GT i Ti Ppp px I G T - media.nature.com · First, we examined functions of TFs: OAF3, RDR1, WAR1, and RPI1. OAF3 acted as a ... SWI4 and SWI6 form SBF complex, playing a major role

Supplementary information: METHODS Data

The ChIP-chip data used in this study were from Harbison, Gordon et al1, which include binding sites information for 203 TFs in 6229 genes identified in rich media condition (or called YPD condition). The transcriptional response of 6429 genes with individual deletions of 269 transcription factors were from Hu, Killion et al2. These two data sets were combined to study the cooperativity between the common 184 TFs (Supplementary information, Table S5). Calculating the correlation significance of a TF pair

We used the following procedure to calculate the statistical significance of the overlap of target genes regulated by a TF pair in the ChIP-chip data or in the knockout data (Figure S1).

Given TF A and a specific p-value threshold (pA), we selected the binding (in the ChIP-chip data) or functional (in the knockout data) target genes of TF A (denoted as TA) whose p-values are less than or equal to the given threshold. Given two TFs A and B and a p-value threshold pair (pA and pB), we evaluated the overlap significance of TA and TB by calculating the probability (PAB) of obtaining more than or equal to the size of an intersection |IAB|=|TA ∩ TB | assuming these two TFs were independent. The probability PAB can be computed according to the hypergeometric distribution as: where G denotes the number of genes in the ChIP-chip or the knockout data, which were 6229 and 6429 respectively. |TA|, |TB| and |IAB| are the size of TA, TB and IAB respectively. Identifying and ranking cooperative TF pairs

Since we calculated the correlation of many TF pairs simultaneously (20503 (2203C ) pairs

for the ChIP-chip data and 36046 (2269C ) pairs for the knockout data), we used the most

stringent methods, Bonferroni correction, with a family wise error rate of 0.01 for multiple testing. Those TF pairs with adjusted p-value less than 0.01 were considered to be significantly correlated. Those TF pairs showed significant correlation in both the ChIP-chip and the knockout data were identified as cooperative TFs.

To rank the significance of cooperativity between TFs, we combined two independent correlation p-values calculated from the ChIP-chip and the knockout data into one p-value

using Fisher's combined probability test3. Briefly, if each p-value ip has a uniform

distribution between 0 and 1, then 2loge ip− has a 2x distribution. Since the tests for

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degrees of freedom. Using the 2x _ distribution we can compute a new combined p-value

based on the above sum. Selecting the optimal p-value threshold pair pA and pB and the appropriate threshold range

When a stringent p-value threshold 0.001 was used, only 20 cooperative TF pairs were identified. To find more reliable cooperative TFs, we tried to relax the threshold to an appropriate point and select the optimal threshold pair PA and PB within the range from 0.001 to this relaxed threshold. We looked at the number of cooperative TF pairs identified within different threshold ranges. When we relaxed the p-value thresholds, the number of cooperative TF pairs identified would increase, but at some point the number would increase much faster. Relaxing the p-value thresholds will always reduces the number of false negatives but increases the number of false positives. Intuitively then, the optimal choice of p-value thresholds will strike a balance between false positives and false negatives. Compared with true positives, false positives occur more frequently in relaxed thresholds than in strict ones. Thus, the faster increasing number of cooperative TF pairs identified is mainly due to the faster increasing number of false positives at this point with high probability. That is, much more false positives would be introduced and less false negatives would be excluded. Therefore, the relaxed p-value threshold chosen at this point would give an optimal balance between false positives and false negatives. Figure S2 suggested that the number of TF pairs identified increased faster within the range from 0.005 to 0.01 than that from 0 to 0.005, while it increased even faster within the range from 0.01 to 0.015. Therefore, 0.005 and 0.01 were chosen as the appropriate point that the p-value threshold was relaxed to. Results with thresholds relaxed to 0.005 were presented in this paper and those relaxed to 0.01 were available upon request.

In addition, we used a procedure to select the optimal threshold pair PA and PB ranging from 0.001 to the appropriate relaxed threshold (0.005 as used in this paper) by an increment of 0.001. The optimal threshold pair was chosen at which the most significant overlap was obtained(argminPAB(pA,pB)). The most significant p-value (min PAB(pA,pB)) was used to estimate the binding/functional correlation significance of the TF pairs (Figure S1). Constructing cooperative network of TFs

We used the Cytoscape Software15 to illustrate the network of the identified cooperative TFs. Each TF was colored according to the function annotated in Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) and MIPS Comprehensive Yeast Genome Database (CYGD; http://mips.helmholtz-muenchen.de/genre/proj/yeast/). Validating target genes of TF pairs

We identified the binding target genes of the cooperative TF pairs from the ChIP-chip data and the functional target genes from the knockout data (see Supplementary Table S6). From the overlap of binding and effectual target genes, we validated the target genes of the TF pairs.

RESULTS Comparison with the results using the stringent p-value threshold

When a stringent p-value thresholds 0.001 were used, only 20 cooperative TF pairs were identified (Table S4), among which 4 pairs contains uncharacterized TFs. Out of the remaining 16 TF pairs, 6 pairs have been experimentally validated and 13 pairs had evidences. In comparison, 9 pairs were experimentally validated and 14 pairs had evidences out of the top 16 TF pairs identified by our method with characterized TFs. Many well-known cooperative TFs were missed with this stringent p-value threshold, such as SWI4-SWI6, HSF1-RPN4, GAL3-GAL80 and MCM1-YOX1 etc. This suggested that a relaxed p-value threshold is required to identify more reliable cooperative TFs. Comparison with the results without the optimal procedure

To evaluate how much the optimal procedure improved the prediction accuracy, we compared our results with those obtained with the same relaxed threshold of 0.005 but without the optimal procedure (Table S4). Several known cooperative TFs, e.g., GCR1-TYE7,

RPN4-YRR1 and YOX1-YHP1 etc，were missed when the optimal procedure was not used.

Furthermore, out of 117 identified TF pairs, 44 pairs were experimentally or computationally validated by the method without the optimization, while 68 out of 186 were supported by our method with optimization. If the number of cooperative TF pairs that having evidences ranged from 100 to 1000, the Jaccard Similarity Score16 could be obtained at different number of true cooperative pairs. The Jaccard Similarity Score is defined as TP/(TP+FP+FN), where TP stands for true positives, FP for false positives, and FN for false negatives. Figure S3 showed that our method always achieved higher score than the method without the optimal principle, which demonstrated that the optimal principle is really helpful for reducing false negatives simultaneously with few false positives. Predicting functions for uncharacterized TFs

12 uncharacterized TFs, including STP4, SNT2, EDS1, STB4, YDR049W, YDR266C, YER130C, YPR196W, YFL052W, YPR022C, YFL278C and YML081W were included in our results. Assuming TFs were more likely to take part in the processes that their cooperative TFs are involved in, functions of those uncharacterized TFs were predicted (Supplementary information, Figure S4).

First, we examined functions of TFs: OAF3, RDR1, WAR1, and RPI1. OAF3 acted as a negative transcriptional regulator involved in multiple cellular responses 17; RDR1 was identified as a transcriptional repressor involved in the control of multidrug resistance 18; WAR1 activation was necessary for the induction of PDR12 through a signal transduction event that elicited adaptation to weak organic acid stress 19; RPI1 was associated with tolerance to weak acid-induced stress 20. Therefore, these four TFs were all involved in stress responses. With cooperation with RPN4, OAF3, RDR1, WAR1 and RPI1, EDS1, YFL052W, YPR022C and YLR278C were predicted to be involved in stress responses. In fact, YFL052W was predicted to regulate stress responses previously 21.

