Structure and properties of transcriptional networks driving selenite stress response in yeasts
- Hélène Salin†1, 3,
- Vivienne Fardeau†1, 4,
- Eugenia Piccini1,
- Gaelle Lelandais1, 5,
- Véronique Tanty2,
- Sophie Lemoine2,
- Claude Jacq1 and
- Frédéric Devaux1Email author
© Salin et al; licensee BioMed Central Ltd. 2008
Received: 09 June 2008
Accepted: 15 July 2008
Published: 15 July 2008
Stress responses provide valuable models for deciphering the transcriptional networks controlling the adaptation of the cell to its environment. We analyzed the transcriptome response of yeast to toxic concentrations of selenite. We used gene network mapping tools to identify functional pathways and transcription factors involved in this response. We then used chromatin immunoprecipitation and knock-out experiments to investigate the role of some of these regulators and the regulatory connections between them.
Selenite rapidly activates a battery of transcriptional circuits, including iron deprivation, oxidative stress and protein degradation responses. The mRNA levels of several transcriptional regulators are themselves regulated. We demonstrate the existence of a positive transcriptional loop connecting the regulator of proteasome expression, Rpn4p, to the pleiotropic drug response factor, Pdr1p. We also provide evidence for the involvement of this regulatory module in the oxidative stress response controlled by the Yap1p transcription factor and its conservation in the pathogenic yeast C. glabrata. In addition, we show that the drug resistance regulator gene YRR1 and the iron homeostasis regulator gene AFT2 are both directly regulated by Yap1p.
This work depicted a highly interconnected and complex transcriptional network involved in the adaptation of yeast genome expression to the presence of selenite in its chemical environment. It revealed the transcriptional regulation of PDR1 by Rpn4p, proposed a new role for the pleiotropic drug resistance network in stress response and demonstrated a direct regulatory connection between oxidative stress response and iron homeostasis.
The adaptation of genome expression to the chemical environment is a complex but crucial challenge for all living cells. Functional genomics analyses in budding yeast have shown that environmental stress responses may involve rapid changes in the expression of up to 30% of the genome. A common response to all stresses, named ESR (Environmental Stress Response), has been described, which consists in the inhibition of the cytosolic translation apparatus and the activation of the energy storage pathways . However, pathways responding specifically to the parameters of the environment also form a key part of the stress response. These pathways involve specific transcriptional modules that rapidly sense the environment as a series of chemical and physical features (e.g. redox, pH, osmolarity, temperature, etc.) and act together to adapt genome expression to the specific nature of each stress . For instance, at least eight different transcription factors act together to define the first-hour response of yeast cells to the toxic metalloid arsenite . These global and rapid responses are highly dynamic, involving sequential waves of gene activation and repression [1, 2, 4]. This requires tight temporal coordination between different transcriptional routes, which can be achieved in two complementary ways. First, the transcription factors involved in stress responses, despite responding to different signals, may have overlapping sets of targets . Second, cross-regulation between transcription factors may ensure the coordinated activation of different pathways . We focus here on the cross-talks between three transcriptional modules responsible for the oxidative stress response, the ubiquitine-mediated protein degradation and the pleiotropic drug resistance, respectively. These cellular pathways exist in all species, from bacteria to mammals and plants. In S. cerevisiae, the oxidative stress response is controlled principally by the Yap1p transcription factor of the AP1-like leucine