeit dissimilarto that of the unlabeled one, could be correlated usingthe image analysis software Progenesis . More spotswere clearly detected with the fluorescently labeled sam-ple, however, indicating that DIGE is more sensitive thanthe silver staining method. This is in good agreementwith previous observations [23]. On the other hand, asmall number of spots, which were clearly detected inthe silver-stained gel, were not visible in the DIGE gel.This may be the result of different degrees of labelingfrom proteins in the same sample, i.e., those proteins hav-ing few lysines may be underlabeled by Cy3/Cy5,whereas proteins of high lysine content may be overla-beled.

We next compared the untreated and the metal-treatedyeast samples and their corresponding images (Cy3 andCy5 channels, respectively) generated from the sameDIGE gel (Figs. 3a and b). Interestingly, a few unmatched,but highly reproducible spots (from repeated experiments)were identified, most of which were located in the low-mo-lecular-weight region of the gel. As demonstrated by ourearlier experiments, these spots were likely not caused byshifts of the same proteins as a result of the Cy3 and Cy5labeling. One possible reason to account for this observa-tion could be that the proteins corresponding to spotsobserved in one channel were either absent, or expressedin very low abundance in the other channel. It should benoted that, although fluorescence labeling is more sensi-tive than most other staining methods, some low-abundantproteins will still be invisible on the gel. Thus, it is entirelypossible that a normally expressed protein, visible (in Cy3channel) under untreated conditions, is down-regulatedand becomes highly underexpressed upon metal treat-ments, thereby rendering it invisible in the Cy5 channel.Similarly, if a low-abundant protein, normally invisible inthe Cy3 channel, is upregulated upon metal treatment, itmay become visible in the Cy5 channel. Consequently,these proteins may play more active roles in the cellulardefense mechanism against metal toxicity. Further investi-gations need to be done to validate this speculation.

3.3 Expression profiling of yeast proteomewith different metals

Trace metals are essential but toxic at high concentra-tions for many organisms [1, 2]. Some inorganic metalions were found to be associated with human diseases

Figure 3. Comparing protein patterns of DIGE images and the silver-stained image. (a) Untreated yeast sample labeledwith Cy3; (b) metal (Co2�)-treated sample labeled with Cy5; (c) untreated sample visualized with silver stain.

1464 Y. Hu et al. Electrophoresis 2003, 24, 1458–1470

[3]. Previous work of organisms under metal stress hadthus far been limited to only a small number of proteins inthe proteome that become responsive to toxic metal suchas Cd2�, Cu2� and Co2� [8, 27]. A global view of proteinexpression profiles from an organism subjected to differ-ent metal treatments will, therefore, be extremely useful incorrelating and understanding different cellular defensemechanisms at the translational level. Yeast is a simpleeukaryotic organism, with both its genomic as well asproteomic data well documented. We envisaged that it isan ideal model to study metal stress, anticipating thatthere may be some overlaps between its cellular defensemechanisms and other eukaryotic organisms such ashuman.

We used the Cy3-Cy5 dye combination in all our DIGEexperiments. Normally, DIGE employs three kinds of fluor-ophores, named Cy2, Cy3 and Cy5, to label protein sam-ples. Earlier work [21] has shown that the combination ofCy3 and Cy5 labeling is the least variable among the threepossible dye combinations. In our experiments, untreatedyeast protein samples were labeled with Cy3, metal-treat-ed samples were labeled with Cy5. Each metal-treatedyeast culture was grown, in parallel, with an untreatedculture, so as to minimize other external effects (tempe-rature, medium, etc). Fifteen untreated control yeastcultures were prepared and analyzed, together with 15metal-treated cultures. In each gel, we were able to read-ily detect 1000–2000 different proteins, �20% of whichsaw significant changes in their expression levels upontreatments with different metals (Fig. 2). This is not sur-prising, considering that exhaustive metal treatmentswere deliberately done to the yeast cells in order for max-imum changes to be observed under our experimentalconditions. For most proteins that changed under treat-ments of one metal, their expression levels typically chang-ed when subjected to other metal treatments, indicatingthat these proteins might be involved in the overall cellularsurvival mechanism. Certain proteins, however, saw theirexpression changed only upon the treatment of a specificmetal, suggesting that they might play a role in specificbiochemical pathways essential for the cellular defensemechanisms against exogenous metals in yeast. Sametypes of metal treatments, were seen to have differenteffects on same proteins in the yeast proteome, as exem-plified by the difference in their expression patterns (upand down), as well as the change in their expressionlevels. From results we obtained upon quantitative dataanalysis (vide infra) (Fig. 2 and Table 1), the 15 metalsmay be tentatively divided into three different groups, A,B and C, according to their up-/downregulation effects toproteins in the yeast proteome. The Group A metals, com-prising of mostly toxic heavy metals, such as Cu2�, Cd2�,Co2�, Cr3�, Mn2�, and Ag�, together with other metals

