Before gene-expression analysis of arsenic responses in S. cerevisiae, we performed a series of dose-response studies. We found that treatment of wild type cells with 100 μM and 1 mM AsIII had a negligible effect on growth, but that these cells still exhibited a pronounced transcriptional response (see Additional data files 1 and 2). Microarray analysis of biological replicates (four chips per replicate experiment) of the high-dose treated cells (1 mM AsIII) clustered extremely well together when using Treeview (see Materials and methods, and Additional data file 2). The lower dose time-course (100 μM AsIII) showed the beginning of gene-expression changes at 30 minutes, with the robust changes occurring at 2 hours, or one cell division (see Additional data file 2). The 2 hour, 100 μM dose clustered together with the 30 minute, 1 mM biological replicates and was in fact so similar to them that an experiment of one set of four chips for the 2 hour lower dose was deemed sufficient. Furthermore, when combining the three datasets (2 hour, 100 μM AsIII and each 30 minute, 1 mM AsIII replicate data) and using a 95% confidence interval (see Materials and methods) we found 271 genes that were not only statistically significant in at least 75% of the total data (9 out of 12 chips), but also that the direction and level of expression of these genes were similar between the datasets. The lower dose time-course also included a 4 hour treatment, or two cell divisions. This experiment demonstrated the greatest degree of variability, indicating either a cycling effect or the cell's return to homeostasis, which was further exemplified by a decrease in the transcriptional response (see Additional data file 2).

Genes were categorized by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Simplified Gene Ontology (biological process, cellular component and molecular function) (Table 1). In total, 829 genes out of 6,240 had significantly altered expression (see Materials and methods) in at least one experimental condition. The categories significantly enriched for differentially expressed genes in the KEGG pathways were glutathione, methionine, sulfur and selenoamino-acid metabolism, and in the Simplified Gene Ontology (biological process), cell communication and heat-shock response (Table 1).

We used the Cytoscape network visualization and modeling environment together with the ActiveModules network search plug-in to carry out a comprehensive search of the regulatory and metabolic networks 2728. The former consists of the complete yeast-interaction network of 20,985 interactions, in which 5,453 proteins are connected into circuits of protein-protein or protein-DNA interactions 2930. For each protein in this network, we defined a network neighborhood containing the protein and all its directly interacting partners. In the metabolic network, based on a reconstruction by Forster et al. 31 with 2,210 metabolic reactions and 584 metabolites, nodes represent individual reactions and edges represent metabolites. A shared metabolite links two reactions. We searched for sequences of related reactions governed by sensitive proteins (enzymes) in the phenotypic profiling data. To aid visualization, these sequences of reactions were combined to create metabolic pathways. We then identified the neighborhoods associated with significant changes in expression using the ActiveModules plug-in. This process resulted in the identification of seven significant neighborhoods in the regulatory network, centered on nodes Fhl1, Pre1, Yap1, Cad1, Hsf1, Msn2 and Msn4 (Figure 1). Together these neighborhoods narrow the significant data to 20% of the genes with the most significant changes in expression across one or more arsenic conditions (see Materials and methods and Additional data file 2). We did not find the emergence of any significant neighborhoods in the metabolic network.

The highest-scoring regulatory network neighborhood was defined by the transcription factor Fhl1 (Figure 1a). Its expression did not change significantly, but it was the highest-scoring node as judged by the significant expression changes observed for its surrounding neighborhood. Fhl1 controls a group of proteins important for nucleotide and RNA synthesis, as well as the synthesis and assembly of ribosomal proteins 32 which, from our data, are downregulated by arsenic exposure. Downregulation of ribosomal proteins in response to environmental stress has been reported previously 3334, but to our knowledge this is the first association of Fhl1 as a key control element in this process. It seems likely that the repression of de novo protein synthesis in response to arsenic allows energy to be diverted to the increased expression of genes involved in stress responses and protection of the cell. One such pathway may involve sulfur metabolism, which leads to glutathione synthesis. In fact, included in Figure 1 is Met31 (Figure 1e), a transcriptional regulator of methionine metabolism, which interacts with Met4, an important activator of the sulfur-assimilation pathway that is probably involved in the glutathione-requiring detoxification process. While the differential expression of this neighborhood was not strictly significant according to ActiveModules (see Materials and methods), it has high biological relevance in light of the statistically significant alteration in expression categorized using KEGG pathways (Table 1).