zipper family. Yap1p acts as a secondary sensor for oxidative molecules, and thus responds to a wide spectra of toxic compounds, such as hydrogen peroxide, metals and metalloids, organic nucleophilic molecules and internal metabolic oxidative stress due to the production of toxic by-products during glycolysis [3, 4, 7, 8]. Yap1p recognizes YRE (Yap1p response elements, 5'-TKACTMA-3') in the promoters of genes involved in redox homeostasis and in xenobiotic export at the plasma membrane. The proteasome is involved in both the degradation of damaged or aggregated proteins and in the post-translational regulation of several biological processes, playing a key role in many stress responses . Expression of the genes involved in proteasome biogenesis and activity, and in ubiquitin-dependent proteolysis, is controlled by the C2H2 zinc finger protein Rpn4p, which recognizes the PACE (proteasome associated control element, 5'-GGTGGCAAA-3') sequence in the promoter of its target genes [3, 10, 11]. Pleiotropic drug resistance involves the upregulation of membrane proteins involved in drug efflux. The corresponding genes are controlled principally by two Zn2Cys6 Gal4p-like transcription factors: Pdr1p and Pdr3p. These two transcription factors have largely overlapping sets of targets and recognize the same DNA motif (named PDRE, 5'-TCCGYGGR-3'), but have different roles and regulatory properties [2, 5, 12–14]. The Yap1p and Rpn4p pathways are simultaneously involved in the yeast response to arsenate . Yap1p acts together with the Pdr1p/Pdr3p pathway to induce a drug specific response to the antifungal drug benomyl . No transcriptional regulation has been described for PDR1, but YAP1, RPN4 and PDR3 are induced by stress [3, 5, 6]. In this work, we used selenite as a model stress to investigate further the interactions between these three transcriptional modules. Selenium is an essential oligoelement that replaces the sulfur atom of some methionine and cysteine in proteins involved in various essential cell functions . Selenium is also a promising agent for cancer therapy and anti-aging treatments . However, high doses of selenium are toxic to eukaryotic cells . In yeast, selenium alters genome stability  and is detoxified in the vacuole after reacting with glutathione . Yeast cells have a high level of selenium tolerance, and yeast enriched in selenium have been used in therapeutic trials . We showed in this study that toxic doses of selenite activated various yeast stress response pathways, including the proteasome, oxidative stress, iron homeostasis and general stress pathways. We demonstrated that, in these growth conditions, the expression of PDR1 and RPN4 was coordinated through a positive transcriptional loop. This loop contributed to the optimal Yap1p-dependent oxidative stress induction of several genes encoding membrane proteins, including FLR1, ATR1 and FRM2. This function seemed to be conserved in the pathogenic yeast species C. glabrata. Finally, our data provide evidence for direct transcriptional regulation of the iron homeostasis regulator Aft2p and of the multidrug resistance regulator Yrr1p by Yap1p, indicating a broader role for this factor in coordination of the oxidative stress response.
Gene ontology mapping of the selenite response
A transcriptional loop connects RPN4 to PDR1
In searches of SGD [30–32] and Yeastract  databases for transcriptional regulators that might account for the selenite-dependent induction of PDR1, we identified a conserved Rpn4p recognition element in the promoters of the PDR1 orthologues in Saccharomyces sensu stricto species. Moreover, Rpn4p was found to bind to the PDR1 promoter in a global study of the genomic locations of yeast transcription factors binding sites . We therefore monitored PDR1 expression in an rpn4Δ strain. The Rpn4p had no apparent role in PDR1 basal expression but the inactivation of RPN4 severely reduced the sensitivity of PDR1 to selenite (figure 3). We conclude from these results that a positive transcriptional loop connects RPN4 and PDR1 in response to selenite.