Out of the TFs cooperating with YML081W, three TFs were related to cell cycle, one TF was related to multiple functions, which suggested YML081W might be related to regulation of cell cycle. Similarly, among TFs interacting with STP4, three TFs were related to cell cycle, two TFs uncharacterized, and two TFs having multiple functions, suggesting that STP4 was

also possibly involved in regulation of the cell cycle. SNT2, cooperating only with STP4, might be involved in the cell cycle. STB4 (with one interacting TF related to metabolism and another one uncharacterized), YDR049W (with 3 interacting TFs related to metabolism, 2 TFs uncharacterized, 2 interacting TFs related to stress responses and 1 interacting TF related to cell cycle), YDR266C (5 interacting TFs related to metabolism, 2 interacting TFs uncharacterized and 2 interacting TFs related to stress response) and YER130C (among the interacting TFs, 3 TFs related to metabolism, 2 TFs uncharacterized, 2 TFs related to stress response and 2 TFs related to cell cycle) may regulate metabolism processes. The cooperative TFs with YPR196W were related to various regulatory and metabolic processes and therefore it might act as crosstalks between different processes. Validation of target genes of cooperative TFs

Out of the 186 cooperative TF pairs, target genes of only 10 TF pairs were identified here (Supplementary information, Figure S5, Table S6), which is mainly due to the low overlap between the ChIP-chip and knockout data 2.

Four genes, YOL140W (Arg8), YJL088W (Arg3), YOR303W (Cpa1), and YER069W (Arg5,6), were identified to be regulated by the ARG80-ARG81 complex. All of them were involved in the arginine biosynthesis and confirmed by previous experiments 22, 23.

Both RAP1 and SFP1 have been reported to control the expression of ribosome biogenesis genes 24, 25. Our results showed evidences for their cooperation in regulating the expression of genes involved in ribosome biogenesis. Among their target genes, YER117W (Rpl23B) and YDL082W (Rpl13A) encoded components of the large ribosomal subunit (60S) and YNL096C (Rps7B) encoded a component of the small subunit (40S) 26.

SWI4 and SWI6 form SBF complex, playing a major role in the cell cycle 27. Most of their target genes encode cell wall proteins, such as TOS6 (YNL300W), SRL1 (YOR247W), CWP1 (YKL096W), and CWP2 (YKL096W-A). This supported the idea that the cell cycle progress was closely associated with the translation of the cell wall proteins in S. cerevisiae 28. One dubious open reading frame, YOR248W, was detected as a target gene of SWI4-SWI6. Given that other target proteins of SWI4-SWI6 were related to cell wall, YOR248W might also express proteins related to cell wall. YKL096W was also regulated by SKO1-SUT1 complex, indicating a connection between stress responses and the change of cell wall proteins. SWI4 was probably also involved in other processes, as suggested by its cooperation with UME6 to regulate YML100W (TSL), which catalyzed the synthesis of trehalose under stress, the major reserve carbohydrate in S. cerevisiae 29, 30.

GCR1-GCR2, TEC1-TYE7 and GCR1-TYE7 regulated genes involved in two different pathways: carbohydrate metabolism and gene expansion. TEC1 31 and TYE7 32 were both required for Ty1-mediated gene expression, their cooperative regulation in this process however has not been tested. Here YDR316W-A, YBR012W-A, YDR316W-B, YGR038C-A, and YDR210W-C were identified as the target genes of the TEC1-TYE7 complex and all of these genes belong to the retrotransposon TYA Gag gene which co-transcribe with TYB Pol gene. This supported the hypothesis that that cooperativity of TEC1 and TYE7 played a role in transposition, a major source of gene expansion during genome evolution. In addition, TYE7 also participated in the carbohydrate metabolism by cooperating with the GCR1-GCR2 complex. In previous experiments, GCR1 and TYE7 (SGC1) have been suggested to function on parallel pathways to activate glycolytic gene expression 33. Other evidence showed that

TYE7 was a multicopy suppressor of GCR2 mutants 34. In this study, we provide evidence that TYE7-GCR1-GCR2 work cooperatively to regulate particular genes: YAL038W (Cdc19), YGR192C (Tdh3), and YBR196C (Pgi1). DISCUSSION

Advantage of our method over existing methods

Combination of various types of biological data including whole genomic sequences, gene expression profiles, ChIP-chip data and protein-protein interactions to identify mechanisms of cooperative transcriptional regulation at a genomic scale can help partially overcome the limitations of any single data type, e.g. noises and incomplete information in single data type and correspondingly improve prediction accuracy 4-14. For example, Banerjee et al. 4 defined a novel measure of cooperativity by evaluating whether the presence of both TFs is required to influence gene expression. Nagamine et al. 6 integrated the protein-protein interactions with the ChIP-chip data to identify cooperative TFs. Yu et al. 13 developed an sequence-based approach to predict the cooperativity of TF pairs. Chen et al. 11, 12 combined microarray data and ChIP-chip data to identify the interaction patterns among multiple transcriptional factors by thermodynamic modeling. Wang et al. 14 designed a Bayesian network framework to reconstruct a whole-genome map of transcriptional cooperativity by integrating a comprehensive list of 15 genomic features.

Although the combination of ChIP-chip data with various genomic data, such as expression profiles, binding motifs and protein-protein interactions, can be used to identify potential cooperativity between TFs, the prediction accuracy is limited because: 1) not all the binding between TFs and genes are functional, i.e. regulating transcription; 2) the genomic data, serving as a measure of similarity of gene regulations, provides only weak and indirect evidences on TF cooperativity. Genetic approach, in particular gene perturbation experiments (e.g. transcription factor knockout experiments), which can identify the functional targets of transcriptional factors on a genomic scale 2, however can provide strong and direct evidence on TF functional cooperativity. This method demonstrated that combination of this functional information with binding evidences help identify cooperative TFs. We also devised a routine to choose the range of the p-value thresholds, avoiding the subjectivity of threshold choices. This method successfully identified many cooperative TFs confirmed by previous experiments and other potential cooperative TFs. Target genes of cooperative TFs

Our results suggested that though our method identified many cooperative TFs, it however only identified a few overlapping target genes (see Validation of target genes of cooperative TFs). This is mainly due to the low overlap between the ChIP-chip and the knockout data 2. A closer examination of the target genes detected from ChIP-chip data and knockout data showed that surprisingly, for each TF pair, the knockout p-values for most of their target genes detected from ChIP-chip data are too large to be the functional target for these TF pairs. Simultaneously, those genes with small p-value in the knockout data have large p-value in the ChIP-chip data. Cooperative TF pairs identified and shared by different data sets are statistical significant, even though their target genes are not shared. We propose a hypothesis, the effect of regulatory amplification, to explain this phenomenon. When two TFs

bind to the same gene (Gene A), they regulate the expression of this gene. The expression of Gene A may regulate other downstream genes, e.g. gene B. After several rounds of cascade regulation, the expression of Gene B would be induced or depressed significantly. Gene A will be detected as a target gene from ChIP-chip experiment because of the binding of these two TFs. However, the expression of Gene A may only be affected slightly and not enough to be detected as the target gene for this TF in the knockout experiment; while under the effect of regulatory amplification, the slightly change of the Gene A expression may induce the large change on the expression of Gene B. Under this hypothesis, one TF pair has two sets of target genes, one it bind directly and another it affect indirectly. These two sets may only share few common genes. Elucidation of this genetic regulation cascade is one major theme of the current biological research. Results from this work suggested that combination of the ChIP-chip data and knockout data might be one potential way to provide information on this signal transduction cascade. Table S1. 186 identified cooperative TF pairs.

