Abstract

Stress-activated protein kinases and transcription factors are crucial for surviving exposure to cadmium and other environmental toxicants, but their effects on the proteome remain largely unexplored. In this study, isobaric tag for relative and absolute quantitation reveals that cadmium stress triggers rapid proteome remodeling in the fission yeast Schizosaccharomyces pombe. Spc1/Sty1, a mitogen/stress-activated protein kinase homologous to human p38 and Saccharomyces cerevisiae Hog1, controls many of these changes, including enzymes of the oxidative phase of the pentose phosphate pathway and trehalose metabolism. Genetic studies indicate that control of carbohydrate metabolism by Spc1 is required for cadmium tolerance. The bZIP transcription factor Zip1, which is functionally related to human Nrf2 and S. cerevisiae Met4, has a smaller effect on cadmium-induced proteome remodeling, but it is required for production of key proteins involved in sulfur metabolism, which are essential for cadmium resistance. These studies reveal how Spc1 and Zip1 independently reshape the proteome to modulate cellular defense mechanisms against the toxic effects of cadmium.

Bivalent cadmium Cd(II) compounds are dangerous environmental toxicants that frequently contaminate industrial and toxic waste sites (Järup, 2003). In the human body, Cd(II) is chelated to glutathione and transferred to metallothionein (MT)3. Cd-MT has a very long half-life, on the order to 10–30 years, allowing it to gradually accumulate in the kidney over several decades (Tamás et al., 2005). Chronic exposure to Cd(II) can cause hematopoietic system deficiencies and increase the probability of lung, prostate, pancreas, and kidney cancer (Järup, 2003; Waalkes, 2003). Accordingly, cadmium has been listed as a class I human carcinogen.

At the cellular level, cadmium creates oxidative stress (Brennan and Schiestl, 1996), increases mutagenesis (Jin et al., 2003), alters epigenetic modifications in chromatin (Doi et al., 2011; Takiguchi et al., 2003), and causes other types of genetic and cellular damage (Bertin and Averbeck, 2006). These effects are derived at least partially from the disturbance of cellular homeostasis of calcium, zinc, and iron. Cellular responses to cadmium involve multiple strategies, with the export or compartmentalization into specific organelles, such as the vacuole, probably being the most efficient detoxification mechanisms found in single cell microbes (Tamás et al., 2005). Depending on the organism, thiol-mediated defense mechanisms involving MT, phytochelatin, or glutathione are used to competitively chelate cadmium and buffer reactive oxygen species (ROS) (Avery, 2001; Tamás et al., 2005). Much wider cytologic, transcriptional, metabolic modifications can also play important roles: e.g., cell cycle arrest (Bertin and Averbeck, 2006; Kennedy et al., 2008), stress-activated transcription regulation (Chen et al., 2003; Wemmie et al., 1994), and sulfur metabolism rearrangement in response to the demands of glutathione biosynthesis (Fauchon et al., 2002; Lafaye et al., 2005) are employed by different yeast species to limit the toxic effects of Cd(II) in contaminated environments.

Conserved mitogen/stress-activated protein kinase (MAPK) cascades play crucial roles in stress responses (Gacto et al., 2003; Keshet and Seger, 2010; Smith et al., 2010). In the fission yeast Schizosaccharomyces pombe, the most critical cascade consists of Sty1/Spc1 MAPK, Wis1 MAPK kinase, and Wak1/Win1 MAPK kinase kinases (Millar et al., 1995; Shiozaki and Russell, 1995a,b). Spc1 orthologs include p38 MAPK in humans and Hog1 MAPK in Saccharomyces cerevisiae. Spc1 regulates multiple cellular responses to cytotoxic stress, of which one of the most important may be the Atf1-Pcr1 heterodimeric transcription factor (Shiozaki and Russell, 1996; Wilkinson et al., 1996). The expression of hundreds of genes is influenced by Spc1 in the presence of Cd(II) (Chen et al., 2003). Cellular responses to other types of stress such as heat, ROS, and osmotic stress are also funneled through this integrative pathway (Gacto et al., 2003).

Zip1, a bZIP transcription factor required for survival of cadmium stress, was first identified as a high copy suppressor of an atf1 mutation (Ohmiya et al., 1999). Zip1 is an ortholog of S. cerevisiae Met4, and both proteins are related to mammalian Nrf2. These proteins regulate genes encoding enzymes involved in sulfur metabolism that are required for cytosolic glutathione pool accumulation (Baudouin-Cornu and Labarre, 2006). Their abundance is regulated by a cadmium-inhibited SCF-like E3 ubiquitin ligase (Harrison et al., 2005). Although Spc1 is required for survival of a variety of stress conditions, Zip1 is more specifically required for cadmium exposure (Rodriguez-Gabriel and Russell, 2005). Accordingly, Spc1 and Atf1 regulate a large number of core environmental stress response (CESR) genes, whereas Zip1 regulates a smaller number of specific environmental stress response (SESR) genes (Chen et al., 2003; Harrison et al., 2005).

Biochemical, genetic, and transcriptome experiments have analyzed cadmium stress in various organisms, but few proteomic studies have been reported. The unexpectedly weak correlation between proteome and transcriptome data emphasizes the need for comparable proteome and transcriptome studies (Griffin et al., 2002; Gygi et al., 1999; Washburn et al., 2003). Proteomic studies are of particular interest in genetically tractable but evolutionary divergent organisms such as S. cerevisiae and S. pombe, especially because these studies can be integrated with comprehensive transcriptome and functional profiling (deletome) analyses. In regard to cadmium stress, a two-dimensional gel electrophoresis study was reported for wild-type (WT) S. cerevisiae (Vido et al., 2001), and an amino acid-coded mass tagging (AACT) strategy was conducted with S. pombe (Bae and Chen, 2004). The S. pombe study identified 106 upregulated proteins, with the major functional classes being protein biosynthesis, oxygen and free radical detoxification, heat shock proteins, and response to stress (Bae and Chen, 2004). Surprisingly, the upregulated proteins did not include those that are involved in cysteine biosynthesis, even though such enzymes are required for production of glutathione and phytochelatin, which are essential for cadmium sequestration. The sulfide-containing Cd-(γ-Glu-Cys)nGly peptides complexes were presumed to be more stable and suitable for detoxification (Mehra and Winge, 1991).