such as Li� and Zn2�, had more proteins being upregu-lated. The group B metals on the other hand, which con-tain most essential metals, such as Mg2�, K�, Na�, andNi2�, had similar protein regulation effects, whereby thenumber of proteins being upregulated was roughly thesame as that of proteins being downregulated. Twometals tested from our experiments, Rb� and Fe3�, wereclassified into group C, which had more proteins beingdownregulated.

It was previously shown that high concentrations ofmetals will cause damage to the cell [31]. To fight againstthis damage, the expression of some proteins, such asmetallothioneins and glutathione (GSH) [4, 7], which aredirectly involved in different cellular defense mechanismsto combat metal toxicity, would be induced to sequesterexcess metals, thereby reducing the amount of free metalions in the cell. In group A, most metals have been provento be toxic to the yeast. Thus, it is possible that manyproteins identified from these metal treatments may bedirectly involved in the detoxification of metals. On theother hand, other proteins may be repressed/induced bymetal stress as a result of DNA/RNA and protein damage.All group B metals, except for Ni2�, were not previouslyknown to be toxic to the yeast, therefore, it is reasonableto believe that changes in protein expression at high con-centrations of these metals are probably the direct reflec-tion of growth inhibition to the yeast. For group C metals,Rb� and Fe3�, more downregulating effects of proteinexpression were observed, indicating growth inhibition tothese proteins, which may imply that there is no efficientmechanism in the yeast to detoxify these two metal salts.

3.4 Quantitative thresholds for significantvariation of protein expression

To date, all quantitative analyses done on DIGE experi-ments employed fold differences of normalized spot-vol-ume ratios [21–23]. The shortcoming of this method isthat the threshold of high-molecular-weight proteinsis different from that of low-molecular-weight proteins.Consequently, demarcation of normalized spot volume isinevitable in order to determine an accurate threshold[21], which, in many cases, produces variables due todifferent scales in the spot volume. In order to circumventthis problem, we used the coefficient of variation (CV) forour spot analysis and quantitation, based on recommen-dations made by the software Progenesis . CV is a statis-tical term which represents the standard deviation of thesample expressed as a percentage of the sample mean,thus is independent of scales of normalized volumes.It provides a good measurement to compare variationsamong samples.

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Proteom

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Table 1. Proteins related to metal stress

Genename

Metal

Protein functionA B C

Cu2� Ca2� Cd2� Co2� Cr3� Ag� Li� Mn2� Zn2� Mg2� K� Na� Ni2� Rb� Fe3�

Proteins withantioxidantproperties

AHP1 �127 �101 �55 �69 �105 �88 N �78 �74 �141 �139 �139 �108 �141 �114 Thioredoxin peroxidaseCCP1 N N N 64 �139 �122 N N �126 �132 �75 �115 �91 �141 N Cytochrome c peroxidasePNC1 N �68 N N N �119 �99 �70 N N �141 N 53 N �64 NAD(�) salvage pathwaySOD1 112 99 103 81 113 N 89 77 122 N 99 73 103 105 77 Cu, Zn superoxide dismutaseTSA1 �105 �70 N N �78 �53 N �99 �53 �132 �55 N �139 �76 N Antioxidant enzyme

