The comprehensive identification of functional transcription factor binding sites (TFBSs) is an important step in understanding complex transcriptional regulatory networks. This study presents a motif-based comparative approach, STAT-Finder, for identifying functional DNA binding sites of STAT3 transcription factor. STAT-Finder combines STAT-Scanner, which was designed to predict functional STAT TFBSs with improved sensitivity, and a motif-based alignment to minimize false positive prediction rates. Using two reference sets containing promoter sequences of known STAT3 target genes, STAT-Finder identified functional STAT3 TFBSs with enhanced prediction efficiency and sensitivity relative to other conventional TFBS prediction tools. In addition, STAT-Finder identified novel STAT3 target genes among a group of genes that are over-expressed in human cancer cells. The binding of STAT3 to the predicted TFBSs was also experimentally confirmed through chromatin immunoprecipitation. Our proposed method provides a systematic approach to the prediction of functional TFBSs that can be applied to other TFs. The ability of any biological system to properly respond to stimuli heavily depends on biochemical cascades of signaling pathways that culminate in the activation of transcription factors (TFs) and the subsequent alteration of gene expression patterns Whole genome approaches to measure genome-wide expression patterns have divulged groups of genes that are co-regulated to exert spatially and temporally controlled cellular responses Based on the observation that conserved non-coding DNA sequences are often important for the regulation of biological functions, cross-species sequence comparisons have been actively integrated to distinguish functional and non-functional TFBSs Signal transducer and activator of transcription 3 (STAT3) belongs to the STAT family of transcription factors, which is activated by Interleukin-6 (IL-6) and related cytokines, such as IL-10, Oncostatin M (OSM), and leukemia inhibitory factor (LIF) To characterize STAT3-mediated transcriptional programs, we have developed a computational framework designed to predict STAT3 TFBSs with improved sensitivity and low false positive rate. Through the integration of the microarray data obtained from the STAT3 activation condition and the TFBS prediction tools, we attempted to identify novel STAT3 target genes. Using our STAT-Finder program, we identified eight novel STAT3 target genes among a group of genes that are highly expressed in cancer cells. These were then confirmed through chromatin immunoprecipitation.To identify direct STAT3 target genes, we developed a computational framework that predicts functional TFBSs of STAT3 with increased sensitivity and low false positive rate. Our framework, STAT-Finder, was constructed based on two computational components, a TFBS scanning program (STAT-Scanner) and a motif-based alignment program . STAT-ScP-value of the gapped alignment between the two PWMs based on the Kullback-Leibler divergence P-value cut-off, reaching maximum cluster numbers of around 10−16P-value  We first compared the performance of STAT-Scanner with the most practical TFBS prediction tools, MATCH 2.7 We also evaluated the performance of STAT-Scanner using genome-wide STAT3 binding data obtained using embryonic stem cells The ultimate goal of computational prediction is to detect functional TFBSs with a high degree of confidence. To filter out the false positive TFBSs with high affinity scores, we examined various functional constraints such as evolutionary conservation and genome structure of predicted STAT3 TFBS regions. Sequence conservation among multiple species has been proven to constrain functional TFBS We next evaluated the performance of STAT-Finder compared to other comparative methods, namely, EEL We next attempted genome-wide prediction of STAT3 binding in the human promoter regions. For this purpose, we first estimated the cut-off value of the motif conservation score (MCS) to identify conserved functional STAT3 TFBSs. The degree of conservation of the predicted TFBS, which was determined by calculating MCS, was integrated with the affinity scores by STAT-Scanner See . The conConstitutive activation of STAT3 and over-expression of its target gene have been suggested to play critical roles in human carcinogenesis JUNB promoter region including one site that has previously been reported to be a STAT3 binding site JUNB promoter by chromatin immunoprecipitation (NNMT) promoter region, a recently identified STAT3 target gene MYC promoter region , HIC2 (hyper-methylated in cancer 2), and THBS1 (Thrombospondin 1). STAT3 binding to these predicted sites was experimentally confirmed by ChIP assay (ATF3 (activating transcription factor 3), DUSP5 , SERPINE1 , NP (nucleoside phosphorylase), and SLC2A3  were also