The functions of specific stress response pathways in proteomic remodeling triggered by exposure to cadmium are largely unknown. In this study, we use isobaric tag for relative and absolute quantitation (iTRAQ) (Ross et al., 2004) to investigate the roles of Spc1 and Zip1 in controlling proteomic responses to cadmium stress in S. pombe. These studies reveal a dominant role for Spc1 in controlling proteomic responses to cadmium stress. Regulation of carbohydrate flux proteins by Spc1 is highlighted as one of the strategies for Cd(II) tolerance in S. pombe.

MATERIALS AND METHODS

Yeast strains and growth conditions. iTRAQ experiments were performed with WT, spc1::kanMX4 (spc1Δ), and zip1::kanMX4 (zip1Δ) mutant strains. Except as noted, all strains were h+leu1-32 ura4-D18 ade6-M216. Standard growth conditions using YES (yeast extract and 3% glucose with supplements) for S. pombe were used as previously described (Forsburg and Rhind, 2006; Moreno et al., 1991).

Yeast protein extraction and labeling. Log phase cultures grown in YES media at 30°C were adjusted to ~0.2 OD600. Cadmium sulfate (CdSO4; Sigma: 255513) was added to a final concentration of 100 µM. Cadmium treated and untreated cultures were incubated for 1h at 30°C. About 50ml of the samples were harvested and washed twice with ice-cold phosphate-buffered saline. Cells were resuspended in 300 µl of ice-cold lysis buffer (50mM Tris–HCl [pH 8.0], 150mM NaCl, 5mM EDTA, 10% glycerol, 50mM NaF, 0.1mM Na3VO4, 125nM okadaic acid, 0.2% NP40) and then disrupted with glass beads in a FastPrep-24-cell homogenizer (MP Biomedicals, Inc.) for 4 × 20 s at 5.0 speed setting and 4°C. Supernatants were recovered after a 13,000rpm centrifugation in an Eppendorf 5424 microcentrifuge for 1min at 4°C. Equal ratios of three cultures were pooled for proteomic analyses as described (Pham and Wright, 2008; Rossouw et al., 2010).

Protein quantification and data analysis. Protein samples were diluted in TNE (50mM Tris pH 8.0, 100mM NaCl, 1mM EDTA) buffer. RapiGest SF reagent (Waters Corp.) was added to a concentration of 0.1%, and the samples were boiled for 5min. Tris (2-carboxyethyl) phosphine (TCEP) was added to a concentration of 1mM, and the samples were incubated at 37°C for 30min. Subsequently, the samples were carboxymethylated with 0.5mg/ml of iodoacetamide for 30min at 37°C followed by neutralization with 2mM TCEP. Proteins samples prepared as above were digested with trypsin (trypsin:protein ratio 1:50) overnight at 37°C. RapiGest was degraded and removed by treating the samples with 250mM HCl at 37°C for 1h followed by centrifugation at 14,000rpm for 30min at 4°C. The soluble fraction was then added to a new tube and the peptides were extracted and desalted using Aspire RP30 desalting columns (Thermo Scientific).

The trypsinized samples (four samples) were labeled with isobaric tags (Ross et al., 2004), where each sample was labeled with a specific tag to its peptides. Each set of experiments were then pooled and fractionated using high pH reverse-phase chromatography (HPRP-Xterra C18 reverse phase, 4.6 × 10mm 5-µ particle [Waters Corp.]). The chromatography conditions were as follows: the column was heated to 37°C and a linear gradient from 5–35% (buffer A: 20mM ammonium formate [pH 10 aqueous], buffer B: 20mM ammonium formate [pH 10] in 80% acetonitrile [ACN]–water) was applied for 80min at 0.5ml/min flow rate. A total of 48 fractions of 0.5ml volume were collected. Each of these fractions were analyzed by high-pressure liquid chromatography (HPLC) coupled with tandem mass spectroscopy (LC-MS/MS) using nanospray ionization. The nanospray ionization experiments were performed using a QSTAR-Elite hybrid mass spectrometer (AB SCIEX) interfaced with nanoscale reversed-phase HPLC (Tempo) using a 10 cm–180µm inner diameter glass capillary packed with 5-µm C18 Zorbax beads (Agilent Technologies, Santa Clara, CA). Peptides were eluted from the C18 column into the mass spectrometer using a linear gradient (5–30%) of ACN at a flow rate of 550 µl/min for 100min. The buffers used to create the ACN gradient were as follows: buffer A (98% H2O, 2% ACN, 0.2% formic acid, and 0.005% TFA) and buffer B (100% ACN, 0.2% formic acid, and 0.005% TFA). MS/MS data were acquired in a data-dependent manner in which the MS1 data were acquired at m/z of 400–1800Da and the MS/MS data were acquired from m/z of 50–2000Da. Finally, the collected data were analyzed as a single experiment using ProteinPilot 4.0 (AB SCIEX) with the Paragon algorithm for peptide identifications and protein quantification. The nonlinear fitting method was used for determining local false discovery rates.

The Cytoscape (Cline et al., 2007) plugin BiNGO (Maere et al., 2005) was used for gene ontology (GO) analysis employing hypergeometric testing and Benjamini and Hochberg false discovery rate correction. The clusterMaker plugin was used for hierarchical clustering analysis (Morris et al., 2011). Pair-wise average-linkage and uncentered correlation were used for these analyses. All proteins that were significantly altered in the Cd(II)-treated WT sample were uploaded into KEGG (Kyoto Encyclopedia of Genes and Genomes) mapper (http://www.genome.jp/kegg/tool/map_pathway1.html) to search the pathway database (Kanehisa et al., 2002). MatInspector from Genomatix software suite version 2.3 was used to identify fungal basic leucine zipper family elements in the promoter regions of 16 genes from the oxidative pentose phosphate pathway (oxPPP) and trehalose metabolism pathways (Cartharius et al., 2005). MatInspector extracted promoter sequences from the fungal EIDorado database with an average length of 602bp. The default Genomatix optimized length was from 500bp upstream of the first transcription start site (TSS) and 100bp downstream of the last TSS. WebLogo was used to identify consensus bZIP transcription factor binding sites (Crooks et al., 2004).