Another high-scoring neighborhood comprises part of the proteasome protein complex (Figure 1b). The components of the proteasome are likely to be upregulated to meet the increased demand for protein degradation brought about by the binding of AsIII to the sulfhydryl groups on proteins and/or glutathione that subsequently interfere with numerous enzyme systems such as cellular respiration 715. In this paper, we will propose that this occurs through indirect oxidative stress as a result of the depletion of glutathione.

Many of the central proteins in the significant neighborhoods uncovered by ActiveModules were transcription factors (Figure 1a,c-f). Although some of these proteins were not differentially expressed themselves, they were still high-scoring nodes because of the highly significant expression of their targets. This is also important to keep in mind as we discuss later which genes might be sensitive to arsenic, but not necessarily differentially expressed, and why many genes that are differentially expressed do not display sensitive phenotypes when deleted.

Transcription factors Msn2, Yap1, Msn4, Cad1 and Hsf1 were the central proteins for many of the significant neighborhoods found (Figure 1c,d,f). Together with several genes previously implicated in oxidative-stress responses, these neighborhoods compose a stress-response network 24263536373839. Of particular interest are Yap1 and Cad1, because of the high number of shared downstream targets (Figure 1c,f).

When overexpressed, Yap1 confers resistance to several toxic agents, and Yap1 mutants are hypersensitive to oxidants 334041424344. Conversely, Cad1 responds strongly to cadmium, but not to hydrogen peroxide (H₂O₂) 2635. Following arsenic exposure, Yap1 is induced at least fourfold, with many of its downstream targets showing high levels of induction (see Additional data file 3). Several of its targets are among the most highly upregulated genes (as high as 178-fold for OYE3 (encoding a NADPH dehydrogenase)). Moreover, Yap1 regulates GSH1, which encodes γ-glutamylcysteine synthetase (an enzyme involved in the biosynthesis of antioxidant glutathione), TRX2 (the antioxidant thioredoxin), GLR1 (glutathione reductase) and drug-efflux pumps ATR1 and FLR1 35454647484950. It should be noted that GSH1 and ATR1 are examples of several genes also targeted by Cad1. All of these specified Yap1 targets are induced after arsenic exposure, recapitulating the toxicant's role as a likely oxidant. During the course of this work, Wysocki et al. 24 also implicated Yap1 in arsenic tolerance.

As Cad1 and Yap1 share many downstream targets, the genes defined by these transcription factors are very similar. To determine which transcription factor is playing the most active role in the high level of differential expression for this group (see Figure 1c,f), we tested the roles of both activators by treatment of yap1Δ and cad1Δ deletion strains with 100 μM AsIII for 2 hours (Additional data file 4). Surprisingly, we did not find that Cad1 was involved in regulation in response to arsenic-mediated stress. The yap1Δ strain was not only sensitive to AsIII by phenotypic profiling (Additional data file 5) but also defective in the induction of several downstream enzymes with antioxidant properties (Figure 2a,b). Conversely, the cad1Δ strain displayed an almost identical profile to wild type, eliminating it as a strong factor in the arsenic response (Figure 2a,b). A list of arsenic-mediated genes with at least a twofold difference in expression compared to wild type for yap1Δ and cad1Δ is provided (Additional data files 6 and 7). These were generated using Rosetta Resolver with a p-value less than 0.001 (see Materials and methods for more detail). Also, Additional data files 8 and 9 contain tables of genes failing to be induced or repressed (or showing such a decrease in expression that they no longer make significantly expressed gene lists) in the yap1Δ and cad1Δ experiments, compared to the parent experiment, after treatment with 100 μM AsIII for 2 hours. These are lists of genes that would be potentially regulated by Yap1 and Cad1 in the presence of arsenic.

Treatment of yeast with as little as 100 μM AsIII for 2 hours resulted in the induction of at least 14 ubiquitin-related and proteasome gene products (Figure 1b and Figure 3). The eukaryotic proteasome consists of a 20S protease core and a 19S regulator complex, which includes six AAA-ATPases known as regulatory particle triple-A proteins (RPT1-6p) 5152. Proteins are targeted for degradation by the proteasome via the covalent attachment of ubiquitin to a lysine side chain on the target protein (Figure 3). Conjugating enzymes then function together with ubiquitin-ligase enzymes to adhere to the target protein, and are tailored to carry out specific protein degradation in DNA repair, growth control, cell-cycle regulation, receptor function and stress response, to name a few 5354. The apparent importance of Yap1 in response to possible oxidative damage by arsenic indicated a potential role for Rpn4 (induced eightfold, Figure 3). This is a 19S proteasome cap subunit, which also acts as a transcriptional activator of the ubiquitin-proteasome pathway and a variety of base-excision and nucleotide-excision DNA repair genes 345556.