experimentally validated , which measures the probability that at least one site within a sequence of length n . This conversion makes it easy to define a threshold, γ, of the affinity score within a statistical hypothesis testing framework. In addition, it is also plausible to directly compare the affinity scores of different STAT-related PWMs.We searched putative TFBSs of the STAT family in input promoter sequences and evaluated their binding affinity scores using STAT-Scanner. Given a set of position count matrices obtained from TRANSFAC 9.4 Given a set of STAT-related PWMs and an input promoter sequence, STAT-Scanner first computes the nucleotide composition of the input sequence in order to select the nearest background model, and searches TFBSs whose affinity scores are larger than the threshold, with the PWMs constructed by the chosen background model. We used eight STAT-related PWMs, V$STAT_01, V$STAT1_01, V$STAT3_01, V$STAT5A_01, V$STAT5B_01, V$STAT5A_02, V$STAT1_02, and V$STAT_Q6, and combined all overlapping sites of the eight PWMs into one with the maximum affinity score. Notably, the max operator is applicable because the affinity scores of different PWMs are directly comparable.STAT-Finder was designed to minimize the false positive discovery of predicted TFBSs using comparative sequence comparisons. It searches conserved sites within the promoters of six orthologous species by sequences of affinity scores. To diminish probabilities of misalignments, we used score sequences defined by STAT-Scanner as a first approximation of the conserved regulatory regions. We regarded a region with nonzero affinity scores within the score sequence as the regulatory region. We focus on the regulatory regions of multiple alignments by ignoring the non-conserved regions. We progressively aligned the six score sequences obtained from orthologous promoter sequences, according to the phylogenetic tree of all six mammalian species, and evaluated the degree of conservation by calculating the Motif Conservation Score (MCS). The MCS value of an aligned TFBS ranges from 0 (non-conserved) to 1 (most conserved).Motif-based alignment tool consists of two main parts to align multiple sequences with different affinity scores of STAT TFBSs. The first part is a pair-wise global alignment module that finds an optimal alignment between two sequences. We adapted a variant version of the Needleman-Wunsch algorithm kth PWM of length STAT-Finder has a unique feature that detects not only conserved binding sites but also non-conserved ones with very strong binding signals to rectify the unavoidable alignment error. Before describing our probabilistic model, we first explain our notations to define our model. Among the 8 STAT3-related PWMs, we denote by jth column vector kth PWM starts at position Suppose we have a promoter sequence kth PWM are governed by the probability Our probabilistic model has the following specification for defining the joint distribution. First, latent variables For each latent position The objective of probabilistic inference in our model is to calculate the posterior probability The degree of conservation of each subsequence can be easily incorporated into our probabilistic framework by assigning relatively higher prior probability We denote two promoter sequences of lengths We construct the optimal alignment by tracing back the choices that result in the final value of We denote two profiles of lengths TTCCNGGAA). We excluded V$STAT5A_02 (homotetramer) because it was not aligned with other PWMs. The operation for random permutation was then applied to the aligned PWMs to generate 100 control motifs. Based on the assumption that the control motifs should have occurrence rates similar to the real motif, we selected 42 control motifs that detect similar numbers of TFBSs in the reference data set (±15%). Among them, we chose 10 motifs that were most dissimilar to V$STAT_01, based on the inter-motif distance measure (cut-off: 0.25) To generate randomly shuffled control motifs, we manually aligned STAT-related PWMs without gaps by looking up the core regions (Human and mouse promoter sequences (−2000∼ +500 bp of the annotated transcription start sites) were downloaded from Table Browser of the UCSC genome browser Genome-wide STAT3 ChIP-Seq data set was obtained from http://ai.stanford.edu/~erans/cancer/). We used Cancer Module 3, 17, 18, 54, 57, 98, 124, 126, and 197, which contain commonly up-regulated genes across liver cancer, B lymphoma, grade3 breast cancer, and stimulated macrophages. Microarray CEL data files of STAT3-C over-expressed cells were obtained from Dr. E.B. Haura  P-value <0.01 (t-test).Cancer module map information was downloaded from the web browser  and 1% penicillin/streptomycin . Human lung carcinoma cell line, A549, and breast cancer cell line, MDA-MB-231, were