Serial dilution and microculture growth analysis. For serial dilution assays, mid-log phase cultures were adjusted to OD600 0.2 and spotted onto YES plates in 10-fold serial dilutions. Data shown are representative of three or more independent experiments. Detailed procedures of microculture growth curve experiments were performed as previously described (Kennedy et al., 2008). Briefly, mid-log phase cultures grown at 30°C were adjusted to 0.2 OD600 in YES media. About 100 µl of samples were aliquoted into flat bottom 96-well plates and incubated at 30°C in a VERSAmax microplate reader (Molecular Devices, Sunnyvale, CA). OD600 measurements were taken every 30min. Data were derived from three cultures. The drug index (DI) was calculated by DI = log2(OD+ Cd/OD- Cd).

Immunoblotting. Whole-cell extracts were prepared as described above with the addition of 100 µM phenylmethylsulfonyl fluoride and 13 protease inhibitor cocktail (Roche: 04693159001). After SDS-PAGE electrophoresis, proteins were transferred to nitrocellulose membranes. Anti-TAP antibody from Sigma (PAP: P1291) and anti-myc from Covance (9E10: MMS-150P) were used to detect target proteins. Anti--tubulin from Sigma (T5168) was used for a loading control.

RESULTS

Protein Identification and Quantification

As transcriptome studies indicated that Spc1 and Zip1 regulate distinct transcriptional responses to cadmium exposure and both proteins are required for survival of cadmium stress (Chen et al., 2003; Harrison et al., 2010), we expected that they likely make independent contributions to survival of cadmium exposure. Indeed, genetic epistasis experiments revealed that an spc1Δ zip1Δ double mutant grew poorly compared with either single mutant, and microculture growth analysis showed that the double mutant was exquisitely sensitive to cadmium (Supplementary fig. 1).

FIG. 1.

Evaluation of iTRAQ data. (A) Histogram of proteins increased or decreased relative to untreated WT. ProteinPilot software from Applied Biosystems was used. For each quantified protein, the program gave a p value to assess the confidence of protein abundance alteration. Proteins with p value < 0.05 (significant), 0.01 (highly significant), and 0.001 (extremely significant) were calculated in different samples. (B) Vertical scatter plot was used to show the relative protein expression alteration in different samples. All proteins with p value < 0.01 were marked. The detailed protein alteration information is listed in Supplementary tables 2–6.

FIG. 1.

Evaluation of iTRAQ data. (A) Histogram of proteins increased or decreased relative to untreated WT. ProteinPilot software from Applied Biosystems was used. For each quantified protein, the program gave a p value to assess the confidence of protein abundance alteration. Proteins with p value < 0.05 (significant), 0.01 (highly significant), and 0.001 (extremely significant) were calculated in different samples. (B) Vertical scatter plot was used to show the relative protein expression alteration in different samples. All proteins with p value < 0.01 were marked. The detailed protein alteration information is listed in Supplementary tables 2–6.

To investigate the extent to which Spc1 and Zip1 regulate proteome remodeling in response to cadmium stress, we used iTRAQ to compare the proteomes of WT, spc1Δ, and zip1Δ cells. We collected samples after 1-h growth in media with 100 µM of CdSO4. We chose this concentration of cadmium because it was sufficient to halt the growth of spc1Δ and zip1Δ cells whilst having only a modest negative impact on the growth of WT (Supplementary fig. 1), and thus was likely to be physiologically relevant. Immunoblotting confirmed that two representative proteins, SPAC869.05c (predicted sulfate transporter) and Pyp2 (tyrosine phosphatase that targets Spc1), obviously increased at this time point in WT cells treated with 100 µM of CdSO4 (Supplementary fig. 2).

FIG. 2.

Comparison of transcriptome, proteome, and deletome profiles in cadmium-treated WT S. pombe. (A) Correlation between transcriptome and proteome were represented by scatter plot. A total of 1602 genes detected by both experiments were included. The correlation coefficient was 0.617414. (B) Venn diagram for transcriptome (T) and proteome (P) data. Up- or down (Dn)-regulated genes were counted separately. Only those proteins with p value < 0.01 were counted. The majority of altered proteins correlate with altered mRNA. (C) Histogram of regulated proteins with correlative changes in mRNA abundance. For example, for the 17 proteins that increased more than 1.5-fold, transcriptome data showed increased (> 2-fold) mRNA for 16 of the corresponding genes. (D) Venn diagram for transcriptome (T), proteome (P), and deletome (D) data for cadmium-treated cells. The deletome data were previously published (Kennedy et al., 2008). (E) Heat map depicts the 28 genes required for cadmium survival in deletome studies that showed alterations in transcriptome or proteome studies.

FIG. 2.

Comparison of transcriptome, proteome, and deletome profiles in cadmium-treated WT S. pombe. (A) Correlation between transcriptome and proteome were represented by scatter plot. A total of 1602 genes detected by both experiments were included. The correlation coefficient was 0.617414. (B) Venn diagram for transcriptome (T) and proteome (P) data. Up- or down (Dn)-regulated genes were counted separately. Only those proteins with p value < 0.01 were counted. The majority of altered proteins correlate with altered mRNA. (C) Histogram of regulated proteins with correlative changes in mRNA abundance. For example, for the 17 proteins that increased more than 1.5-fold, transcriptome data showed increased (> 2-fold) mRNA for 16 of the corresponding genes. (D) Venn diagram for transcriptome (T), proteome (P), and deletome (D) data for cadmium-treated cells. The deletome data were previously published (Kennedy et al., 2008). (E) Heat map depicts the 28 genes required for cadmium survival in deletome studies that showed alterations in transcriptome or proteome studies.

Cd(II)-treated or untreated samples harvested from biological triplicates were assessed in four-plex iTRAQ experiments. Untreated WT was used to quantify relative protein abundance in each experiment. More than 1600 proteins, representing ~35% of the predicted proteome (Wood et al., 2002), were quantified for each sample (Supplementary table 1).