TF1 TF2 minPAB

ChIP-chip minPAB

Knock-out combined

P-value Experimental

Evidence Computational

Evidence SWI4 SWI6 2.05E-148 6.44E-10 4.79E-155 35 1, 4, 7, 10, 36, 37 RAP1 SFP1 3.72E-03 2.79E-135 3.28E-135 38 1, 4, 6, 10, 36, 39, 40 TEC1 TYE7 2.15E-07 3.68E-106 2.05E-110 36 MBP1 SWI4 1.66E-52 3.54E-06 7.79E-56 41 1, 4-7, 10, 36, 42, 43

YDR049W YER130C 6.91E-32 5.10E-13 3.56E-42 HIR3 YOX1 3.04E-37 3.87E-04 1.09E-38 37 SPT23 YOX1 1.93E-35 1.43E-05 2.54E-38 1, 36 ACE2 SWI5 2.85E-24 3.97E-12 9.21E-34 44-46 4, 5, 7, 13, 36, 37, 39, 43, 47

DAL81 STP1 4.49E-12 2.63E-23 9.35E-33 48 4, 39 RGM1 YPR196W 2.72E-12 1.00E-22 2.13E-32 STB2 YDR049W 1.53E-17 1.23E-15 1.40E-30 MIG1 NRG1 1.51E-18 6.15E-14 6.72E-30 49 13

YDR049W YDR266C 3.02E-14 6.37E-16 1.29E-27 GAT3 RAP1 1.30E-26 4.69E-03 4.01E-27 6, 7, 13, 40 CAD1 YAP1 2.66E-14 4.89E-15 8.50E-27 50 1, 5, 36, 39, 42, 51 STP4 YPR196W 3.65E-17 6.83E-12 1.61E-26 STB2 YER130C 2.81E-15 9.11E-14 1.65E-26 GTS1 RIM101 1.68E-11 2.00E-17 2.15E-26

YDR266C YER130C 2.70E-12 3.88E-16 6.60E-26 GCR1 GCR2 7.61E-09 7.20E-19 3.37E-25 52 6, 13 RPN4 STB2 4.29E-19 1.73E-08 4.53E-25

ACE2 MBP1 3.36E-24 7.92E-03 1.60E-24 36, 37, 43, 51 GAL3 GAL80 2.74E-17 1.45E-09 2.37E-24 53 GAT3 RGM1 6.05E-13 8.93E-14 3.20E-24 1, 5-7, 13, 36, 39, 47 CHA4 GAT3 4.10E-06 5.26E-20 1.25E-23 STP4 TEC1 8.21E-06 7.86E-20 3.66E-23 RAP1 YAP5 5.37E-22 1.78E-03 5.38E-23 6, 7, 13, 36, 39, 40, 47, 51 RPI1 YLR278C 5.95E-05 5.72E-20 1.87E-22

RGM1 RPN4 2.62E-10 1.91E-14 2.73E-22 HSF1 RPN4 1.14E-13 5.37E-11 3.32E-22 54 10, 36 CIN5 CUP9 1.25E-11 4.90E-13 3.35E-22 4, 13 RDR1 YFL052W 2.32E-10 2.90E-14 3.65E-22 NRG1 SOK2 7.44E-20 9.55E-05 3.86E-22 4, 5, 36, 39 SKO1 SUT1 4.55E-14 2.40E-10 5.88E-22 36, 55 NRG1 SKO1 4.26E-21 6.17E-03 1.39E-21 13, 36, 39

YFL052W YLR278C 5.71E-10 7.74E-14 2.32E-21 AFT2 UGA3 8.32E-05 1.15E-18 4.94E-21 36

YDR049W YRR1 1.60E-11 6.83E-12 5.65E-21 MET18 RGM1 5.20E-10 3.40E-13 9.02E-21 RPN4 YER130C 1.24E-11 2.03E-11 1.28E-20 CUP9 HMS1 2.30E-11 1.80E-11 2.07E-20 CHA4 YER130C 7.07E-06 7.00E-17 2.48E-20 YHP1 YOX1 4.26E-03 1.42E-19 3.01E-20 56 13, 36 GAT3 YPR196W 7.52E-10 1.29E-12 4.80E-20 AFT2 RGM1 9.88E-09 1.29E-13 6.24E-20 KSS1 YER130C 4.18E-05 1.06E-16 2.12E-19 STB2 YRR1 1.45E-16 3.43E-05 2.37E-19 STB2 YHP1 1.83E-05 3.81E-16 3.30E-19

ARG80 YOX1 4.21E-12 3.96E-09 7.77E-19 CUP9 YAP6 2.17E-17 1.07E-03 1.08E-18 5, 13, 51 STB2 YDR266C 2.12E-09 2.09E-11 2.02E-18

YER130C YHP1 2.82E-06 2.19E-14 2.79E-18 ARG80 ARG81 9.45E-13 6.59E-08 2.82E-18 57 4-6, 51 GAT3 TEC1 6.37E-03 1.75E-17 4.99E-18 ROX1 SMP1 2.45E-08 5.36E-12 5.84E-18 WAR1 YLR278C 2.55E-17 6.87E-03 7.74E-18

YFL052W YPR022C 2.19E-07 9.06E-13 8.76E-18 GAL3 YDR266C 1.55E-12 2.27E-07 1.53E-17 NRG1 SWI4 8.16E-09 4.85E-11 1.72E-17 13, 36, 37 MCM1 SWI4 3.61E-07 1.56E-12 2.42E-17 1, 6, 10, 13, 36, 42, 51 MOT3 ROX1 6.09E-09 9.78E-11 2.56E-17 1, 5, 36

YFL052W OAF3 1.74E-08 8.69E-11 6.34E-17 MIG3 YOX1 8.76E-04 5.30E-15 1.90E-16 MIG3 NRG1 3.06E-10 2.33E-08 2.89E-16 49

CST6 YER130C 1.11E-08 1.89E-09 8.30E-16 MET18 YPR196W 3.37E-13 6.26E-05 8.32E-16 GCR1 TYE7 3.33E-10 1.65E-07 2.11E-15 33 1, 36 RPN4 SMP1 1.70E-07 3.51E-10 2.29E-15 LEU3 MAC1 4.49E-04 1.41E-13 2.42E-15

ARG80 SPT23 1.97E-11 4.93E-06 3.67E-15 SWI6 TEC1 2.60E-04 4.00E-13 3.93E-15 27 1, 36, 37, 39 SNT2 STP4 1.41E-05 1.16E-11 6.13E-15