Rpn4 is required for tolerance to cytotoxic compounds and may regulate multidrug resistance via the proteasome 57. Moreover, Owsianik et al. 57 identified an YRE (Yap-response element) site present in the RPN4 promoter. This YRE was found to be functional and important for the transactivation of RPN4 by Yap1 in response to oxidative compounds, such as H₂O₂. However, we also located the Rpn4-binding sequence, TTTTGCCACC, 47 bases distant from the open reading frame (ORF) of YAP1, indicating that Yap1 not only activates Rpn4, but that Rpn4 may in fact activate Yap1 58. In support of this hypothesis we found that relative to wild type, the level of Yap1 induction was lower in the rpn4Δ strain under arsenic stress conditions, whereas Rpn4 was equally induced in the yap1Δ strain (Additional data file 10).

With respect to wild type, the profile of rpn4Δ after treatment with arsenic was the most dramatically altered, save for arr1Δ (Figure 2 and Additional data files 11 and 12). These data suggest that arsenic modification of sulfhydryl groups on proteins leads to protein inactivation and therefore degradation via the 26S proteasome. Another scenario is that the proteasome, and/or its proteases, is sensitive to arsenic-related events, leading to dysfunctional protein turnover and an increased requirement for 26S proteasome subunits. A similar idea was proposed for the direct methylating agent, methylmethane sulfonate 34.

Arr1 is structurally related to Yap1 and Cad1 2024. However, little is known about how Arr1 may be involved in oxidative stress and/or multidrug resistance. Furthermore, Arr1 is not well represented by the interactions present in the yeast regulatory network. However, studies by Bobrowicz et al. 2059 show that the transcriptional activation of Arr3 requires the presence of the Arr1 gene product. Moreover, a report by Bouganim et al. 60 supports our finding that Yap1 also is important for arsenic resistance. They show that overproduction of Yap1 blocks the ability of Arr1 to fully activate Arr3 expression at high doses of arsenite, suggesting that Yap1 can compete for binding to the promoter of the Arr1 target gene, ARR3. While this paper was being written, Tamas and co-workers 24 showed that Arr1 transcriptionally controls Arr2 and Arr3 expression from a plasmid containing their promoters fused to the lacZ gene and measuring β-galactosidase activities. This was done by growing the cells for 20 hours with a low dose of metalloid and spiking the concentration to 1 mM AsIII for the last 2 hours of incubation. These experiments showed that ARR1 deletion resulted in complete loss of Arr3-lacZ induction, whereas YAP1 deletion did not significantly affect induction. Similar results were obtained for the Arr2-lacZ induction assay and the authors concluded that Yap1 has a role in metalloid-dependent activation of oxidative stress response genes, whereas the main function of Arr1 seems linked to the control of Arr2 and Arr3. Interestingly, this study was shortly followed by another from Menezes et al. 25 which found contrasting results when looking at mRNA and Northern-blot analysis. In this study, the induction of Arr2 and Arr3, after treatment with 2 mM AsIII for up to 90 minutes, did not occur in either the ARR1-deleted strain or the YAP1-deleted strain. These authors conclude that the requirement for both YAP1 and ARR1 is vital to yeast in the function of regulating and inducing genes important for arsenic detoxification. Finally, transcription profiling experiments presented here show that the arsenic transport proteins Arr2 and Arr3 are still expressed (2.9-fold induction for Arr2 and 1.8-fold for Arr3, respectively) in the ARR1 mutant, but show defective induction in the yap1Δ strain treated in parallel (Additional data files 4 and 10). These results indicate that Yap1 may control Arr2 and Arr3 when yeast is subjected to 100 μM AsIII for 2 hours.