cultured in DMEM with 10% FBS and 1% penicillin/streptomycin. For IL-6 stimulation, cells were treated with rhIL-6 (10 ng/ml) and rhIL-6sR (10 ng/ml)  for 15 minutes.ChIP assays were performed as described with minor changes Figure S1PWM similarity clustering. (A) Total 565 vertebrate TRANSFAC PWMs were clustered by pair-wise similarity comparison with the Kullback-Leibler divergence. The number of PWM clusters at different similarity P-value cut-offs is plotted. (B) PWM cluster at 10–7 P-value of similarity was represented by Cytoscape (1.55 MB TIF)Click here for additional data file.Figure S2Quality scores of STAT-related PWMs and clustered STAT-related PWMs in the known STAT3 TFBSs. (A) Histogram of PWM quality score for all 565 vertebrate PWMs derived from TRANSFAC ver. 9.4. (B) Number of STAT3 binding sites detected by combined PWMs. Forty STAT3 TFBSs (0.96 MB TIF)Click here for additional data file.Figure S3STAT3 TFBS prediction using MATCH and MotifLocator. Curves for the changes of the number of true positive TFBSs detected using MotifLocator (A) or MATCH (B) in the reference set of 22 STAT3 target genes. PWM: V$STAT3_01, V$STAT1_01, or combined PWMs of V$STAT3_01 and V$STAT1_01 (All).(0.67 MB TIF)Click here for additional data file.Figure S4Comparison of the TFBS prediction programs using the genome-wide STAT3 binding. Curves for the changes of the number of true positive TFBSs detected using MATCH (A) or MotifLocator (B) in the genome-wide STAT3 ChIP-Seq dataset.(0.75 MB TIF)Click here for additional data file.Figure S5Estimation of MCS confidence scores. The graph displays confidence scores (dotted line) and predicted numbers of conserved TFBSs (solid line) at each MCS cut-off value.(0.48 MB TIF)Click here for additional data file.Figure S6Genome-wide distribution of predicted STAT3 TFBSs. Using 2.5-kb promoter sequences of all annotated human reference genes, predicted STAT3 TFBSs with STAT-Scanner  or STAT-Finder  were plotted. The red line (random) shows the distribution of predicted TFBSs in the randomly permutated promoter sequences.(0.80 MB TIF)Click here for additional data file.Figure S7Experimental validation of STAT3 binding to the novel STAT3 TFBS. The affinity score  and posterior probability  of the predicted STAT3 TFBS are plotted in the sliding windows for a 2.5-kb promoter region across the ATF3 (A), DUSP5 (C), SERPINE1 (E), NP (G), SLC2A3 (I), and CCL2 (K) genomic loci. The closed square at bottom indicates predicted STAT3 TFBS with posterior probability >0.5.  ChIP analysis with an anti-STAT3 antibody.(7.45 MB TIF)Click here for additional data file.Figure S8Performance comparison of the comparative alignment tools for the STAT3 target genes identified in this study.(0.36 MB TIF)Click here for additional data file.Table S1Lists of the reference set for known STAT3 TFBSs.(0.17 MB DOC)Click here for additional data file.Table S2The information for primer sets used in ChIP experiment.(0.04 MB DOC)Click here for additional data file.


Characterization of the target genes shows that SEPALLATA3 integrates and modulates different growth-related and hormonal pathways in a combinatorial fashion with other MADS-box proteins and possibly with non-MADS transcription factors. In particular, the results suggest multiple links between SEPALLATA3 and auxin signaling pathways. Our gene expression analyses link the genomic binding site data with the phenotype of plants expressing a dominant repressor version of SEPALLATA3, suggesting that it modulates auxin response to facilitate floral organ outgrowth and morphogenesis. Furthermore, the binding of the SEPALLATA3 protein to cis-regulatory elements of other MADS-box genes and expression analyses reveal that this protein is a key component in the regulatory transcriptional network underlying the formation of floral organs.The molecular mechanisms by which floral homeotic genes act as major developmental switches to specify the identity of floral organs are still largely unknown. Floral homeotic genes encode transcription factors of the MADS-box family, which are supposed to assemble in a combinatorial fashion into organ-specific multimeric protein complexes. Major mediators of protein interactions are MADS-domain proteins of the SEPALLATA subfamily, which play a crucial role in the development of all types of floral organs. In order to characterize the roles of the SEPALLATA3 transcription factor complexes at the molecular level, we analyzed genome-wide the direct targets of SEPALLATA3. We used chromatin immunoprecipitation followed by ultrahigh-throughput sequencing or hybridization to whole-genome tiling arrays to obtain genome-wide DNA-binding patterns of SEPALLATA3. The results demonstrate that SEPALLATA3 binds to thousands of sites in the genome. Most potential target sites that were strongly bound in wild-type inflorescences