YDR049W YHP1 1.18E-04 1.81E-12 7.90E-15 SKO1 SWI4 4.74E-11 4.59E-06 8.07E-15 36 MIG1 MIG3 1.75E-04 2.12E-12 1.36E-14 PDR1 SWI5 6.22E-13 1.27E-03 2.83E-14 13, 51 GAT3 ZAP1 6.37E-11 1.58E-05 3.57E-14 13, 43 CST6 GAT3 1.19E-04 1.03E-11 4.31E-14 PHD1 SKN7 4.78E-10 2.68E-06 4.52E-14 1, 13, 36, 39, 58 KSS1 STB4 1.02E-06 1.35E-09 4.89E-14 RGM1 STP4 1.03E-04 1.72E-11 6.22E-14 MCM1 YOX1 3.51E-11 5.28E-05 6.47E-14 56 36 KSS1 YDR049W 2.43E-03 7.66E-13 6.51E-14 HIR3 MET28 2.98E-06 6.37E-10 6.63E-14 HIR1 HIR3 2.68E-04 7.75E-12 7.22E-14 59 7, 37, 39 ACE2 SWI4 7.29E-13 2.98E-03 7.54E-14 13, 36, 43, 51 AFT2 RPN4 1.66E-04 1.33E-11 7.67E-14 36

GAL80 SIP4 1.14E-04 2.06E-11 8.17E-14 GAT3 STP4 6.26E-03 4.10E-13 8.88E-14

MET18 SPT23 1.79E-11 1.55E-04 9.61E-14 MIG1 SOK2 3.35E-09 1.17E-06 1.34E-13 GLN3 HAP4 1.28E-12 3.11E-03 1.36E-13 RGM1 SMP1 7.06E-12 5.71E-04 1.38E-13 13, 40 WAR1 YFL052W 1.33E-11 3.11E-04 1.41E-13 CIN5 PHD1 5.06E-12 1.15E-03 1.97E-13 1, 4, 13, 36, 39 MIG3 SOK2 8.03E-09 1.16E-06 3.10E-13

YML081W YOX1 1.63E-11 6.73E-04 3.64E-13 GAL80 YDR266C 1.57E-10 7.17E-05 3.72E-13 ACE2 SKN7 4.86E-10 3.78E-05 6.00E-13 4, 13, 36, 39, 42, 51 SWI5 YAP3 5.94E-03 3.42E-12 6.62E-13 MIG3 STP4 8.31E-04 3.37E-11 9.02E-13 MSN2 SWI5 3.19E-05 1.15E-09 1.17E-12 36 CUP9 PHD1 7.99E-12 5.11E-03 1.30E-12 4, 13 OPI1 YML081W 2.36E-09 3.04E-05 2.25E-12

MET18 STP4 4.22E-10 1.92E-04 2.52E-12 CIN5 MIG1 6.14E-07 2.74E-07 5.12E-12 13 RPN4 YPR196W 9.59E-03 1.89E-11 5.51E-12

MIG3 YPR196W 8.62E-06 4.55E-08 1.16E-11 ACE2 GAL4 6.22E-03 6.66E-11 1.22E-11 FZF1 HIR1 1.38E-06 1.00E-06 3.91E-11 GCR2 TYE7 4.36E-04 3.24E-09 4.00E-11 RGM1 ZAP1 2.51E-03 8.48E-10 5.94E-11 13 RDR1 OAF3 2.43E-03 2.16E-09 1.41E-10 CHA4 ZAP1 8.64E-09 8.19E-04 1.89E-10 GCR2 SPT2 6.05E-06 1.20E-06 1.93E-10 EDS1 RPN4 2.38E-07 3.20E-05 2.02E-10 AFT2 MET28 4.66E-05 2.01E-07 2.47E-10 CST6 RPN4 6.55E-04 1.99E-08 3.39E-10 CHA4 CST6 3.14E-03 5.32E-09 4.31E-10 CST6 STB2 3.35E-06 5.68E-06 4.89E-10 SPT23 STP4 4.01E-04 6.88E-08 6.99E-10 IXR1 NRG1 1.55E-06 1.95E-05 7.61E-10 13 CIN5 SWI4 1.36E-08 2.38E-03 8.18E-10 13, 36 RPN4 YFL052W 6.47E-03 7.19E-09 1.15E-09 SMP1 SWI5 1.77E-07 3.19E-04 1.39E-09 4, 13, 51 GCR2 MCM1 8.71E-05 8.58E-07 1.82E-09 43 AFT2 YPR196W 6.62E-06 1.28E-05 2.05E-09 RTG3 YOX1 1.86E-07 5.52E-04 2.47E-09

GAL80 YDR049W 8.76E-04 1.90E-07 3.91E-09 PPR1 YDR049W 2.94E-04 5.92E-07 4.09E-09 HIR3 STP1 8.85E-03 2.58E-08 5.30E-09 CUP9 SMP1 2.94E-04 8.85E-07 6.00E-09 GCR2 STP1 1.19E-05 2.28E-05 6.23E-09 HAP2 HAP4 4.47E-07 6.29E-04 6.46E-09 60, 61 1, 5, 36, 39, 42 LEU3 YOX1 1.27E-05 2.32E-05 6.76E-09

MET28 NDT80 2.15E-05 3.15E-05 1.50E-08 GAL4 RAP1 7.38E-03 1.34E-07 2.14E-08 62

MET18 YOX1 9.77E-06 1.06E-04 2.24E-08 37 CHA4 YAP5 4.83E-07 2.17E-03 2.27E-08 GLN3 RAP1 2.48E-04 5.01E-06 2.67E-08 36 GAT3 RPN4 7.14E-07 1.77E-03 2.72E-08 CAD1 RGM1 2.95E-03 1.39E-06 8.32E-08 BAS1 STP1 2.26E-03 2.12E-06 9.68E-08 KSS1 YDR266C 4.14E-03 1.30E-06 1.08E-07 NRG1 SWI5 2.50E-04 4.69E-05 2.26E-07 36 MOT3 SOK2 5.92E-04 2.10E-05 2.39E-07 36, 58 STB4 YDR049W 1.90E-03 7.25E-06 2.64E-07 GAL3 SIP4 3.52E-03 4.29E-06 2.87E-07

ARO80 HAP2 5.16E-03 3.32E-06 3.23E-07 MBP1 MCM1 9.46E-06 1.90E-03 3.38E-07 6, 10, 36, 42, 51

HIR2 ZAP1 9.38E-06 2.41E-03 4.20E-07 CST6 GLN3 8.58E-03 2.65E-06 4.23E-07 STP1 STP2 3.86E-04 6.53E-05 4.66E-07 63

YDR266C YRR1 6.71E-03 3.82E-06 4.74E-07 STP1 YOX1 4.29E-04 6.49E-05 5.12E-07 HMS1 RTG3 9.01E-06 3.16E-03 5.23E-07 GAL80 STB2 2.18E-04 1.58E-04 6.25E-07 GCR2 SMP1 9.86E-06 3.72E-03 6.65E-07 JHD1 YER130C 5.78E-05 6.52E-04 6.82E-07 PHO2 SWI4 2.76E-03 1.47E-05 7.31E-07 5 GAL4 SMP1 2.40E-04 1.83E-04 7.88E-07 13, 43 MIG3 YML081W 3.80E-04 1.49E-04 1.00E-06 GAL4 SWI5 3.12E-03 1.95E-05 1.07E-06 4, 13 GAT3 HIR2 7.05E-06 8.64E-03 1.07E-06 40 RPN4 YRR1 2.60E-05 2.51E-03 1.14E-06 64 HAP5 HMS1 5.48E-03 1.43E-05 1.36E-06 CST6 YDR266C 1.48E-04 6.40E-04 1.63E-06 SKO1 SOK2 2.88E-05 3.37E-03 1.67E-06 1, 36, 39, 58 LEU3 SPT23 3.92E-03 3.01E-05 2.00E-06 SPT2 YML081W 9.61E-03 1.33E-05 2.16E-06 TEC1 YAP5 8.85E-05 1.90E-03 2.79E-06 CST6 GAL4 2.41E-04 7.57E-04 3.01E-06 MIG3 SPT23 5.99E-05 3.45E-03 3.39E-06 CST6 RGM1 8.92E-03 2.70E-05 3.91E-06 RDR1 WAR1 1.46E-03 1.79E-04 4.24E-06 NRG1 SFP1 8.93E-03 2.98E-05 4.30E-06 36 CUP9 SWI4 5.19E-04 5.78E-04 4.80E-06 SUT1 YOX1 2.82E-03 1.09E-04 4.93E-06 ACE2 SMP1 2.72E-03 1.66E-04 7.03E-06 HAP2 ROX1 2.02E-03 3.47E-04 1.06E-05 10, 36 HMS2 SKN7 1.75E-03 4.44E-04 1.17E-05 ARO80 NRG1 5.29E-04 3.81E-03 2.85E-05 4 HAC1 YDR266C 1.86E-03 1.52E-03 3.90E-05 FZF1 STP1 1.05E-03 8.20E-03 1.09E-04 DAT1 RPN4 1.27E-03 7.99E-03 1.27E-04 SWI4 UME6 2.80E-03 5.33E-03 1.81E-04 36, 65