Our results and those of Menezes et al. 25, in contrast to the results of Tamas and colleagues 24, might be explained by the following. Our and Menezes et al.'s studies looked at genes in the normal chromosome context rather than genes ectopically expressed from a plasmid; in addition, in our study, we treated the yeast with 100 μM AsIII while Wysocki et al. 24 started with a low dose, but spiked the concentration to 1 mM AsIII in the last 2 hours of incubation. However, Menezes et al. 25 used an even higher dose (2 mM AsIII for a time-course ending at 90 minutes) and obtained more similar results to ours, with the exception that their Northern-blot analysis, which can sometimes miss relatively small changes, indicated an apparent lack of induction of ARR2 or ARR3 in either the ARR1- or YAP1-deleted strains. Taken together, these data indicate that both ARR1 and YAP1 are important genes involved in the process of arsenite detoxification in the yeast cell, but because of the different strains and treatment protocols used between these three studies, further experiments are warranted to resolve the differences.

Other interesting results from our transcription profiling of the arr1Δ and parent strains after arsenic treatment (Figure 2a,d and Additional data files 13 and 14), included large differences in expression as a whole and in particular the inability of arr1Δ to induce serine biosynthesis-related genes such as SER3, and sulfur and methionine amino-acid metabolism genes including SAM4. Conversely, arr1Δ failed to repress SAM3, as well as CIT2, a glutamate biosynthesis gene, when compared to the parent profile.

These observations indicate that Arr1 may regulate sulfur-assimilation enzymes that are necessary for arsenic detoxification. This is particularly interesting considering that the ActiveModules algorithm identified the node Met31 (Figure 1e), the transcriptional regulator of methionine metabolism which interacts with Met4, an important activator of the sulfur-assimilation pathway that is likely to be involved in the glutathione-requiring detoxification process. Sulfur metabolism was also a functional category in the Simplified Gene Ontology found to be significantly enriched by the hypergeometric statistical test (see Materials and methods) (Table 1). Furthermore, phenotypic profiling results discussed later show the importance of serine and glutamate metabolism in the sensitivity response to arsenic. Lastly, it is important to note that arr1Δ also displays loss of expression of a number of ubiquitin-proteasome-related gene products, sharing similar expression patterns with rpn4Δ (Additional data files 13 and 14) and suggesting that it may have a role in protein degradation as well.

Our arsenic-treatment experiments revealed the strong induction of over 20 enzymes in the KEGG sulfur amino acid and glutathione biosynthesis pathways (Table 1). This is consistent with the hypothesis that glutathione acts as a first line of defense against arsenic by sequestering and forming complexes with the toxic metalloid 21.

Dormer et al. 61 showed that GSH1 induction by cadmium is dependent on the presence of Met4, Met31, Met32 and Cbf1 in the transcriptional complex of MET genes. Met4 and Met32 are also differentially expressed in response to arsenic and interact with Met31, which defines a network neighborhood as shown in Figure 1e. The biological impact of the sulfur-related stress response was further exemplified by comparisons of our arsenic profiles to H₂O₂ profiles (400 μM H₂O₂) from Causton et al. 62 (Table 2). Although we found many expected similarities between arsenic and H₂O₂ gene-expression profiles in regard to oxidative-stress response genes, sulfur and methionine metabolism genes, in response to H₂O₂, were either repressed or did not change (Table 2). Furthermore, a study by Fauchon et al. 63 showed that yeast cells treated for 1 hour with 1 mM of the metal Cd²⁺, responded by converting most of the sulfur assimilated by the cells into glutathione, thus reducing the availability of sulfur for protein synthesis. Our arsenic profile showed a similar response to the sulfur-assimilation profile seen with Cd²⁺ (Table 2). As a consequence, arsenic may be conferring indirect rather than direct oxidative stress mediated by the depletion of glutathione, thus inhibiting the breakdown of increasing amounts of H₂O₂ by glutathione peroxidase (GPX2, up 13-fold) (Figure 4) 2164.

To identify genes and pathways that confer sensitivity to arsenic, we identified deletion mutants with increased sensitivity to growth inhibition using a deletion mutant library of nonessential genes (4,650 homozygous diploid strains) 6566. Each strain contains two unique 20-bp sequences (UPTAG and DOWNTAG) enabling their growth to be analyzed en masse and the fitness contribution of each gene to be quantitatively assayed by hybridization to high-density oligonucleotide arrays. The top 50 sensitive deletion strains included: THR4, SER1, SER2, CPA2, CPA1, HOM2, HOM3, HOM6, ARG1, YAP1, CDC26, ARR3, CIN2, ARO1, ARO2 and ARO7. A listing of the rank order for all sensitivities is available (Additional data file 5).