Heat shock and CPR5 N N N N N �63 N N �119 N N N N N N Cyclophilin Dchaperone SSA1 �141 �106 �88 �141 �141 N N �137 �141 �133 �141 �132 �140 �103 �133 Heat shock proteinproteins SSA2 �141 �109 �51 �138 �141 N N �141 �139 �128 �141 �137 �141 �133 �136 Heat shock protein

HSP26 N N N N �126 N �128 N N N �59 N �66 �141 N Heat shock protein

Proteases PEP4 N N �57 �102 N �99 N N N 96 83 N �141 N N Vacuolar proteinase A

Carbohydrate ALD3 �106 �109 N �73 �138 N �74 �101 �138 �135 �122 �57 �134 �72 �132 Aldehyde dehydrogenasemetabolism ALD6 �120 �109 N �106 �110 N �54 N �109 �129 �113 �102 �120 �72 N Aldehyde dehydrogenase

DAL7 �136 �100 �100 �56 �140 �65 N �139 �134 �141 �127 �141 �72 �132 �141 Malate synthaseENO2 N �60 �108 N �83 N N N N N �55 N N �105 �132 EnolaseFBA1 63 82 102 70 95 N 141 N 101 N N 50 56 N 50 Fructose-bisphosphate aldolaseGLK1 �59 N �105 N �118 N 65 141 �122 �141 �124 �141 �141 141 �139 Glucose phosphorylationGPD1 �64 �140 �119 �140 �140 �136 N �141 �122 �121 �78 N �97 �105 �139 Glycerol-3-phosphate

dehydrogenaseIPP1 �141 �120 �112 �106 �94 �100 N N �141 �141 �120 �128 �138 �133 �100 Inorganic pyrophosphatasePCK1 �111 N N N �114 �141 141 N �97 �137 �120 �107 N �63 N Phosphoenolpyruvate

carboxylkinasePDC1 �90 N �68 N �120 N 77 N �114 �130 �100 �70 �99 �124 �100 Pyruvate decarboxylasePGK1 �119 �117 �124 N �99 �126 N �63 �75 �73 N N �55 N �68 Phoshoglycerate kinasePGI1 N �100 �100 �84 �103 �141 N �132 �117 �141 �128 N N �112 �140 Glucose-6-phosphate isomeraseTDH2 �76 N N �83 �140 �100 N N �137 �141 �132 �110 90 �133 �82 Glyceraldehyde 3-phosphate

dehydrogenaseTDH3 �141 N �125 �113 �141 N N �141 �100 �93 �92 141 �133 �117 �91 Glyceraldehyde-3-phosphate

dehydrogenaseTPI1 N �80 N �59 N �61 N N 50 66 N N �141 88 N Triose-phosphate isomeraseYEL047C �141 N �111 �141 �103 N N 141 �125 N 141 N 141 141 �138 Fumurate reductase soluble

1466Y.H

uetal.

Electrop

horesis2003,24,1458–1470

Table 1. Continued

Genename

Metal

Protein functionA B C

Cu2� Ca2� Cd2� Co2� Cr3� Ag� Li� Mn2� Zn2� Mg2� K� Na� Ni2� Rb� Fe3�

DNA/proteinsynthesis

ADE1 N �50 N N N �112 �93 N N �124 N N N N N Phosphoribosyl amino imidazole-succinocarbozamide synthetase

ADO1 �102 �115 N N N �100 N N �141 N N N 66 �86 �89 Adenosine kinaseAPT1 �67 110 123 117 �110 68 �82 107 125 �60 �90 N N �99 N Adenine phosphoribosyltransferaseBET4 N 101 �114 69 N 103 134 N �141 �126 N �136 N �86 �99 Catalyzes prenylation of Ypt1pFCY1 91 N N N N �68 N 59 N 50 68 52 83 N 93 Cytosine deaminaseLYS9 �52 �126 N �92 �128 �114 N �135 �141 �135 N �82 N �83 �128 Saccharopine dehydrogenaseRPN9 �140 N �127 N N N �141 141 �141 N 141 �91 141 141 �141 Regulatory particle non-ATPaseSAM1 �63 �124 �64 �138 �128 �111 N �136 �136 �120 �116 N N �134 �75 S-Adenosylmethionine synthetase