Table S2. Standard dataset for the evaluation of four methods. ARG80-MCM1 ARG80-ARG81 MET4-CBF1 MET4-MET32 MET4-MET28 MET4-MET31 GCR1-GCR2 STP4-STP2 STP4-STP1 CBF1-MET28 RTG1-RTG3 SWI4-SWI6 IME1-UME6 MCM1-ARG81 SWI6-MBP1

GAL80-GAL4 HAP5-HAP4 HAP5-HAP2 HAP5-HAP3 MET32-MET28 STP2-STP1 HAP4-HAP2 HAP4-HAP3 MET28-MET31 HAP2-HAP3 PIP2-OAF1 GAL3-GAL80

Table S3. Comparison of our method with existing methods. MIPS complex (MIPSco; 29 TFs, 27 TF pairs)

Our Method Banerjee et al. Nagamine et al. Yu et al.

Number of overlapping TFs 20 8 10 16 Number of possible cooperativities among overlapping TFs

190 28 45 120

Number of MIPSco cooperativities among overlapping TFs

14 5 7 13

Number of predicted cooperativities among overlapping TFs

12 3 6 10

Number of MIPSco cooperativities that are correctly predicted

6 3 4 6

Fisher's exact test p-value 3.65E-05 3.05E-03 3.12E-03 7.93E-05

Table S4. Comparison of our results with those only using p-value threshold 0.001 and 0.005.

TF1 TF2 In

P<=0.001

Not

in P<=0.005

Evid

ences

TF1 TF2 In

P<=0.001

Not

in P<=0.005

Eviden

ces

AFT2 UGA3 + - * GAT3 RPN4 -

CST6 GAT3 + - GAT3 STP4 -

KSS1 YDR266C + - GAT3 TEC1 -

MET18 YOX1 + - * GCR1 TYE7 - ***

SNT2 STP4 + - GCR2 SMP1 -

YML081W YOX1 + - GCR2 TYE7 -

ACE2 SWI5 + *** HAC1 YDR266C -

ARG80 ARG81 + *** HAP2 ROX1 - *

CAD1 YAP1 + *** HAP5 HMS1 -

CIN5 CUP9 + * HIR1 HIR3 - *

DAL81 STP1 + *** HIR2 ZAP1 -

GAT3 RGM1 + * HIR3 STP1 -

GAT3 ZAP1 + * HMS1 RTG3 -

GCR1 GCR2 + *** HMS2 SKN7 -

IXR1 NRG1 + * LEU3 SPT23 -

MIG1 NRG1 + *** MBP1 MCM1 - *

NRG1 SWI4 + * MET28 NDT80 -

RPN4 STB2 + MIG1 MIG3 -

STB2 YER130C + MIG3 STP4 -

STP4 YPR196W + MIG3 YML081W -

ACE2 GAL4 - MOT3 SOK2 - *

ACE2 MBP1 - * NRG1 SFP1 - *

ACE2 SMP1 - NRG1 SKO1 - *

AFT2 MET28 - PDR1 SWI5 - *

ARO80 HAP2 - PHO2 SWI4 - *

BAS1 STP1 - PPR1 YDR049W -

CAD1 RGM1 - RAP1 SFP1 - ***

CHA4 GAT3 - RDR1 OAF3 -

CHA4 YAP5 - RGM1 STP4 -

CHA4 ZAP1 - RGM1 ZAP1 - *

CIN5 PHD1 - * RPN4 YFL052W -

CIN5 SWI4 - * RPN4 YPR196W -

CST6 GAL4 - RPN4 YRR1 - **

CST6 RGM1 - RTG3 YOX1 -

CUP9 SWI4 - SPT2 YML081W -

DAT1 RPN4 - SWI4 UME6 - *

FZF1 STP1 - SWI5 YAP3 -

GAL4 RAP1 - ** TEC1 YAP5 -

GAL4 SMP1 - * WAR1 YLR278C -

GAL4 SWI5 - * YDR266C YRR1 -

GAT3 HIR2 - * YHP1 YOX1 - ***

GAT3 RAP1 - *

Note: In Column (In P<=0.001), “+” means identified using p-value threshold 0.001; In Column (Not in P<=0.005), “-” means predicted by our method but not in the results using p-value threshold 0.005 without any optimal principle; In Column (Evidences), “*”: only supported by computational evidences; “**”: only supported by experimental evidences; “***”: supported by both kinds of evidences.

Table S5. The common 184 TFs between the ChIP-chip and the knockout data. ABF1 DAL82 HAP2 MCM1 PDR1 RTG1 STP4 YAP3 ACA1 DAT1 HAP3 MDS3 PDR3 RTG3 SUM1 YAP5 ACE2 DIG1 HAP4 MET18 PHD1 SFL1 SUT1 YAP6 ADR1 DOT6 HAP5 MET28 PHO2 SFP1 SUT2 YAP7 AFT2 ECM22 HIR1 MET31 PHO4 SIP3 SWI4 YBR239C ARG80 EDS1 HIR2 MET32 PIP2 SIP4 SWI5 YDR026C ARG81 FKH1 HIR3 MGA1 PPR1 SKN7 SWI6 YDR049W ARO80 FKH2 HMS1 MIG1 PUT3 SKO1 TBS1 YDR266C ARR1 FZF1 HMS2 MIG2 RAP1 SMK1 TEC1 YER130C ASH1 GAL3 HOG1 MIG3 RCO1 SMP1 THI2 YER184C ASK10 GAL4 HSF1 MOT3 RDR1 SNF1 TOD6 YFL052W AZF1 GAL80 IME1 MSN1 RDS1 SNT2 TOS8 YGR067C BAS1 GAT1 INO2 MSN2 REB1 SOK2 TYE7 YHP1

BYE1 GAT3 INO4 MSN4 RFX1 SPT10 UGA3 YJL206C CAD1 GCN4 IXR1 MSS11 RGM1 SPT2 UME6 YKL222C CBF1 GCR1 JHD1 MTH1 RGT1 SPT23 UPC2 YLR278C CHA4 GCR2 KSS1 NDT80 RIM101 STB1 URC2 YML081WCIN5 GLN3 LEU3 NNF2 RLM1 STB2 USV1 YNR063W CRZ1 GTS1 MAC1 NRG1 RME1 STB4 WAR1 YOX1 CST6 GZF3 MAL13 OAF1 ROX1 STB5 WTM1 YPR022C CUP9 HAA1 MAL33 OAF3 RPH1 STB6 WTM2 YPR196W DAL80 HAC1 MBF1 OPI1 RPI1 STP1 XBP1 YRR1 DAL81 HAL9 MBP1 OTU1 RPN4 STP2 YAP1 ZAP1

Table S6. Validation of target genes of TF pairs.