The PDR1/RPN4 loop optimizes the Yap1p dependent oxidative stress response
Conservation of the YAP1/RPN4/PDR1 co-regulation in C. glabrata
Expanding the Yap1 network: connections with the Yrr1 and Aft2 regulons
Network mapping for the selenite response in budding yeast
We used selenite as a model stress to decipher part of the transcriptional network controlling the adaptation of the genome expression to toxic environmental conditions in yeast. Our data suggested that one of the earliest effects of selenite on gene expression was the induction of an iron starvation-like response, known to be controlled by the Aft1p and Aft2p transcription factors [28, 43]. Selenite may affect iron homeostasis at two, non exclusive, levels. First, selenium can interact with iron, with a high affinity, in the culture medium [44, 45], rendering this metal unavailable to the cell. Second, it may interfere with iron homeostasis by replacing sulfur in iron-sulfur cluster protein biosynthesis in the mitochondria . Interestingly, the sets of yeast mutant strains which accumulating selenium or iron are very similar, suggesting that these two elements are metabolized through similar cellular routes and affect similar cellular processes . Like arsenite , selenite induces a strong oxidative stress response, triggering redox homeostasis pathways and proteasome activity. Selenite may affect redox homeostasis in several ways. First, each selenite molecule contains three atoms of oxygen, which may generate reactive oxygen species (ROS) during selenite reduction. Second, selenite is metabolized through interaction with thiol derivatives, including gluthatione , probably leading to imbalance in redox homeostasis. Third, as mentioned above, selenite may interfere with iron homeostasis and iron-sulfur cluster protein biosynthesis, potentially affecting mitochondrial activity and redox homeostasis . Another feature of the oxidative stress response generated by metals and metalloids is the upregulation of genes involved in methionine and sulfur metabolism [3, 49]. This effect was not detected in our transcriptome analyses of the yeast response to selenite. This was certainly due to our experiments being carried out in rich media, in which the abundance of sulfur amino acids efficiently switched off the MET gene transcription , whereas the studies mentioned above were conducted in minimal media. Despite the simplicity of its molecular structure, selenite induces many different stress response pathways. Moreover, the dose of selenite used clearly compromised the cells ability to respond to stress efficiently. We were therefore able to observe the regulation of mRNA levels for many transcriptional regulators involved in stress responses. This made it possible to reveal new connections between these stress response pathways.
Transcriptional loops connect proteasome to pleiotropic drug resistance and oxidative stress response
The proteasome functions in many cellular processes, some of which are essential for cell survival as cell cycle progression or the adaptation to environmental changes [9, 51]. Its activity is therefore tightly regulated. One level of regulation is the expression of its subunits, which is controlled by the Rpn4p transcription factor in yeast . RPN4 is itself positively regulated by a complex array of transcriptional controls connected to environmental stress. These include the heat shock factor Hsf1p, the multidrug resistance regulators Pdr1p and Pdr3p and the oxidative stress response major regulator, Yap1p [3, 6]. In this work, we confirmed that Pdr1p and Yap1p were required for the full selenite-driven induction of RPN4 (figure 3 and see additional file 6). Recent data have suggested that RPN4 not only is a target for stress response pathways but Rpn4p also has a direct impact on the expression of some of its regulators. Indeed, the response of YAP1 to selenite and arsenite was diminished in the absence of RPN4 (see additional file 6,), as Rpn4p binds to a PACE present in the YAP1 promoter , providing strong evidence in favor of a transcriptional loop connecting RPN4 and YAP1. Similarly, one of the major findings of this study was that Rpn4p controls the expression of PDR1 in response to selenite (figure 3). Therefore, RPN4 establishes positive feedback loops with both the oxidative stress response and the pleiotropic drug resistance network. These loops seem to optimize part of the oxidative stress response, as the deletion of RPN4, PDR1 or PDR3 decreases the positive effect of Yap1p on some of its target genes by 50% (figure 4C). These effects on gene expression have apparently no impact on cell survival in laboratory conditions (see additional file 8), but evidence for their biological significance is provided by the observation of RPN4, YAP1 and PDR1 co-regulation in response to oxidative stress in the yeast C. glabrata (figure 6A). Our results are also consistent with those of a recent study showing that the Rpn4p, Yap1p, Pdr3p and Yrr1p transcription factors collaborate in the upregulation of FLR1 in response to oxidative stress . Positive feedback loops may induce bistability in biological systems . Bistability has obvious advantages in the responses of microbial cells to environmental changes. After transient exposure to stress, bistability allows some cells to maintain a particular pattern of gene expression long after the stimulus has ended. This provides a mechanism for the anticipation of future environmental changes based on past environmental conditions and can potentially generate heterogeneity in isogenic cell populations by generating bimodal population responses [53, 54]. For Rpn4p and Yap1p, the transcriptional positive feedback loop is counterbalanced by a post-translational negative feedback loop, with the levels of Rpn4p and Yap1p being negatively regulated by the proteasome [11, 55].