Only 10% of the top 50 sensitive mutant strains were significantly differentially expressed in the transcript profile. This lack of direct correlation between gene expression and fitness data is consistent with data from our own and other laboratories 2465. At least three factors may contribute to this discrepancy. First, some highly expressed genes when deleted are nonviable (around 1,000 genes) and are therefore unable to be scored for fitness. Some examples of highly expressed, yet nonviable, genes under arsenic stress are ERO1 (7- to 10-fold induced), HCA4 (5- to 9-fold induced), and DCP1 (9- to 22-fold induced). Second, there are redundant pathways mediated by multiple genes, such that deletion of one does not lead to sensitivity. OYE2, OYE3, and a large number of reductases fall into this category. Finally, gene products that do not change significantly, mediate important biological responses and thus when deleted could sensitize the cell to a specific stressor. ARO1, ARO2, THR4 and HOM2 are examples of genes that are not differentially expressed but are very sensitive to arsenic.

Like the gene-expression data, the phenotypic data was subjected to searches performed against the regulatory network of yeast protein-protein and protein-DNA interactions as well as the metabolic network of all known biochemical reactions in yeast. Unlike the transcription profile, the phenotypic data analysis revealed no significant regions in the regulatory network, but did map to two statistically significant metabolic networks. The first significant pathway was amino acid synthesis/degradation with the terminal products being L-threonine and L-homoserine, beginning with precursors such as L-arginine, fumarate and oxaloacetate (Figure 5a). These products function in serine, threonine and glutamate metabolism. The second network indicated the importance of the shikimate pathway, which is essential for the production of aromatic compounds in plants, bacteria and fungi (Figure 5b). The shikimate pathway operates in the cytosol of yeast and utilizes phosphoenol pyruvate and erythrose 4-phosphate to produce chorismate through seven catalytic steps. It is a pathway with multiple branches, with chorismate representing the main branch point, and various branches giving rise to many end products. Interestingly, chorismate is also used for the production of ubiquinone, p-aminobenzoic acid (PABA) and folates, which are donors to homocysteine 676869.

Combining transcript profiling and phenotypic profiling provides deeper insights into the biology of arsenic responses. Until now there has been a lack of correlation between the differential expression of genes and sensitivity of deletion mutants 2465 and this was the case in the present study. However, by mapping each dataset to the regulatory and metabolic networks, we have uncovered the likely reason for this lack of congruence. Our data show that many of the most sensitive genes (Additional data file 5; top 50 ranks) are involved in serine and threonine metabolism, glutamate, aspartate and arginine metabolism, or shikimate metabolism, which are pathways upstream of the differentially expressed sulfur, methionine and homocysteine metabolic pathways, respectively. These downstream pathways are important for the conversion to glutathione, necessary for the cell's defense from arsenic (Figures 4, 5a, 6 and Table 1). This overlap of sensitive upstream pathways and differentially expressed downstream pathways provides the link between transcriptional and phenotypic profiling data (Figures 4 and 6).

Thus, we believe our work shows that the deletion of an individual gene can lead to a change in sensitivity to an agent only if the protein product of that gene is important for some process (for example, amino-acid synthesis or a transcription factor required for the increased expression of genes needed to protect against the agent). On the other hand, expression profiling shows the end product of the cell's response to arsenic. Therefore, an agent such as arsenic might cause a transcription factor (Yap1, for example) to increase the expression of as many as 50 genes, 20 of which might help to protect against the agent. However, deletion of any of the 50 would not be expected to have an effect on the response to arsenic. The effect of gene deletion would be on the transcription factor itself (whose expression might not be affected by the agent). Thus, in the case of arsenic exposure, we conclude that phenotypic profiling interrogates genes upstream of the genes that ultimately protect against arsenic toxicity and that the downstream targets that demonstrate differential expression probably share redundant functions and are not vulnerable in the phenotypic profiling (Figure 6).

S. cerevisiae strain BY4741 (MATa, his3Δ, leu2Δ0, met15Δ0, uraΔ0) was used and grown in synthetic complete medium at 30°C. Cells were grown to a density of 1 × 10⁷ cells per ml. Cultures were split into two; NaAsO₂ (100 μM and 1 mM in two biological repeats) was added to one culture, and both were incubated at 30°C for 0.5, 2 or 4 h. Cells were pelleted and washed in distilled water before RNA extraction. Deletion strains (yap1Δ, cad1Δ, arr1Δ and rpn4Δ) of the same background were obtained from Research Genetics, confirmed and treated the same way, for 2 h and 100 μM NaAsO₂.