TF1 TF2 Gene Description TF1_

cc_p-value

TF2_cc_

p-value

TF1_ko

_p-value

TF2_ko_p-valu

e

TEC1 TYE7 YBR012W

-A

Retrotransposon TYA Gag gene

co-transcribed with TYB Pol; translated

as TYA or TYA-TYB polyprotein; Gag is

a nucleocapsid protein that is the

structural constituent of virus-like

particles (VLPs); similar to retroviral Gag

0.000847 0.00286 1.39E-0

9 5.51E-05

YDR316W

-B

Retrotransposon TYA Gag and TYB Pol

genes; transcribed/translated as one

unit; polyprotein is processed to make a

nucleocapsid-like protein (Gag), reverse

transcriptase (RT), protease (PR), and

integrase (IN); similar to retroviral genes

0.000277 0.00030

4

6.38E-1

0 4.75E-05

YGR038C

-A







0.00384 0.00022

2

9.93E-0

9 3.15E-05

YDR316W

-A







0.000277 0.00030

4

9.18E-0

8 1.75E-05

YDR210W

-C






0.00337 9.84E-0

5

1.97E-1

0 4.35E-05


GCR2 TYE7 YGR192C

Glyceraldehyde-3-phosphate

dehydrogenase, isozyme 3, involved in

glycolysis and gluconeogenesis;

tetramer that catalyzes the reaction of

glyceraldehyde-3-phosphate to 1,3

bis-phosphoglycerate; detected in the

cytoplasm and cell-wall

0.000398 1.23E-0

5

4.78E-0

6 0.0029196

GCR1 TYE7 YAL038W

Pyruvate kinase, functions as a

homotetramer in glycolysis to convert

phosphoenolpyruvate to pyruvate, the

input for aerobic (TCA cycle) or

anaerobic (glucose fermentation)

respiration

9.94E-06 2.01E-0

4

2.42E-1

1 0.00239047

YGR192C








8.33E-05 1.23E-0

5

8.33E-1

4 0.0029196

YGR038C

-A







0.000112 0.00022

2

0.00018

8627 3.15E-05

YDR210W

-C







4.25E-05 9.84E-0

5

0.00016

5473 4.35E-05

ARG8

0

ARG

81 YOL140W

Acetylornithine aminotransferase,

catalyzes the fourth step in the

biosynthesis of the arginine precursor

ornithine

1.26E-07 1.61E-1

3

7.48E-1

2 1.66E-11

YJL088W

Ornithine carbamoyltransferase

(carbamoylphosphate:L-ornithine

carbamoyltransferase), catalyzes the

sixth step in the biosynthesis of the

0.000554 1.55E-0

9

1.73E-1

1 2.09E-11

arginine precursor ornithine

YOR303W

Small subunit of carbamoyl phosphate

synthetase, which catalyzes a step in the

synthesis of citrulline, an arginine

precursor; translationally regulated by an

attenuator peptide encoded by

YOR302W within the CPA1 mRNA

5'-leader

0.000159 7.97E-0

7

1.48E-0

5 0.0016613

YER069W

Protein that is processed in the

mitochondrion to yield acetylglutamate

kinase and

N-acetyl-gamma-glutamyl-phosphate

reductase, which catalyze the 2nd and

3rd steps in arginine biosynthesis;

enzymes form a complex with Arg2p

2.08E-07 1.13E-1

1

2.13E-0

6 3.08E-06

SWI4 SWI6 YNL300W

Glycosylphosphatidylinositol-dependent

cell wall protein, expression is periodic

and decreases in respone to ergosterol

perturbation or upon entry into stationary

phase; depletion increases resistance to

lactic acid

3.28E-09 1.28E-1

1

1.77E-0

6 6.62E-06

YKL096W

Cell wall mannoprotein, linked to a

beta-1,3- and beta-1,6-glucan

heteropolymer through a phosphodiester

bond; involved in cell wall organization;

required for propionic acid resistancethe

cell wall; involved in low pH resistance;

precursor is GPI-anchored

1.36E-07 5.17E-0

4

0.00384

714 0.000450388

YOR247W

Mannoprotein that exhibits a tight

association with the cell wall, required

for cell wall stability in the absence of

GPI-anchored mannoproteins; has a

high serine-threonine content;

expression is induced in cell wall

mutants

1.14E-09 2.22E-1

6

1.53E-0

6 0.00128777

YOR248W

Dubious open reading frame unlikely to

encode a functional protein, based on

available experimental and comparative

sequence data

1.14E-09 2.22E-1

6

1.12E-0

5 0.00130004

YKL096W-

A

Covalently linked cell wall mannoprotein,

major constituent of the cell wall; plays a

role in stabilizing the cell wall; involved in

low pH resistance; precursor is

2.53E-10 8.68E-1

2

6.76E-0

5 0.00260588

GPI-anchored

SKO1 SUT1 YKL096W





required for propionic acid resistance

0.00052 0.00030

6

9.63E-0

7 0.00277112

GCR1 GCR

2 YBR196C

Glycolytic enzyme phosphoglucose

isomerase, catalyzes the

interconversion of glucose-6-phosphate

and fructose-6-phosphate; required for

cell cycle progression and completion of

the gluconeogenic events of sporulation

0.000193 0.00051

3

2.30E-1

0 7.82E-05

YGR192C








8.33E-05 3.98E-0

4

8.33E-1

4 4.78E-06

SKO1 SWI4 YKL096W





required for propionic acid resistance

0.00052 1.36E-0

7

9.63E-0

7 0.00384714

SWI4 UME

6 YML100W

Large subunit of trehalose 6-phosphate

synthase (Tps1p)/phosphatase (Tps2p)

complex, which converts

uridine-5'-diphosphoglucose and

glucose 6-phosphate to trehalose,

homologous to Tps3p and may share

function

4.68E-05 3.04E-0

3

1.01E-0

6 0.000433485

RAP1 SFP1 YER117W

Protein component of the large (60S)

ribosomal subunit, identical to Rpl23Ap

and has similarity to E. coli L14 and rat

L23 ribosomal proteins

0.000481 0.00222 1.39E-1

0 1.46E-05

YDL082W

Protein component of the large (60S)

ribosomal subunit, nearly identical to

Rpl13Bp; not essential for viability; has

similarity to rat L13 ribosomal protein

0.000831 0.00492 0.00063

6884 7.37E-06

YNL096C

Protein component of the small (40S)

ribosomal subunit, nearly identical to

Rps7Ap; interacts with Kti11p; deletion

causes hypersensitivity to zymocin; has

similarity to rat S7 and Xenopus S8

ribosomal proteins

0.000218 0.00449 1.13E-0

9 3.15E-08

Figures

Figure S1: the schema of our method.