A third mode of functioning for the PDR pathway
Yap1p: a central node in the oxidative stress network
Yap1p is the major regulator of the genes encoding proteins involved in redox homeostasis in response to various sources of oxidative stress [1, 58]. Yap1p has been shown to influence other regulatory pathways, for instance by regulating RPN4 and CIN5 [3, 23]. A previous global analysis of DNA binding sites for most of the transcription factors of yeast provided the first evidence to suggest that Yap1p may regulate the expression of transcription factors . We have shown that the induction of YRR1  and AFT2 (figure 4) in response to stress is dependent on YAP1 and that Yap1p actually binds to the promoters of these genes in the presence of various sources of oxidative stress (figure 7). In response to both benomyl and selenite, YRR1 was induced later than other Yap1p targets (figure 2, ). Remarkably, several Yrr1p targets (e.g. FLR1, SNG1, SNQ2)  were found to be also directly regulated by Yap1p in response to selenite (see additional files 6 and 7). The role of Yrr1p therefore seems to be to support Yap1p in the long-term regulation of these genes. The physiological connection between iron metabolism and redox homeostasis has been established before. In particular, it has been shown that the activity of the Aft1p transcription factor, which senses iron through iron-sulfur protein biogenesis status , is influenced by the glutathione biosynthesis pathway and the Grx3 and Grx4 glutaredoxins, which are involved in the thiol redox system . Moreover, the double deletion of AFT1 and AFT2 induces cell hypersensitivity to oxidative stress . Our finding that Yap1p controls AFT2 expression provides a direct transcriptional connection between the two pathways. It may seem paradoxical that Yap1p positively controls a system that responds to iron starvation, given that iron uptake is likely to cause oxidative stress. Noteworthy, Aft2p specifically controls the expression of genes involved in the transport of iron from the cytosol to the vacuole and mitochondria . By contrast, Aft1p, which actually controls iron uptake from the environment, does not seem to be positively regulated by Yap1p.
In conclusion, Yap1p is a central node in the oxidative stress response network, coordinating the expression of at least four transcription factors involved in various stress response pathways (figure 8).
Structure and dynamics of the regulatory networks driving cell adaptation to environmental changes
We analyzed the structure and dynamics of the transcriptional regulatory network which controls the adaptation of yeast transcriptome to toxic doses of selenite (figure 8). Our findings hinted several important features of the regulatory networks involved in chemical stress responses. First, these networks are highly interconnected. The cross-regulation of different regulators makes it possible to transmit information between the different transcriptional routes, resulting in the tight coordination of the various cellular pathways required for cell survival. Second, these networks have versatile and dynamic structures and properties. This plasticity is based on the combination of different transcription factors responsive to different chemical and physical parameters, but also on the fact that the same transcription factor can change its protein partners and/or its DNA binding properties to adapt its activity to the physiological conditions (e.g. the three modes of functioning of the Pdr1p/Pdr3p combination). Third, there is a clear hierarchy in these networks, as illustrated by unidirectional regulations (e.g. Hsf1p on RPN4) and the unequal weightings of different relationships (e.g. RPN4/YAP1 and RPN4/PDR1 loops), which may also be a function of time and physiological status of the cell.
The Saccharomyces cerevisiae strains used were all of the BY4742 (MATa; his3Δ1;leu2Δ0; lysΔ0; ura3Δ0) background. The rpn4Δ, yap1Δ, pdr1Δ and pdr3Δ strains were purchased from Euroscarf . The Pdr1-myc and the Yap1-myc strains have been described elsewhere [2, 40]. The Candida glabrata strain was CBS418.