are also bound in the floral homeotic  Most regulatory genes encode transcription factors, which modulate gene expression by binding to regulatory sequences of their target genes. In plants in particular, which genes are directly controlled by these transcription factors, and the molecular mechanisms of target gene recognition in vivo, are still largely unexplored. One of the best-understood developmental processes in plants is flower development. In different combinations, transcription factors of the MADS-box family control the identities of the different types of floral organs: sepals, petals, stamens, and carpels. Here, we present the first genome-wide analysis of binding sites of a MADS-box transcription factor in plants. We show that the MADS-domain protein SEPALLATA3 (SEP3) binds to the regulatory regions of thousands of potential target genes, many of which are also transcription factors. We provide insight into mechanisms of DNA recognition by SEP3, and suggest roles for other transcription factor families in SEP3 target gene regulation. In addition to effects on genes involved in floral organ identity, our data suggest that SEP3 binds to, and modulates, the transcription of target genes involved in hormonal signaling pathways. The key floral regulator SEPALLATA3 binds to the promoters of a large number of potential direct target genes to integrate different growth-related and hormonal pathways in flower development. In contrast to animals, most developmental processes of plants occur postembryonically and integrate a variety of internal and environmental cues. Modularity of plant development is based on the ability of plants to maintain pools of undifferentiated stem cells throughout the life cycle of the plant. Stem cells near the tip of the growing shoot are located in the shoot apical meristem, where different types of plant organs, such as vegetative leaves or floral organs, can be initiated from the flanks of the meristem. Leaves and floral organs are “variations of a theme”: they arise via modifications of a common basic genetic program . Which tThus, change in the identity of plant organs is initiated by reprogramming within the meristems . Plant dArabidopsis thaliana is roughly 20-fold smaller than the human genome; still, it encodes about 27,000 protein-coding genes, which is more than found for humans . One ofSEP1–3 function have flowers with floral organs converted into sepals and display a loss of determinacy in the center of the flower 7G in the ChIP-SEQ data. Using a binomial test, we found that three out of eight possible half-sites for the consensus CC[A/T]6GG were significantly overrepresented in the SEP3 ChIP-SEQ data, and four out of 24 for the consensus CC[A/T]7G  and 72 (AGRIS) DNA-binding consensus sequences of plant transcription factors. In addition to confirming the enrichment of MADS-, TCP, and bHLH/bZIP binding sites, we found that also ARF, C2H2 (ID1) DNA recognition motifs, and a bHLH (MYC) DNA-binding site similar to the G-box were enriched with increasing peak score threshold, and located in the center of the ChIP-SEQ peaks  and S4.ag mutant. We were interested in the overlap of DNA-binding sites in these two samples, which could point to target genes involved in the formation of perianth organs. There is clear preference for overlapping genomic positions of SEP3 ChIP-SEQ peak maximum positions in wild type and ag mutant .The overlap of potential SEP3 targets in wild type and  by SEP3 B. WhereaDR level B. Thus, ely more B, as do According to these results, only a small fraction of strongly enriched direct target genes is specific to the SEP3 complexes specifying stamen and carpel development . These gIn the ChIP-SEQ approach to identify potential SEP3 targets, different floral tissues corresponding to different developmental stages were used. In order to evaluate the relevance of DNA-binding events in the regulation of the genes corresponding to the ChIP-SEQ peaks, we used comprehensive gene expression array data that are publicly available. Mainly, the collection of AtGenExpress experiments provides information about timing of gene expression and changes in different floral homeotic mutants .lfy-12, ap1-15, ap2-6, ap3-6, ag-12)  in the total dataset). Considering SEP3 binding sites in genes that are differentially expressed during development, we found the strongest enrichment for genes that change expression in the meristem during the earliest stages of floral development . About 63% of the potential targets of SEP3 are differentially expressed at any stage of reproductive development starting from floral transition to flowers of stage 12  were differentially expressed at very young developmental stages in at least one of the homeotic mutants  . This frstage 12 .In total, 72% of the potential SEP3 targets are differentially expressed