Bonferroni

Correction

TF knockout

ChIP-chip

TF A

TF B

TA

TB

PA

PB

Threshold selection

Hypergeometric tests

Optimize Procedure

TF A

TF B

PA

PB

Threshold selection

Hypergeometric tests

Optimize Procedure

Bonferroni

Correction

Fisher combined

Probability

TA

IAB

TB

IAB

Figure S2: the increasing number of cooperative TF pairs identified within each range. The total numbers of pairs identified when thresholds are relaxed to certain points are shown above the bar.

200 400 600 800 1000

0.00

0.05

0.10

0.15

0.20

0.25

0.30

the size of positive sets

Jacc

ard

Sim

ilarit

y S

core

200 400 600 800 1000

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Figure S3 Comparing our method with the one using the same relaxed threshold 0.005 but without the optimal procedure based on Jaccard similarity score.

Our method

the method without optimization

Figure S4: Prediction of uncharacterized TFs by their direct cooperative TFs. This network shows the relations between uncharacterized TFs and their direct cooperative TFs. Blue Node: Cell Cycle; Red Node: Stress Response; Green Node: Metabolism; Pink Node: Multiple Function; Yellow Node: Uncharacterized.

Figure S5: Validation of target genes of TF pairs. TFs are represented by colored circles. Blue Node: Cell Cycle; Red Node: Stress Response; Green Node: Metabolism; Pink Node: Multiple Function or Uncertain; Target genes are represented by yellow rectangles.

REFERENCES

1. Harbison CT, Gordon DB, Lee TI et al. Transcriptional regulatory code of a eukaryotic genome. Nature 2004; 431 (7004):99-104. 2. Hu Z, Killion PJ, Iyer VR. Genetic reconstruction of a functional transcriptional regulatory network. Nat Genet 2007; 39 (5):683-687. 3. Fisher RA. Statistical methods for research workers. Oliver and Boyd 1970.

4. Banerjee N, Zhang MQ. Identifying cooperativity among transcription factors controlling the cell cycle in yeast. Nucleic Acids Res 2003; 31 (23):7024-7031. 5. Manke T, Bringas R, Vingron M. Correlating protein-DNA and protein-protein interaction networks. J Mol Biol 2003; 333 (1):75-85. 6. Nagamine N, Kawada Y, Sakakibara Y. Identifying cooperative transcriptional regulations using protein-protein interactions. Nucleic Acids Res 2005; 33 (15):4828-4837. 7. Wu WS, Li WH, Chen BS. Computational reconstruction of transcriptional regulatory modules of the yeast cell cycle. BMC Bioinformatics 2006; 7:421. 8. Wang RS, Wang Y, Zhang XS, Chen L. Inferring transcriptional regulatory networks from high-throughput data. Bioinformatics 2007; 23 (22):3056-3064. 9. Datta D, Zhao H. Statistical methods to infer cooperative binding among transcription factors in Saccharomyces cerevisiae. Bioinformatics 2008; 24 (4):545-552. 10. Lee HG, Lee HS, Jeon SH et al. High-resolution analysis of condition-specific regulatory modules in Saccharomyces cerevisiae. Genome Biol 2008; 9:R2. 11. Chen CC, Zhong S. Inferring gene regulatory networks by thermodynamic modeling. BMC Genomics 2008; 9 Suppl 2:S19. 12. Chen CC, Zhu XG, Zhong S. Selection of thermodynamic models for combinatorial control of multiple transcription factors in early differentiation of embryonic stem cells. BMC Genomics 2008; 9 Suppl 1:S18. 13. Yu X, Lin J, Masuda T et al. Genome-wide prediction and characterization of interactions between transcription factors in Saccharomyces cerevisiae. Nucleic Acids Res 2006; 34 (3):917-927. 14. Wang Y, Zhang XS, Xia Y. Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data. Nucleic Acids Res 2009. 15. Shannon P, Markiel A, Ozier O et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13 (11):2498-2504. 16. Lemmens K, Dhollander T, De Bie T et al. Inferring transcriptional modules from ChIP-chip, motif and microarray data. Genome Biol 2006; 7 (5):R37. 17. Smith JJ, Ramsey SA, Marelli M et al. Transcriptional responses to fatty acid are coordinated by combinatorial control. Mol Syst Biol 2007; 3:115. 18. Hellauer K, Akache B, MacPherson S, Sirard E, Turcotte B. Zinc cluster protein Rdr1p is a transcriptional repressor of the PDR5 gene encoding a multidrug transporter. J Biol Chem 2002; 277 (20):17671-17676. 19. Kren A, Mamnun YM, Bauer BE et al. War1p, a novel transcription factor controlling weak acid stress response in yeast. Mol Cell Biol 2003; 23 (5):1775-1785. 20. Mira NP, Lourenco AB, Fernandes AR, Becker JD, Sa-Correia I. The RIM101 pathway has a role in Saccharomyces cerevisiae adaptive response and resistance to propionic acid and other weak acids. FEMS Yeast Res 2009; 9 (2):202-216. 21. Segal E, Shapira M, Regev A et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 2003; 34 (2):166-176. 22. Jauniaux JC, Urrestarazu LA, Wiame JM. Arginine metabolism in Saccharomyces cerevisiae: subcellular localization of the enzymes. J Bacteriol 1978; 133 (3):1096-1107. 23. Minet M, Jauniaux JC, Thuriaux P, Grenson M, Wiame JM. Organization and expression of a two-gene cluster in the arginine biosynthesis of Saccharomyces cerevisiae. Mol Gen Genet 1979; 168 (3):299-308.

24. Lieb JD, Liu X, Botstein D, Brown PO. Promoter-specific binding of Rap1 revealed by genome-wide maps of protein-DNA association. Nat Genet 2001; 28 (4):327-334. 25. Jorgensen P, Nishikawa JL, Breitkreutz BJ, Tyers M. Systematic identification of pathways that couple cell growth and division in yeast. Science 2002; 297 (5580):395-400. 26. Planta RJ, Mager WH. The list of cytoplasmic ribosomal proteins of Saccharomyces cerevisiae. Yeast 1998; 14 (5):471-477. 27. Iyer VR, Horak CE, Scafe CS et al. Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF. Nature 2001; 409 (6819):533-538. 28. Igual JC, Johnson AL, Johnston LH. Coordinated regulation of gene expression by the cell cycle transcription factor Swi4 and the protein kinase C MAP kinase pathway for yeast cell integrity. EMBO J 1996; 15 (18):5001-5013. 29. Ferreira JC, Thevelein JM, Hohmann S et al. Trehalose accumulation in mutants of Saccharomyces cerevisiae deleted in the UDPG-dependent trehalose synthase-phosphatase complex. Biochim Biophys Acta 1997; 1335 (1-2):40-50. 30. Pereira MD, Eleutherio EC, Panek AD. Acquisition of tolerance against oxidative damage in Saccharomyces cerevisiae. BMC Microbiol 2001; 1:11. 31. Laloux I, Dubois E, Dewerchin M, Jacobs E. TEC1, a gene involved in the activation of Ty1 and Ty1-mediated gene expression in Saccharomyces cerevisiae: cloning and molecular analysis. Mol Cell Biol 1990; 10 (7):3541-3550. 32. Lohning C, Ciriacy M. The TYE7 gene of Saccharomyces cerevisiae encodes a putative bHLH-LZ transcription factor required for Ty1-mediated gene expression. Yeast 1994; 10 (10):1329-1339. 33. Nishi K, Park CS, Pepper AE et al. The GCR1 requirement for yeast glycolytic gene expression is suppressed by dominant mutations in the SGC1 gene, which encodes a novel basic-helix-loop-helix protein. Mol Cell Biol 1995; 15 (5):2646-2653. 34. Sato T, Lopez MC, Sugioka S et al. The E-box DNA binding protein Sgc1p suppresses the gcr2 mutation, which is involved in transcriptional activation of glycolytic genes in Saccharomyces cerevisiae. FEBS Lett 1999; 463 (3):307-311. 35. Koch C, Moll T, Neuberg M, Ahorn H, Nasmyth K. A role for the transcription factors Mbp1 and Swi4 in progression from G1 to S phase. Science 1993; 261 (5128):1551-1557. 36. Pham TH, Clemente JC, Satou K, Ho TB. Computational discovery of transcriptional regulatory rules. Bioinformatics 2005; 21 Suppl 2:ii101-107. 37. Tsai HK, Lu HH, Li WH. Statistical methods for identifying yeast cell cycle transcription factors. Proc Natl Acad Sci U S A 2005; 102 (38):13532-13537. 38. Coleman CB, Gonzalez-Villalobos RA, Allen PL et al. Diamagnetic levitation changes growth, cell cycle, and gene expression of Saccharomyces cerevisiae. Biotechnol Bioeng 2007; 98 (4):854-863. 39. Boorsma A, Lu XJ, Zakrzewska A, Klis FM, Bussemaker HJ. Inferring condition-specific modulation of transcription factor activity in yeast through regulon-based analysis of genomewide expression. PLoS ONE 2008; 3 (9):e3112. 40. Hvidsten TR, Wilczynski B, Kryshtafovych A et al. Discovering regulatory binding-site modules using rule-based learning. Genome Res 2005; 15 (6):856-866. 41. Horak CE, Luscombe NM, Qian J et al. Complex transcriptional circuitry at the G1/S transition in Saccharomyces cerevisiae. Genes Dev 2002; 16 (23):3017-3033. 42. Chen G, Jensen ST, Stoeckert CJ, Jr. Clustering of genes into regulons using integrated