For the cDNA hybridization experiments, total RNA was isolated using an acid-phenol method. Pellets were resuspended in 4 ml lysis buffer (10 mM Tris-HCL pH 7.5, 10 mM EDTA, 0.5% SDS). Four milliliters of acid (water-saturated, low pH) phenol was added followed by vortexing. The lysing cell solutions were incubated at 65°C for 1 h with occasional vigorous vortexing and then placed on ice for 10 min before centrifuging at 4°C for 10 min. The aqueous layers were re-extracted with phenol (room temperature, no incubation) and extracted once with chloroform. Sodium acetate was then added to 0.3 M with 2 volumes of absolute ethanol, placed at -20°C for 30 min, and then spun. Pellets were washed two or three times with 70% ethanol followed by Qiagen Poly(A)⁺ RNA purification with the Oligotex oligo (dT) selection step. Total RNA for the specific knockout strains and parent experiment was isolated by enzymatic reaction, following the RNeasy yeast protocol (Qiagen).

A cDNA yeast chip, developed in-house at National Institute of Environmental Health Sciences (NIEHS), was used for gene-expression profiling experiments. A complete listing of the ORFs on this chip is available at 70. cDNA microarray chips were prepared as previously described 7172. The cDNA was spotted as described 73. Each poly(A) RNA sample (2 μg) was labeled with Cy3- or Cy5-conjugated dUTP (Amersham) by a reverse transcription reaction using the reverse transcriptase SuperScript (Invitrogen), and the primer oligo(dT) (Amersham). The hybridizations and analysis were performed as described Hewitt et al. 74 except that genes having normalized ratio intensity values outside of a 95% confidence interval were considered significantly differentially expressed. Lists of differentially expressed genes were deposited into the NIEHS MAPS database 75. Genes that were differentially expressed in at least three of the four replicate experiments were compiled and subsequently clustered using the Cluster/Treeview software 76. GeneSpring (Silicon Genetics) and Cytoscape 28 were used to further analyze and visualize the data.

The knockout experiments were conducted on an Agilent yeast oligo array platform. Samples of 10 μg total RNA were labeled using the Agilent fluorescent direct label kit protocol and hybridizations were performed for 16 h in a rotating hybridization oven using the Agilent 60-mer oligo microarray-processing protocol. Slides were washed as indicated and scanned with an Agilent scanner. Data was gathered using the Agilent feature extraction software, using defaults for all parameters, save the ratio terms. To account for the use of the direct label protocol, error terms were changed to: Cy5 multiplicative error = 0.15; Cy3 multiplicative error = 0.25; Cy5 additive error = 20; Cy3 additive error = 20.

GEML files and images were exported from the Agilent feature extraction software and deposited into Rosetta Resolver (version 3.2, build 3.2.2.0.33) (Rosetta Biosoftware). Two arrays for each sample pair, including a fluor reversal, were combined into ratio experiments in Rosetta Resolver. Intensity plots were generated for each ratio experiment and genes were considered 'signature genes' if the p-value was less than 0.001. p-values were calculated using the Rosetta Resolver error model with Agilent error terms. The signature genes were analyzed with GeneSpring. The entire in-house and Agilent-based dataset is available in the Additional data files.

Genes have previously been categorized into various ontologies and pathways. If a particular pathway is enriched for genes that are significantly expressed in response to a process, we conclude that the pathway is likely to be involved in this process. In total, 829 genes out of 6,240 had a significant alteration in expression in at least one experimental condition. Along with the size of each functional category, a statistical measure for the significance of the enrichment was calculated by using a hypergeometric test. The level of significance for this test was determined using the Bonferroni correction, where the α value was set at 0.05 and the number of tests conducted for KEGG pathway and Simplified Gene Ontology (biological process) were 27 and 11, respectively.