Transcription EGD2 �135 60 N N �121 58 89 112 107 �89 �61 �119 �74 �141 N GAL4 enhancer proteinregulation HFI1 �141 N �52 �141 �103 N N 141 N N 141 N 141 N �132 Transcription factor involved in global

regulation of gene expressionMED4 �112 87 N 101 132 N N 82 128 N 115 N 63 98 N Member of RNA polymerase II

transcriptional regulation mediatorPET18 �122 �61 �68 N �96 �110 N N �126 90 �120 �98 �53 �134 �85 Transcription regulator

Unclassified ACT1 101 88 124 N N 118 N N 116 N N N�86

122 N Actinproteins ADK1 88 N N 53 89 N N �129 N N N N �141 95 N Adenylate kinase

ARP2 �141 �132 �129 �135 �51 �108 �70 �80 �138 �132 �138 �135 �136 �140 �117 Actin-related proteinBMH1 �137 �110 �80 N �126 �87 �98 N �136 �135 �124 �130 �71 �140 �140 Brain modulosignalin homologBMH2 �140 �110 �78 N �138 �94 N N �140 �136 �133 �129 �64 �140 �141 Brain modulosignalin homologDAL3 N N �53 55 N �90 N N 50 �59 �52 N �100 N N Ureidoglycolate hydrolaseSAR1 N �54 �103 N N �109 N N 61 �72 N �56 �77 �139 �104 GTP-binding protein of the ras superfamilySBA1 �125 69 �104 �137 N �84 �50 �141 76 52 N N N N N Binds to Hsp90 and is important for

pp60v-src activityTPM1 �63 N N N �138 N �55 N �74 �74 �103 �79 N �67 N TropomyosinVMA1 N N N �76 N 117 86 �103 N 109 N 74 72 N �136 Vacuolar H-ATPase catalytic subunit

Unknown PST2 �119 �117 �65 N �99 �126 N �63 132 �73 115 N N N �68 Protoplasts-secreted proteinfunction YFR044C N �137 �99 �71 �106 N N �107 �138 �135 �139 N N �124 �117 Hypothetical ORFproteins YIR179C �136 �136 N N �138 N N �133 63 �141 �133 �131 �111 �141 �141 Hypothetical ORF

YIR301W N �113 �62 N 52 �103 �118 N N N N N N 62 �96 Hypothetical ORF

(�) Upregulation; (�) downregulation; N, the value of CV is lower than 50% and thus shows no significant changes.

Electrophoresis 2003, 24, 1458–1470 Proteome analysis of S. cerevisiae under metal stress 1467

Due to the slight difference in fluorescence and cross-over effects [32] between Cy3 and Cy5, two imagesgenerated from the same protein sample labeled withCy3 and Cy5, respectively, would still show variableratios of the normalized spot volume [21]. By takingthis into account, we sought to obtain a statistical stan-dard deviation for our experimental data, and to quanti-tatively determine the lowest “allowable” CV thresholdof spot volume. Aliquots of the untreated control samplewere independently labeled with Cy3 and Cy5, respec-tively, mixed and separated on the same 2-D gel. Thequantitative data of each protein spot in the same sam-ple was obtained following image scanning and dataanalysis by Progenesis . The CV threshold of 95% ofconfidence is 50%, which represents at least a 2.1-folddifference in protein expression. For 99% of confidence,the CV threshold is 100% and represents at least a5.7-fold difference in protein expression (Fig. 2a). Over-all, under our experimental conditions, roughly 200–500proteins were seen to have their expression levels alteredby over 5-fold, whereas more than 2-fold changes inexpression levels were observed for 500–700 proteins(Fig. 2b).