TABLE 1

Summary of the iTRAQ Data

Sample Protein number CESR SESR (Cd) 
WT + Cd 100 − 34 22 (64.7%) 
66 37 (56.1%) 
spc1Δ 61 − 36 16 (44.4%) 
25 8 (32%) 
spc1Δ + Cd 87 − 47 25 (53.2%) 
40 9 (22.5%) 
zip1Δ 96 − 34 19 (55.9%) 
62 9 (14.5%) 
zip1Δ + Cd 213 − 82 42 (51.2%) 
131 43 (32.8%) 
Sample Protein number CESR SESR (Cd) 
WT + Cd 100 − 34 22 (64.7%) 
66 37 (56.1%) 
spc1Δ 61 − 36 16 (44.4%) 
25 8 (32%) 
spc1Δ + Cd 87 − 47 25 (53.2%) 
40 9 (22.5%) 
zip1Δ 96 − 34 19 (55.9%) 
62 9 (14.5%) 
zip1Δ + Cd 213 − 82 42 (51.2%) 
131 43 (32.8%) 

Note. Proteins that were highly significant (p value < 0.01), up-regulated “+”, or down-regulated “−” relative to untreated WT. The CESR and cadmium-SESR gene numbers were calculated for each sample (Chen et al., 2003).

Untreated WT samples were contrasted to assess the reliability of the measurements. A p value of < 0.01 resulted in very few false positives (Figs. 1A and 1B) and was therefore used to compile lists of up- and down-regulated proteins (Supplementary tables 2–6). In untreated spc1Δ cells, 61 proteins were changed, whereas in untreated zip1Δ cells, the value was 96. For cadmium-treated WT, spc1Δ or zip1Δ cells, the values were 100, 87, and 213, respectively. Table 1 summarizes the iTRAQ data.

Comparison of Transcriptome, Proteome, and Deletome Studies

As previous transcriptome profiles of Cd(II)-treated WT, spc1Δ and zip1Δ mutants were highly correlated (Chen et al., 2003; Harrison et al., 2005) (Supplementary fig. 3), we combined these data and took the average value of WT + Cd for the following analyses. Pearson product-moment correlation coefficient indicated that ~40% (r = 0.62) of the changes in protein abundance may be explained by changes in corresponding messenger RNAs (mRNAs) (Fig. 2A). Previous studies in budding yeast yielded similar results (Griffin et al., 2002; Gygi et al., 1999). Transcriptome and proteome data were highly correlated for the proteins that were significantly altered by Cd(II)-treatment, especially for up-regulated proteins (Figs. 2B and 2C). Proteins with the largest increases were more likely to be transcriptionally regulated (Fig. 2C). For example, of 17 proteins that increased > 1.5-fold in response to Cd(II), 16 of the corresponding mRNAs increased > 2-fold (mRNA data were lacking for one of these genes). For this subset, the average protein increase was 2.1-fold, and the average mRNA increase was 17.8-fold. For the 66 proteins that were significantly increased in the Cd(II)-treated cells, the corresponding mRNAs increased an average of 8.1-fold. In contrast, for the 34 proteins that were significantly decreased in the Cd(II)-treated cells, the corresponding mRNAs decreased an average of 0.543 relative to untreated cells.

FIG. 3.

Proteome and transcriptome profiles of spc1Δ and zip1Δ mutants. (A) Venn diagrams comparing iTRAQ data from spc1Δ, zip1Δ, and WT. Samples with (+Cd) or without (−Cd) cadmium treatment were analyzed separately. Only those proteins with p value < 0.01 were counted. Dn: down-regulated proteins; Up: up-regulated proteins. (B) Hierarchical clustering of transcriptome (top panel) and proteome (bottom panel) data for spc1Δ, zip1Δ, and WT. The spc1Δ and zip1Δ transcriptome data were previously published (Chen et al., 2003; Harrison et al., 2005). (C) Scatter plot analysis of the effects of spc1Δ and zip1Δ mutations. A total of 1412 genes and proteins detected by both iTRAQ and microarray experiments are represented on the x-axis. Alterations caused by Cd(II) treatment are represented by logarithmic value of relative alteration between mutants and WT (y-axis).

FIG. 3.

Proteome and transcriptome profiles of spc1Δ and zip1Δ mutants. (A) Venn diagrams comparing iTRAQ data from spc1Δ, zip1Δ, and WT. Samples with (+Cd) or without (−Cd) cadmium treatment were analyzed separately. Only those proteins with p value < 0.01 were counted. Dn: down-regulated proteins; Up: up-regulated proteins. (B) Hierarchical clustering of transcriptome (top panel) and proteome (bottom panel) data for spc1Δ, zip1Δ, and WT. The spc1Δ and zip1Δ transcriptome data were previously published (Chen et al., 2003; Harrison et al., 2005). (C) Scatter plot analysis of the effects of spc1Δ and zip1Δ mutations. A total of 1412 genes and proteins detected by both iTRAQ and microarray experiments are represented on the x-axis. Alterations caused by Cd(II) treatment are represented by logarithmic value of relative alteration between mutants and WT (y-axis).

Viewed from a different perspective, there were 317 genes for which mRNA increased > 2-fold in response to Cd(II)-treatment, of which 157 had iTRAQ measurements. For these 157 genes, 48 (31%) were significantly increased for protein abundance (1.56-fold average), none were significantly decreased, and the average change in protein abundance for all 157 genes was a 1.343 increase. In contrast, there were 171 genes for which mRNA was reduced below 0.53 relative to WT in response to cadmium treatment, of which 125 had iTRAQ measurements. Of these 125 genes, 17 (14%) were significantly decreased for protein abundance (average was 0.863), none were increased, and the average protein decreased 0.923 relative to WT. These strong correlations between mRNA and protein abundance suggest that regulation of gene expression, either through mRNA synthesis or degradation, underlie a large portion of proteome remodeling triggered by cadmium stress.