The ActiveModules algorithm was used to identify neighborhoods in the regulatory network corresponding to significant levels of differential expression. In this search, if a protein has many neighbors, it is likely that at random a few will show significant changes in expression and these could be selected as a significant sub-network. Neighborhood scoring is a method we used to correct for this bias. In this scheme, a significant sub-network must contain either all or none of the neighbors of each protein. The significance then represents an aggregate over all neighbors of a protein. This prevents the biased selection of a few top-scoring proteins out of a large neighborhood in the search for significant sub-networks. For an in-depth description of this algorithm see Ideker et al. 1.

In defining the network used in the metabolic analysis, edges corresponding to metabolites linking more than 175 reactions were eliminated. This excludes metabolic cofactors such as ATP, NADH and H₂O from the search. Scores for each ORF were generated by mapping the fitness significance value to a Z-score. To assign scores to the individual reactions, Förster's mapping from ORF to reaction was used to generate a list of ORFs for each reaction. The Z-scores of these ORFs were then aggregated into a single score for that reaction using the following equation:

We used a dynamic programming algorithm adapted from Kelley et al. 77 to identify high-scoring paths in this network. Briefly, the highest-scoring path of length (n) ending at each node is determined by combining the scores of the individual node and the highest-scoring path of length (n - 1) ending at a neighbor node using the following formula:

Since a node with many neighbors is more likely to belong to a high-scoring path by random chance, the score of the neighboring path is corrected against the extreme-value statistic with the number of observations equal to the number of neighbors.

The significances of the top-scoring networks were determined by comparison to a distribution of the top-scoring networks from random data (reaction scores randomized with respect to the nodes of the network). After running the path finding/scoring algorithm, the score of the single highest-scoring path was added to the null distribution. This process was repeated for 10,000 interactions. This null distribution was then used to determine an empirical p-value, which represents the null hypothesis that there is no significant correlation between the topology of the metabolic network and the assignment of significance values to nodes in that network.

The intensity plots were generated from each experiment in Rosetta Resolver. A gene was considered a signature gene if the p-value was less than 0.001 and if the fold-change value was greater than or equal to twofold. Signature genes were then broadcasted on the intensity plot and exported as text files. Lists were imported into GeneSpring. The 'Filter on Fold Change' function was used to compare the parent control vs. parent AsIII experiment with each deletion (AsIII) experiment. The gene list selected for each filter on fold change analysis was a combination of the parent signature gene list and the signature gene list of the AsIII-treated deletion being analyzed at the time. For example, if the comparison was being done between parent (AsIII-treated) and Yap1 (AsIII-treated), the list used in the analysis was the combination of the parent signature genes and the Yap1 signature genes. The filter on fold change function reports genes that were selected from the one condition (parent) that had normalized data values that were greater or less than those in the other condition (deletion under investigation) by a factor of twofold. Each resulting gene list was saved. All the resulting gene lists were combined and an annotated gene list was exported for use in Eisen's Cluster/Treeview package (described earlier). The format of the exported data was the natural log. The gene tree generated for the paper was generated in GeneSpring. Each filter on fold change was saved as an annotated gene list.

Signature gene lists were generated in Rosetta Resolver from intensity plots as described above. Each signature gene list was saved as a 'Bioset' in Resolver. The parent Bioset was compared to each deletion Bioset using the 'Minus' function. This function finds those members in Bioset group 1 (parent) that do not exist in Bioset group 2 (deletion). Each of the resulting lists was saved as a new Bioset. The new 'minus' Bioset was broadcasted on its corresponding intensity plot and exported as a text file. This was repeated for each experiment with fine-tuning of the data using GeneSpring.

Homozygous diploid deletion strains and pooling of the strains were done as described 66. Aliquots were grown until logarithmic phase, diluted to OD₆₀₀ 0.05-0.1, split into tubes and treated with arsenic for 1-2 h at 1 mM, 2 mM and 5 mM concentrations. Similar responses were observed at each concentration, so the results were pooled. These cultures and a mock-treated sample were maintained in logarithmic phase growth by periodic dilution for 16-18 h. UPTAG and DOWNTAG sequences were separately amplified from genomic DNA of the drug and mock-treated samples by PCR using biotin-labeled primers as described previously 66. The amplification products were combined and hybridized to Tags3 arrays (Affymetrix). Procedures for PCR amplification, hybridization and scanning were done as described 66, and according to the manufacturer's recommendation when applicable. The images were quantified by using the Affymetrix Microarray Suite software. UPTAG and DOWNTAG values were separately normalized, ratioed (treated sample signal/control) and filtered for intensities above background 78.