3.5 Qualitative and quantitative analysis ofindividual spots on 15 gels

In order to validate DIGE as a powerful tool for potentialhigh-throughput proteome analysis, we sought to test thefeasibility of using only one control sample (untreatedyeast culture) as the internal reference to run parallel tothe 15 metal-treated yeast samples on the same 2-D gel,then use the 15 control images generated from the controlsample (Cy3 channels) separated on 15 gels to matchspots of the same proteins in different metal-treated sam-ples (Cy5 channels). Simple visualization of the 15 images(Cy3 channel) generated from the same control yeastsample separated on 15 different gels revealed thatmore than 80% of all proteins detected can be easilymatched on the 15 gels. By further adjustment of thespots, either manually or with the aid of Progenesis , wewere able to match nearly all spots on the gel (� 95%),confirming the potential high-throughput capability ofthe proteomic approach presented herein.

In order to further identify proteins that have undergoneexpression changes as a result of metal treatments, spotswere excised from the gel, enzymatically digested, andfurther characterized by MS. In this particular study, wefocused our attention primarily to those proteins that ob-served expression changes upon treatments with mostmetals tested, and at the same time taking into accounttheir overall spot intensity on the 2-D gel. In all, morethan 100 different protein spots were chosen and further

analyzed, and more than 50 yeast proteins were unam-biguously identified, both qualitatively and quantitatively(Fig. 4 and Table 1). The proteins identified by MS weregrouped into several categories based on their knownbiological functions (Table 1). It should be pointed outthat, since many other proteins that underwent metal-dependent expression changes were not identified byMS, the data shown in Table 1 may not truly reflect theoutcome of a given biological function as a result of metaltreatments.

It is evident from results shown in Table 1, that most of theproteins positively characterized by MS have more down-regulation effects resulting from different metal treat-ments. This is probably a coincidence, as results shownin Fig. 2 have already indicated that the numbers of up-/downregulated proteins were roughly the same. Never-theless, some consistent trends were observed. Out offive antioxidation-related proteins identified, four of whichsaw their protein expression level downregulated upontreatments with most metals tested, which probablyreflected the degree of oxidative stress the cells under-went. Interestingly, Cu/Zn superoxide dismutase (SOD1)(Fig. 3b), a protein previously identified to have antioxi-dant properties, was upregulated with all metals testedexcept two (i.e., Ag� and Mg�), indicating the proteinmay be actively involved in metal detoxification of yeast.Treatments with all metals from group A resulted in over2-fold upregulation effects on this protein. However, theCV of Ag� is less than the quantitative threshold for signif-icant variation we had determined earlier. Among group Bmetals, Mg2� had little effect on SOD1 expression, where-as the rest induced significant expression changes in thisprotein. The CV of Rb� and Fe3�, the two group C metals,were determined to be 105% and 77%, respectively.Among all 15 metal salts, the CV of Cu2�, Cd2�, Cr3�,Zn2�, Ni2�, and Rb� were all determined to be higherthan 100%, corresponding to more than 6.4-fold changesin SOD1 expression. From these results we may concludethat SOD1 plays a vital role in heavy metal detoxification,in good agreement with previous work [33]. More interest-ingly, Zn2�, a so-called “nontoxic” metal, has the highestupregulating effect on SOD1 expression. The exact rea-son for this is still under our current investigations. Withall metal treatments, all heat shock and chaperone pro-teins identified were either downregulated, or did notshow significant changes in their expression levels, imply-ing the critical balance of metal contents needed in theprotein folding processes. Most proteins involved in thecarbohydrate metabolism process were downregulated,underlining the importance of this essential biologicalprocess, and at the same time indicating the lethal effectof high-dosage metals towards cell survival and growth.For many proteins involved in DNA/protein synthesis, as

1468 Y. Hu et al. Electrophoresis 2003, 24, 1458–1470

Figure 4. (a) Protein map of Saccharomyces cerevisiae. All proteins positively identified by MS are highlighted, togetherwith their ORF names. (b) 3-D profiles of SOD1 present in 15 gels. Left panels show Cy3-labeled control samples, whileright panels are Cy5-labeled metal-treated samples. The images of spots are displayed in both the upper panels (2-D)and lower panels (3-D).