Microarray studies identified CESR genes, which are transcriptionally regulated in response to diverse types of environmental stress, and SESR genes, which only respond to certain types of stress (Chen et al., 2003). We found that in Cd(II)-treated WT, 22 of 34 (64.7%) of down-regulated and 37 of 66 (56.1%) of up-regulated proteins are encoded by CESR genes, representing ~60% of the total amount (Table 1). This value is higher than Cd(II)-caused transcriptome alterations, in which ~25% were CESR genes. However, of 32 Cd(II)-specific SESR genes, 14 encoded proteins that were detected by iTRAQ, 4 of which were up-regulated in cadmium-treated WT. These include the Zip1-regulated gene hmt2/SPBC2G5.06c, which encodes a mitochondrial sulfide-quinone oxidoreductase that is essential for Cd(II) resistance (Vande Weghe and Ow, 2001).

Kennedy et al. performed global fitness profiling of Cd(II)-treated fission yeast by individually screening 2649 haploid deletion mutants, finding 237 mutants that were sensitive to cadmium (Kennedy et al., 2008). Only five genes were shared by transcriptome, proteome, and deletome data sets (Fig. 2C), whereas 28 genes required for survival of Cd(II) stress were altered in the transcriptome or proteome samples (Fig. 2D). Of these, 23 (82%) were up-regulated, which is consistent with their essential functions in Cd(II) tolerance. The abundance of many mRNAs and proteins required for cadmium resistance do not change in response to cadmium exposure, although incomplete coverage of deletion library, genetic redundancy, multilayer gene expression regulation, and differences in the sensitivities of these measurements likely also underlie the low overlap of the deletome data with the proteome or transcriptome data sets. Generally, similar results were reported for physiological stress studies in budding yeast (Birrell et al., 2002; Giaever et al., 2002; Jin et al., 2008).

Transcriptome and Proteome Profiles in spc1Δ and zip1Δ Mutants

Then, we analyzed data from spc1Δ and zip1Δ cells (Fig. 3A). Without Cd(II) treatment, only 10 down-regulated and 5 up-regulated proteins were shared by both mutants, indicating that the Spc1 and Zip1 pathways function independently, which is consistent with the strong genetic interactions between the mutations in the absence of stress agents (Supplementary fig. 1). In the presence of cadmium, 1 of 34 (3%) of down-regulated and 12 of 66 (~18.2%) of up-regulated proteins in WT had similar alterations in both mutants, demonstrating that there are Spc1- and Zip1-independent pathways for regulating the Cd(II)-responsive proteome.

To reveal more details about the roles of Spc1 and Zip1, we performed hierarchical cluster analysis of the transcriptome and proteome data amongst the mutants and WT (Fig. 3B). In the absence of cadmium stress, the transcriptome patterns of zip1Δ cells were clearly different from WT. In contrast, the transcriptome effects of spc1Δ in untreated cells were weaker. These differences in untreated mutant cells were reflected in our proteome studies. In cadmium-treated cells, the effects of the mutations were the opposite. The transcriptome pattern in spc1Δ cells was quite different from WT, whereas the pattern in cadmium-treated zip1Δ cells was more similar to WT. These patterns were also reflected in the proteome data. After normalizing these data to Cd(II)-treated WT, the conclusion becomes more evident: at both transcriptome (top panel) and proteome (bottom panel) levels, loss of Spc1 has the greater effect (Fig. 3C).

Protein Classification and Pathway Analysis

We performed GO analysis of the proteome data sets (see Supplementary table 7 for detailed information). Without Cd(II) treatment, the overall patterns of spc1Δ and zip1Δ were similar (Fig. 4). Proteins involved metabolic processes, stress response, translation, and cellular transport were substantially affected by loss of Spc1 or Zip1, accounting for ~85% of the altered proteins in spc1Δ cells and ~91% of the altered proteins in zip1Δ cells. Stress response proteins were more strongly impacted in spc1Δ cells (20%) as compared with zip1Δ cells (9%). These observations are consistent with Spc1 regulating the expression of a greater number of stress tolerance genes. After Cd(II) treatment, the four major categories of affected proteins were the same. However, in many categories, the ratios between up- and down-regulated proteins were obviously changed. In WT, almost all stress response proteins were increased following Cd(II) stress. The translation-related proteins were down-regulated and protein degradation-related proteins were up-regulated. Similar patterns were found in zip1Δ cells. However, the patterns for spc1Δ were largely unchanged by Cd(II) treatment. These results further emphasize the importance of Spc1 in remodeling the proteome in response to Cd(II) stress. The data indicate that Spc1 is particularly required for controlling the abundance of proteins involved in translation and protein degradation, suggesting that Spc1 regulates stress responses at multiple levels (de Nadal and Posas, 2010).

FIG. 4.

Functional analysis of proteome. The proteins were sorted into nine groups according to GO biological process category. Bar charts showed relative enriched pattern in spc1Δ, zip1Δ, WT + Cd, spc1Δ + Cd, and zip1Δ + Cd samples. Up-regulated (cross-hatched bar) or down-regulated (white bar) proteins are sorted separately. The number adjacent to each column is the percentage of proteins in certain group comparing with total altered proteins. Detailed information is presented in Supplementary table 7.

FIG. 4.

Functional analysis of proteome. The proteins were sorted into nine groups according to GO biological process category. Bar charts showed relative enriched pattern in spc1Δ, zip1Δ, WT + Cd, spc1Δ + Cd, and zip1Δ + Cd samples. Up-regulated (cross-hatched bar) or down-regulated (white bar) proteins are sorted separately. The number adjacent to each column is the percentage of proteins in certain group comparing with total altered proteins. Detailed information is presented in Supplementary table 7.

Proteome Analysis Implicates oxPPP Pathway and Trehalose Metabolism in Cd(II) Tolerance

Cadmium stress caused SPAC22F8.05 (,-trehalose-phosphate synthase) to increase 1.90-fold, whilst Zwf1/SPAC3A12.18 (glucose-6-phosphate 1-dehydrogenase) increased 1.33-fold. Indeed, using a p value of < 0.05, almost all enzymes in the oxPPP and trehalose metabolism pathway increased in Cd(II)-treated cells. The protein up-regulation correlated with transcriptional induction (Fig. 5A). In growth assays, we found that SPCC16C4.10 (6-phosphogluconolactonase) from the oxPPP pathway and SPCC794.10 (UTP-glucose-1-phosphate uridylyltransferase) from trehalose metabolism were required for full resistance to Cd(II) (Fig. 5B). Interestingly, Godon et al. observed a related phenomenon in H2O2-treated budding yeast (Godon et al., 1998), suggesting that regulation of carbohydrate metabolic flux may be a conserved mechanism of coping with oxidative stress caused by H2O2 or Cd(II).