during flower development or in any of the homeotic mutants. Although the differential expression can also be due to indirect effects, the data suggest that the majority of potential direct SEP3 targets may also be regulated by SEP3. We also found an enrichment in frequency of genes that are correlated in expression with SEP3 expression with increasing peak score threshold in ChIP-SEQ . The fraSEP genes are required for the up-regulation of floral homeotic genes, and that this up-regulation is crucial for the establishment of the identities of the different floral organs. However, until now, it has not been demonstrated whether this regulation is direct.Genetic and gene expression experiments suggest that the SEEDSTICK (STK) and CAULIFLOWER (CAL) do not have significantly enriched regions. In most cases, the peaks are located in the promoters of the respective genes. In case of the APETALA1 (AP1), APETALA3 (AP3), SEP1, and SEP2 loci, there are also peaks in the 5′ UTR, whereas a SEP3 binding site is present in the second intron of AGAMOUS (AG).We analyzed the binding profiles for the genomic loci corresponding to the floral homeotic genes and found that SEP3 binds to nearly all of these loci . Only SEAG and AP3 are best characterized. The spatial expression pattern of AP3 is driven by regulatory elements within approximately 500 bp upstream of the transcriptional start. CArG boxes in this part of the promoter are important for the positive as well as negative regulation of AP3 7G/C[A/T]7GG can serve as functional binding sites for SEP3 and associated MADS-domain proteins in planta. Instead, we found significant enrichment for particular types of half-sites in the SEP3 ChIP-SEQ data. In addition, there is clear interdependence between the sequence of individual half-sites, and between half-sites and surrounding bases. Thus, commonly used consensus sequences and position weight matrices, which assume independency of nucleotide positions of a binding site, are highly oversimplified. The enrichment for certain half-sites may reflect the recognition site for SEP3, while the other half of the binding site could match the recognition site of the dimer partner of SEP3. It is known that SEP3 is able to dimerize with many other MADS-domain proteins in yeast assays [in planta , which makes it difficult to determine the consensus CArG box sequence for SEP3. Combining ChIP-SEQ data obtained from different MADS dimer partners  and determining the overlap in binding sites will elucidate the recognition sites for particular MADS-box dimers. We found that there is a clear overlap of target genes in wild type compared to the ag mutant, although there are differences in number of binding sites and binding affinity at individual sites. Although at this point we cannot exclude that the wild-type binding sites are enriched for perianth-specific targets, these results support the hypothesis that different MADS-domain protein complexes  may bind to overlapping sets of target genes, but regulate them in a different way, for instance by recruiting different sets of cofactors leading to differences in activation or repression of genes. In line with the idea that cofactors play a role in differential regulation of downstream targets, it has been shown that SEP3 and AP1 can recruit the corepressor SEUSS, and that this complex acts to repress AG expression in petal development [Although in vitro studies have revealed consensus binding sites for many transcription factors, including MADS-domain proteins ,32, it rt assays  and in pelopment . Our finelopment , differeThe interplay between different proteins implicates that the dynamics in the floral developmental network is strongly dependent on relative quantities and affinities of individual homeotic proteins competing for common protein interaction partners and key downstream targets. The large number of targets suggests that floral homeotic protein complexes globally control and modify the genetic programs that are active in all plant organs, so that only a limited number of targets would be expected to be unique for the individual floral homeotic protein complexes.In addition to CArG box elements, we found several other transcription factors consensus DNA-binding sequences to be enriched in the center of the peaks, suggesting that these transcription factors act in a combinatorial fashion with MADS-domain proteins in common regulatory modules. Until now, there has been very limited information about interactions between plant MADS-domain proteins and other transcription factors, and this rare information is based on artificial yeast and/or in vitro protein interaction data. The types of transcription factors whose binding sites are enriched in the peaks link MADS-domain proteins with other cellular, developmental, and hormonal pathways. TCP transcription factors