Growth conditions and time-course analyses of stress responses
Cells were grown at 30°C in YPD (1%(w/v) bacto-yeast extract, 2% (w/v) bacto-peptone, 2% (v/w) glucose) to an OD600 nm of 0.5. The cultures were then split in two. Sodium selenite (1 mM), hydrogen peroxide (0.3 mM) or benomyl (20 μg/ml) was added to one of the two half-cultures and water or DMSO (mock treatment) was added to the other. The cells were incubated for an appropriate period of time (see text), and were then either flash-frozen in cold ethanol for RNA extraction or treated with formaldehyde for chromatin immunoprecipitation (see below).
Cell culture (15 ml) was flash-frozen in 30 ml of absolute ethanol at -80°C. The cells were harvested by centrifugation (4 minutes at 3000 g). The cell pellets were stored at -80°C. Total RNAs was extracted as previously described .
Transcriptome and quantitative RT PCR analyses
The S. cerevisiae microarrays used are fully described in Array express (; accession numbers A-MEXP-337, A-MEXP-114 and A-MEXP-1064). The C. glabrata microarrays are described in the Gene Expression Omnibus database (; accession number: GPL3922). We used 10 μg of total RNA for cDNA synthesis and labeling. The microarray experiments were conducted as previously described . Raw data were normalized using global lowess followed by print-tip median methods, with background removal, as implemented in Goulphar . Experiments with wild-type strains were carried out 4 times, with dye swapping. The statistical significance of the differences in expression observed was determined with the TMEV version of SAM, with a FDR of 5% and the exact number of permutations [21, 63, 64]. Only genes with less than 25% missing values were considered for the SAM analyses. The remaining missing values were imputed by the KNN input method directly in the TMEV application [21, 63, 64]. Hierarchical clustering was performed using TMEV, with Euclidean distances and average linkage [63, 64]. The complete S. cerevisiae transcriptome data are available as additional files 1 and 5. The raw data can be downloaded from the array express database (accession number: E-TABM-439). The C. glabrata data can be downloaded from the Gene Expression Omnibus database (accession number: GSE10244).
We used 500 ng of total RNA for quantitative RT-PCR, which was performed as previously described . The oligonucleotides used are described in additional file 10. ACT1 was used as a reference for normalization.
Chromatin immunoprecipitation experiments, followed by intergenic microarray or quantitative PCR analyses, were performed as previously described . The S. cerevisiae intergenic arrays are described in the Array express database (accession number: A-MEXP-1065). The array results were normalized using the print-tip median . The statistical significance of the ChIP enrichments was assessed with the TMEV version of SAM with a FDR of 1% and the exact number of permutations [21, 63, 64]. The complete ChIP-chip results are available as additional file 11. The raw data can be downloaded from the array express database (accession number: E-TABM-437). The sequence of the oligonucleotides used for quantitative PCR can be found in additional file 10.
Functional analyses and network mapping of the genome-wide data were carried out with T-profiler , SGD GO term finder  or Yeastract , using the default parameters. Promoter sequence analyses were performed with the DNA pattern search tool from RSA tools  and the genome Browser tool from the SGD .
environmental stress response
Yap1p response element
proteasome associated control element
pleiotropic drug response element
open reading frame
methyl methane sulfonate
saccharomyces genome database
ChIP associated to microarrays
significance analysis of microarrays
reactive oxygen species
methionine biosynthesis pathway
pleiotropic drug resistance.
VF was the recipient of a fellowship from the French ministery of research (MESR). HS had a post-doctoral grant from the "Toxicologie nucléaire" French national program. EP was funded by the Italian Istituto Pasteur-Fondazione Cenci bolognetti. We are grateful to the Service de Génomique Fonctionnelle, CEA-Evry, and in particular to Frank Amyot and Xavier Gidrol, who kindly provided the yeast intergenic microarrays. This work was supported by the "Toxicologie nucléaire" program.
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