FIG. 5.

Two branches of glucose metabolism respond to cadmium exposure. (A) Outline of the oxPPP and trehalose metabolism pathways in S. pombe derived from KEGG. The numbers in parentheses list cadmium-induced changes in mRNA and protein abundance, respectively. Values in bold and underlined font are > 2-fold increase (mRNA) or p value < 0.05 (protein). (B) Dilution assays of mutants in the oxPPP and trehalose metabolism pathways. Mutants lacking SPCC16C4.10 and SPCC794.10 were sensitive to cadmium.

FIG. 5.

Two branches of glucose metabolism respond to cadmium exposure. (A) Outline of the oxPPP and trehalose metabolism pathways in S. pombe derived from KEGG. The numbers in parentheses list cadmium-induced changes in mRNA and protein abundance, respectively. Values in bold and underlined font are > 2-fold increase (mRNA) or p value < 0.05 (protein). (B) Dilution assays of mutants in the oxPPP and trehalose metabolism pathways. Mutants lacking SPCC16C4.10 and SPCC794.10 were sensitive to cadmium.

Spc1 was required for much of the Cd(II)-induced up-regulation of proteins in the oxPPP and trehalose pathways (Fig. 6A), suggesting a role for Spc1 in regulating carbohydrate fluxes. In support of this notion, we found that spc1Δ mutants formed smaller colonies in growth media containing 0.1% glucose in comparison to standard YES media, which contains 3% glucose (Fig. 6B).

FIG. 6.

Spc1 may regulate cellular response to Cd(II) by adjusting glucose metabolism. (A) Heat map representation of the mRNA and protein levels for the enzymes in oxPPP and trehalose metabolism pathways. The ClusterMaker plugin of Cytoscape software was used for heat map generation. The contrast value for transcriptome data is 1.9 and for proteome is 0.4. (B) Dilution assays reveal that growth of spc1Δ mutant is impaired in low glucose media. All strains in this panel had the genetic background h-ade6-M216. (C) Microculture growth assays of WT and spc1Δ cells grown in YES media with or without 10 µM CdSO4 in a range of glucose concentrations. DI = log2(OD+Cd/OD-Cd). (D) MatInspector from Genomatix software suite version 2.3 uncovered significant enrichment (p value = 0.020) of fungal basic leucine zipper family elements in the promoter regions of 16 genes from the oxPPP and trehalose metabolism pathways. The predicted bZIP transcription factor binding sites were uploaded into online software WebLogo to generate the figure.

FIG. 6.

Spc1 may regulate cellular response to Cd(II) by adjusting glucose metabolism. (A) Heat map representation of the mRNA and protein levels for the enzymes in oxPPP and trehalose metabolism pathways. The ClusterMaker plugin of Cytoscape software was used for heat map generation. The contrast value for transcriptome data is 1.9 and for proteome is 0.4. (B) Dilution assays reveal that growth of spc1Δ mutant is impaired in low glucose media. All strains in this panel had the genetic background h-ade6-M216. (C) Microculture growth assays of WT and spc1Δ cells grown in YES media with or without 10 µM CdSO4 in a range of glucose concentrations. DI = log2(OD+Cd/OD-Cd). (D) MatInspector from Genomatix software suite version 2.3 uncovered significant enrichment (p value = 0.020) of fungal basic leucine zipper family elements in the promoter regions of 16 genes from the oxPPP and trehalose metabolism pathways. The predicted bZIP transcription factor binding sites were uploaded into online software WebLogo to generate the figure.

We performed microculture growth assays to further explore the relationship between glucose metabolism and Cd(II) tolerance (Fig. 6C). To assess the effect of cadmium over the range of glucose concentrations, we calculated a “DI” value defined as DI = log2(OD+Cd/OD-Cd) (see Materials and Methods). In WT, higher glucose concentrations modestly improved growth during mid-log phase (5–10h) in the presence of cadmium, whilst in late log phase (10–15h), the effect was reversed (Fig. 6C). In contrast, in spc1Δ cells, the higher glucose concentrations partially ameliorated the effects of cadmium throughout all growth phases (Fig. 6C). These observations supported the notion that regulation of carbohydrate metabolism by Spc1 contributes to Cd(II) tolerance.

Predicted bZIP Transcription Factor Binding Sites for oxPPP and Trehalose Pathway Genes

Regulation of gene expression by Spc1 is mediated largely through its phosphorylation of the bZIP transcription factor Atf1 (Gaits et al., 1998; Wilkinson et al., 1996). Indeed, spc1Δ and atf1Δ mutations have similar effects on expression of genes of the oxPPP and trehalose pathways (Fig. 6A). In silico detection of potential bZIP transcription factor binding sites in the promoter regions of these genes identified the motif TTACGTAA (Fig. 6D). This sequence differs by only one nucleotide from the previously predicted Atf1-binding sequence TTACGTCA (Chen et al., 2003). Thus, regulating Atf1 in the transcription of genes of the oxPPP and trehalose pathways is likely to be one mechanism by which Spc1 enhances Cd(II) tolerance.

DISCUSSION

Exposure to cadmium and other environmental toxicants triggers rapid and extensive changes in patterns of gene expression, but the degrees to which these changes are translated into reshaping the proteome has not been extensively investigated. In this study, we have found that cadmium exposure leads to rapid proteome remodeling in fission yeast, and the most dramatic changes correlate well with transcriptome alterations. These data are consistent with studies showing that Spc1 and Zip1 are crucial for cadmium resistance and have major roles in regulating gene expression in response to cadmium exposure. Indeed, we find that Spc1 and Zip1 strongly influence proteome composition in Cd(II)-treated cells.