modeling-COGRIM. Genome Biol 2007; 8 (1):R4. 43. Dong He DZ, Yanhong Zhou, et al. Identifying synergistic transcriptional factors involved in the yeast cell cycle using Microarray and ChIP-chip data. Proceedings of the 5th International Conference on GCC Workshops: Hunan 2006:4. 44. Dohrmann PR, Voth WP, Stillman DJ. Role of negative regulation in promoter specificity of the homologous transcriptional activators Ace2p and Swi5p. Mol Cell Biol 1996; 16 (4):1746-1758. 45. Doolin MT, Johnson AL, Johnston LH, Butler G. Overlapping and distinct roles of the duplicated yeast transcription factors Ace2p and Swi5p. Mol Microbiol 2001; 40 (2):422-432. 46. Stillman DJ, Dorland S, Yu Y. Epistasis analysis of suppressor mutations that allow HO expression in the absence of the yeast SW15 transcriptional activator. Genetics 1994; 136 (3):781-788. 47. Noto K, Craven M. Learning probabilistic models of cis-regulatory modules that represent logical and spatial aspects. Bioinformatics 2007; 23 (2):e156-162. 48. Boban M, Ljungdahl PO. Dal81 enhances Stp1- and Stp2-dependent transcription necessitating negative modulation by inner nuclear membrane protein Asi1 in Saccharomyces cerevisiae. Genetics 2007; 176 (4):2087-2097. 49. Zhou H, Winston F. NRG1 is required for glucose repression of the SUC2 and GAL genes of Saccharomyces cerevisiae. BMC Genet 2001; 2:5. 50. Cohen BA, Pilpel Y, Mitra RD, Church GM. Discrimination between paralogs using microarray analysis: application to the Yap1p and Yap2p transcriptional networks. Mol Biol Cell 2002; 13 (5):1608-1614. 51. Schlitt T, Palin K, Rung J et al. From gene networks to gene function. Genome Res 2003; 13 (12):2568-2576. 52. Uemura H, Jigami Y. Role of GCR2 in transcriptional activation of yeast glycolytic genes. Mol Cell Biol 1992; 12 (9):3834-3842. 53. Sil AK, Alam S, Xin P et al. The Gal3p-Gal80p-Gal4p transcription switch of yeast: Gal3p destabilizes the Gal80p-Gal4p complex in response to galactose and ATP. Mol Cell Biol 1999; 19 (11):7828-7840. 54. Hahn JS, Neef DW, Thiele DJ. A stress regulatory network for co-ordinated activation of proteasome expression mediated by yeast heat shock transcription factor. Mol Microbiol 2006; 60 (1):240-251. 55. Buck MJ, Lieb JD. A chromatin-mediated mechanism for specification of conditional transcription factor targets. Nat Genet 2006; 38 (12):1446-1451. 56. Pramila T, Miles S, GuhaThakurta D, Jemiolo D, Breeden LL. Conserved homeodomain proteins interact with MADS box protein Mcm1 to restrict ECB-dependent transcription to the M/G1 phase of the cell cycle. Genes Dev 2002; 16 (23):3034-3045. 57. Amar N, Messenguy F, El Bakkoury M, Dubois E. ArgRII, a component of the ArgR-Mcm1 complex involved in the control of arginine metabolism in Saccharomyces cerevisiae, is the sensor of arginine. Mol Cell Biol 2000; 20 (6):2087-2097. 58. Carter GW, Prinz S, Neou C et al. Prediction of phenotype and gene expression for combinations of mutations. Mol Syst Biol 2007; 3:96. 59. Prochasson P, Florens L, Swanson SK, Washburn MP, Workman JL. The HIR corepressor complex binds to nucleosomes generating a distinct protein/DNA complex resistant to remodeling by SWI/SNF. Genes Dev 2005; 19 (21):2534-2539. 60. Forsburg SL, Guarente L. Identification and characterization of HAP4: a third component of the

CCAAT-bound HAP2/HAP3 heteromer. Genes Dev 1989; 3 (8):1166-1178. 61. Olesen JT, Guarente L. The HAP2 subunit of yeast CCAAT transcriptional activator contains adjacent domains for subunit association and DNA recognition: model for the HAP2/3/4 complex. Genes Dev 1990; 4 (10):1714-1729. 62. Ryan MP, Stafford GA, Yu L, Morse RH. Artificially recruited TATA-binding protein fails to remodel chromatin and does not activate three promoters that require chromatin remodeling. Mol Cell Biol 2000; 20 (16):5847-5857. 63. de Boer M, Nielsen PS, Bebelman JP et al. Stp1p, Stp2p and Abf1p are involved in regulation of expression of the amino acid transporter gene BAP3 of Saccharomyces cerevisiae. Nucleic Acids Res 2000; 28 (4):974-981. 64. Teixeira MC, Dias PJ, Simoes T, Sa-Correia I. Yeast adaptation to mancozeb involves the up-regulation of FLR1 under the coordinate control of Yap1, Rpn4, Pdr3, and Yrr1. Biochem Biophys Res Commun 2008; 367 (2):249-255. 65. Ye C, Galbraith SJ, Liao JC, Eskin E. Using network component analysis to dissect regulatory networks mediated by transcription factors in yeast. PLoS Comput Biol 2009; 5 (3):e1000311.

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GT i Ti Ppp px I G T - media.nature.com · First, we examined functions of TFs: OAF3, RDR1, WAR1, and RPI1. OAF3 acted as a ... SWI4 and SWI6 form SBF complex, playing a major role