Electrophoresis 2003, 24, 1458–1470 Proteome analysis of S. cerevisiae under metal stress 1469

well as transcriptional regulation, some were upregulatedwhile others were downregulated, further indicating thecomplexity of cellular defense mechanisms against differ-ent metal stresses.

Aerobic organisms, such as yeast, are known to beequipped with a number of defense mechanisms in orderto counter the toxicity generated from reactive oxygenspecies [27, 33]. For instance, SOD catalyzes the dispro-portion of superoxide anion to hydrogen peroxide andoxygen [33]. Interestingly, previous work by Vido et al.[27] has shown that SOD1 was not induced by Cd2�.This is in contrast to our experimental data, whereby theCV of Cd2� was higher than 100%, representing a 6.5-foldchange in the expression level of SOD1. This clear dis-crepancy may be explained by the difference in exposuretime of yeast to Cd2� in the two different experiments, i.e.,in the work done by Vido et al., exponentially growingyeast cells were treated by Cd2� at most for 1 h, whereasin our experiments cells were exhaustively treated withCd2� until harvest (� 8 h). We may, therefore, hypothesizethat SOD1 is likely involved in the detoxification of metalssuch as Cd2�, although its effect was not detected at theinitial stage of heavy metal treatments. As the cell wasexposed to prolonged treatments of heavy metals, SOD1was effectively induced so as to take part in the detoxifi-cation process, leading to cell survival.

4 Concluding remarks

In summary, we have successfully used DIGE to study theglobal expression profiles of the yeast proteome in thepresence of different exogenous metals. A total of 15 2-Dgels were needed to study the effects of as many metalstowards yeast at its translational level. It would have takenat least 30 gels for the conventional 2-D approaches togenerate the same amount of data. This, to our knowl-edge, presents the first case where DIGE was used as ahigh-throughput tool for analysis of expression profiles ina proteome. Also for the first time, the global effects ofyeast proteome caused by different types of exogenousmetals have been systematically examined. With thismethod, alterations of not only all proteins expressed inan organism by individual metal salts, but also of individ-ual proteins as a result of different metal treatments couldbe determined in a single set of experiments. It is envi-saged that a better understanding of this knowledge willfacilitate our insights into the mechanism of eukaryoticorganisms against different metal stresses.

Like all existing techniques, DIGE has its intrinsic draw-backs. Due to the covalent linkage of Cy3/Cy5 dyes tothe proteins, which adds approximately 500 Da each tothe protein’s mass, there is a slight migration difference

between the labeled and unlabelled proteins, especiallyfor lower-molecular-weight proteins [21], presentingsome potential problems in protein excision and sub-sequent characterization. All evidence gathered thusfar, however, has indicated that significant shifts betweenthe labeled and unlabeled proteins in the pI dimensionhave not been observed. In our hands, we typically post-stained the DIGE gel with SyproRuby , whose detectionsensitivity is comparable to DIGE [23], followed by excis-ing the protein spots based on images obtained fromSyproRuby , with references to the DIGE images.

Overall, DIGE is a powerful tool in proteomic analysis. Weshowed that DIGE has great advantages in quantitativeanalysis and potential high-throughout applications. Asan emerging proteomic technology, it should find evenwider applications in the post genomic era. Work is under-way to identify more proteins from our 2-D experiments,and to build a global yeast protein expression network asthe function of different metal treatments. Images of allDIGE gels, as well as their corresponding data generatedfrom Progenesis analysis, are available upon request(from SQY at chmyaosq@nus.edu.sg).

Funding supports from the National University of Singa-pore (NUS) and the Agency for Science, Technology andResearch (A*STAR) of Singapore are acknowledged.S. Q. Y. is the recipient of the 2002 Young InvestigatorAward (YIA) from the Biomedical Research Council(BMRC). We thank Amersham Biosciences Singapore forthe evaluation version of Progenesis and technical assis-tance.

Received November 11, 2002

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