The overall picture of proteome remodeling triggered by Cd(II) stress in S. pombe is similar to those obtained from stress studies of S. cerevisiae: heat-shock, detoxification, carbohydrate, and protein metabolism proteins were enriched in the Cd(II)-treated WT sample, suggesting the importance of these proteins in common stress tolerances (Szopinska and Morsomme, 2010). Eight of 34 down-regulated and 21 of 66 up-regulated proteins matched a previous proteomic study of cadmium stress in fission yeast, which used an AACT technology (Bae and Chen, 2004). The poor overlap may be explained by the different protein quantification technologies or the different CdSO4 concentrations used in this study (100 µM) versus the earlier AACT investigation (1000 µM).

Without Cd(II) stress, all genetic (Supplementary fig. 1), transcriptome, and proteome (Fig. 3) data pointed to little functional overlap of the Spc1 and Zip1 pathways. Compared with the spc1Δ mutant, zip1Δ cells grew slower in the absence of Cd(II) stress. Correspondingly, transcriptome and proteome alterations were greater in zip1Δ cells (Fig. 3), and these cells displayed large changes involving proteins in metabolic and translation processes (Fig. 4). These data suggest that Zip1 has housekeeping activities that are critical in the absence of exogenous stress; indeed, the absence of Zip1 creates physiological stress. In contrast, our data suggest that Spc1 plays a more crucial role in mediating transcriptome and proteome remodeling that are triggered by Cd(II) stress.

Integrating our proteome data with previous transcriptome studies, Pearson product-moment correlation coefficient indicated that mRNA alterations could explain ~40% (r = 0.62) of the protein changes measured in WT cells treated with cadmium. Different CdSO4 concentrations used in the proteome and transcriptome studies, 100 versus 500 µM, respectively, may have diminished the correlation. However, if we consider the proteins that change the most in response to Cd(II) treatment, the correlation appears to be much higher. About 90% of the proteins that increase > 1.2-fold within 1h Cd(II) treatment are encoded by mRNAs that increase > 2-fold (Fig. 2C). Of the proteins that decrease more than 10% from the untreated condition, ~64% were encoded by mRNAs that decreased at least 50%. These strong correlations suggest that transcriptional regulation is responsible for a large fraction of the most dramatic proteome remodeling that is triggered by cadmium stress.

Our data suggest that post-transcriptional regulation may be involved in smaller changes involving a larger number of proteins. This may occur as a central adaptive response to stress exposure (Li et al., 2011; Sheikh et al., 1999; Sheikh and Fornace, 1999; Teige et al., 2001). Melamed et al. reported that high salt stress in S. cerevisiae can repress translation of many mRNAs and leads the accumulation of a non-translating pool of mRNA in P-bodies that re-enter the translation pool upon recovery from the stress (Brengues et al., 2005; Melamed et al., 2008). In S. pombe, we found that two RNA-binding proteins, Csx1 and Upf1, are required to stabilize atf1 mRNA and modulate transcriptional responses to oxidative stress (Rodriguez-Gabriel et al., 2003; Rodriguez-Gabriel and Russell, 2005). Genetic studies indicated that this process is counteracted by two other RNA-binding proteins, Cip1 and Cip2 (Martin et al., 2006). Many structural components of the ribosome (Rpl702, Rpl3001, Rps2), translation initiation factors (Tif33, Tif211), and elongation factors (Tef5) were down-regulated by Cd(II) treatment. Some proteins involved in ubiquitin-dependent protein metabolic process (Ubx4) and 19S proteosome regulatory subunits (Rpn502, Rpn11, Rpt6, Mts4) were up-regulated. These results suggest that regulation of translation and protein degradation is an important part of the cellular response to cadmium stress.

Previous studies suggested that budding yeast and fission yeast use different strategies to regulate sulfur metabolism in response to Cd(II) stress. Increasing sulfur amino acid biosynthetic enzymes was proposed to be critical in budding yeast (Vido et al., 2001), whereas incorporation of inorganic sulfide was suggested to be more important in fission yeast (Bae and Chen, 2004). Our experiments identified several key sulfur metabolism proteins that increased in response to Cd(II) treatment, including glutathione S-transferase Gst2, glutathione peroxidase Gpx1, and sulfide-quinone oxidoreductase Hmt2. Other proteins involved in sulfur metabolism were not increased in response to cadmium stress but were nevertheless dependent on Zip1 for protein expression, including Sua1/SPBC27.08c sulfate adenylyltransferase and Sir1/SPAC10F6.01c sulfite reductase.

We compared our data with an earlier proteome study in budding yeast that used two-dimensional gel electrophoresis to identify orthologs that are up-regulated in response to cadmium stress (Vido et al., 2001). Ten shared proteins are listed in Table 2. The list includes several heat shock chaperone proteins (e.g., Hsp104), proteins involved with responding to oxidative stress (e.g., catalase), and Met17/SPBC428.11, which is a homocysteine synthase (Baudouin-Cornu and Labarre, 2006). Increasing synthesis of homocysteine and methionine is believed to help sulfur sparing (Brzywczy et al., 2002), and methionine might be degraded to form S2− (Benevenga and Egan, 1983). This is consistent with the observation that inorganic sulfide accumulates after Cd(II) treatment (Bae and Chen, 2004). S2− forms stable complexes with Cd, glutathione, and phytochelatins, which are transported into vacuoles by ABC-type transporters (Bae and Chen, 2004; Benevenga and Egan, 1983; Perego and Howell, 1997). This appears to be one of the critical strategies for detoxifying cadmium in S. pombe (Tamás et al., 2005). The absence of a reverse trans-sulfuration pathway in fission yeast blocks the homocysteine to cysteine metabolic flux (Brzywczy and Paszewski, 1994; Brzywczy et al., 2002), thereby eliminating one mechanism for synthesizing cysteine for conversion into glutathione (Matityahu et al., 2006; Vido et al., 2001). However, Met17 might assist cysteine synthases by converting O-acetylserine to cysteine (Brzywczy and Paszewski, 1994; Yamagata, 1989). This is a special sulfur metabolism branch that is absent in budding yeast but occurs in many other fungi and higher plants (Brzywczy et al., 2002; Wirtz and Hell, 2006). The transcriptional repression of met17+ by cysteine supports this model (Brzywczy et al., 2002). Through this mechanism, Met17 might still be able to regulate glutathione synthesis. Taken together, both sulfur amino acid biosynthesis and inorganic sulfide accumulation appear to be employed for Cd(II) tolerance in fission yeast.

Our studies suggest that carbohydrate metabolism is involved in the cellular response to cadmium stress. Several studies have indicated that the oxPPP pathway and trehalose metabolism help cells deal with cytotoxic stress (Attfield, 1987; Bolanos and Almeida, 2010; Gonzalez-Hernandez et al., 2005; Pham and Wright, 2008). In most cell types, 10–20% of glucose oxidation occurs via the oxPPP pathway (Wamelink et al., 2008). As an alternative pathway for glucose oxidation, one of the main functions of the oxPPP pathway is to provide a major source of reducing equivalents in the form of NADPH, which are necessary for intracellular glutathione pool renewal and other biosynthetic processes (Kruger and von Schaewen, 2003; Wamelink et al., 2008). It appears that some stresses such as ROS can route more carbohydrate flux to this pathway by inactivating two enzymes in glycolysis: glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and triosephosphate isomerase (TPI) (Ralser et al., 2007). S. pombe has two GAPDH: Gpd3 and Tdh1. Gpd3 abundance is regulated by Spc1, whilst Tpi1 abundance is likely affected by Zip1 (0.873 in untreated zip1Δ cells, p value < 0.02). These observations suggest that both Spc1 and Zip1 may regulate carbohydrate flux redirection. Trehalose is PPP-related stress defender in living organisms. It can prevent membranes dehydration, scavenge free radicals, and play other important roles in cellular stress responses (Elbein et al., 2003). Enhanced recycling of this disaccharide is thought to be critical for tolerating several types of stress (Parrou et al., 1997; Zahringer et al., 1997). In this study, most of the enzymes of these two pathways were up-regulated in Cd(II)-treated cells (Fig. 5), suggesting that they may be important for Cd(II) tolerance. Spc1 was found to play important role in regulation of these enzymes (Fig. 6).

Taken together with previous transcriptome, functional profiling, and signaling studies, these new proteome studies suggest how Spc1 and Zip1 impose multilayer control for pathways important for Cd(II) tolerance in S. pombe (Fig. 7). Zip1 is crucial for housekeeping regulation of key enzymes that are involved in sulfur metabolism and are required for detoxification of cadmium, whereas Spc1 has a central role in the acute responses to cadmium stress. Acute cadmium stress leads to rapid proteome remodeling, and a large portion of this change correlates with transcriptional regulation, much of which is regulated by Spc1.

FIG. 7.

Model of Spc1- and Zip1-regulated responses to cadmium.

FIG. 7.

Model of Spc1- and Zip1-regulated responses to cadmium.

TABLE 2

List of Proteins Conserved in Cadmium Response in S. pombe and S. cerevisiae

Protein S. pombe gene S. cerevisiae gene H. sapiens homologue Product 
O00091 SPAC3A12.18 ZWF1 G6PD Glucose-6-phosphate 1-dehydrogenase (predicted) 
Q9UT59 SPAC513.07 GRE2  Flavonol reductase/cinnamoyl-CoA reductase family 
O14075 SPACUNK4.10 YNL274C  Glyoxylate reductase (predicted) 
O13326 SPBC428.11 MET25  Homocysteine synthase Met17 
P55306 SPCC757.07c CTT1 CAT Catalase 
O74402 SPBC4F6.17c HSP78  Mitochondrial heat shock protein Hsp78 (predicted) 
Q10265 SPAC13G7.02c SSA1  Heat shock protein Ssa1 (predicted) 
SSA2   
SSA4   
O94641 SPBC16D10.08c HSP104  Heat shock protein Hsp104 (predicted) 
Q9USI5 SPCC645.14c STI1 STIP1 Chaperone activator Sti1 (predicted) 
Q9P3A7 SPAC1565.08 CDC48 VCP AAA family ATPase Cdc48 
Protein S. pombe gene S. cerevisiae gene H. sapiens homologue Product 
O00091 SPAC3A12.18 ZWF1 G6PD Glucose-6-phosphate 1-dehydrogenase (predicted) 
Q9UT59 SPAC513.07 GRE2  Flavonol reductase/cinnamoyl-CoA reductase family 
O14075 SPACUNK4.10 YNL274C  Glyoxylate reductase (predicted) 
O13326 SPBC428.11 MET25  Homocysteine synthase Met17 
P55306 SPCC757.07c CTT1 CAT Catalase 
O74402 SPBC4F6.17c HSP78  Mitochondrial heat shock protein Hsp78 (predicted) 
Q10265 SPAC13G7.02c SSA1  Heat shock protein Ssa1 (predicted) 
SSA2   
SSA4   
O94641 SPBC16D10.08c HSP104  Heat shock protein Hsp104 (predicted) 
Q9USI5 SPCC645.14c STI1 STIP1 Chaperone activator Sti1 (predicted) 
Q9P3A7 SPAC1565.08 CDC48 VCP AAA family ATPase Cdc48 

Note. S. cerevisiae data were derived from a previous study (Vido et al., 2001). The protein accession number is from the UniProtKB database. Homologs for these two yeasts were derived from online tool: YOGY (http://www.bahlerlab.info/YOGY) (Penkett et al., 2006). Human homologs are from the HomoloGene database.

SUPPLEMENTARY DATA

The supplementary data include an epistasis analysis of Spc1 and Zip1 (Supplementary fig. 1), immunoblot analysis of two representative cadmium-regulated proteins (Supplementary fig. 2), scatter plot correlation of microarray studies (Supplementary fig. 3), a list of proteins identified and quantified from iTRAQ (Supplementary table 1), lists of proteins significantly changed in WT or mutant backgrounds (Supplementary tables 2–6), and cadmium-regulated proteins sorted according to GO biological process category (Supplementary table 7). Supplementary data are available online at http://toxsci.oxfordjournals.org/.

FUNDING

National Institute of Environmental Health Sciences (P42ES010337).

ACKNOWLEDGMENTS

We thank our colleagues in the UCSD Superfund Basic Research Program for their valuable suggestions, Dr Anastasia Kralli for sharing the VERSAmax microplate reader, and Oliver Limbo for superb technical advice.

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