have been shown to be important for cell growth –56, and cis-element is relevant for transcription factor binding. Chromatin remodeling, which is active throughout plant development, is likely affecting the accessibility of transcription factors and transcriptional activity. It occurs during postembryonic developmental transitions  and is required for maintenance of meristematic cell identity as well as for the formation of organ primordia, with different chromatin factors acting at different stages , making it even possible that there is a direct interaction between these transcription factors and chromatin remodeling factors. Interesting in this respect is that we identified four out of five members of ATP-dependent chromatin remodeling complexes as targets of SEP3 in the ChIP experiments, suggesting an interdependence relationship.In addition to primary sequence characteristics, the accessibility of binding sites, and thus the chromatin structure, can influence the recruitment of MADS-box transcription factors to specific sites in the genome. We observed a strong enrichment of binding sites close to the transcriptional start site of genes, suggesting that the recognition sites are not randomly distributed in the genome. Assuming that any small sequence motif is randomly distributed in the genome, it suggests that not only the primary sequence itself, but also the position of the  stages  mutants or plants treated with the auxin transport inhibitor NPA [In addition to specifying floral meristem and organ identity, an important role of SEP and AP1-like proteins is to trigger organ growth. The impaired . Similarox genes . This hyitor NPA . These pDevelopmental transcription networks are composed of positive and negative regulatory loops, which often unite into larger transcription units. Positive feedback loops that are made up of two transcription factors regulating each other result in a robust expression in response to a transient developmental signal in order to establish and maintain a developmental program . HoweverSEP genes and their protein products are part of these regulatory networks [The expression of MADS-box genes is often regulated by transcription complexes composed of their own gene products. One of the earliest examples for this phenomenon was the finding that floral homeotic genes responsible for the formation of petals and stamens (the B class genes in the ABC model) are up-regulated by dimers consisting of the encoded proteins . More exnetworks . The abinetworks  and the networks ,64 illusFrom SOC1 and AGL24). In line with this idea, binding sites of SEP3 and AP1 overlap at the SOC1 promoter , sep3-1 (Col-0), and ag-1 (Ler) mutant plants were grown under standard greenhouse conditions . Flower material was harvested from primary and secondary inflorescences of 5–7-wk-old plants.sep3 mutant plants. Total extracts were produced using a standard protocol [ChIP experiments were performed essentially as described in . For theprotocol ; nuclei ag-1 mutant samples. For the sep3-1 mutant sample (negative control), we pooled the DNA of three different ChIP experiments to obtain sufficient material for amplification. The amplified material was subjected to Solexa sequencing following Illumina's instructions. The sequence datasets were submitted to Gene Expression Omnibus (GEO) (accession number GSE14600).Linker annealing, amplification, and gel purification for the Solexa sequencing were essentially performed as instructed by the Illumina protocol with small modifications. The gel purification was done after the amplification step. We used individual, complete ChIP samples for each amplification reaction for the wild-type and Arabidopsis Tiling 1.0R Arrays (Affymetrix) as biological duplicates. Tiling array data were submitted to GEO (accession number GSE14635).For the amplification of the ChIP-DNA for ChIP-CHIP, we used a protocol published by  with modp-value) was obtained, we define as ChIP-CHIP peaks the genomic regions with a p-value lower than 0.05, and not separated by more than 100 bp with the highest p-value not higher than 0.05.Enrichments in ChIP sample hybridizations relative to input were calculated from raw intensity (CEL) files using a nonparametric statistical method implemented in the Affymetrix Tiling Analysis Software (TAS) . BiologiArabidopsis reference genome  using the SOAP software [The 35- or 36-nucleotide (nt) reads were mapped to the unmasked software , allowinag-1 mutant, against the enrichment found in the sep3-1 mutant treated as a negative control. For this, data concerning positions of the mapped reads were transformed into numbers characterizing all nucleotide positions in the genome in the following way. Define isx, where i is the nucleotide position and s = 1,2 for the examined and control sample respectively, as the minimum of the counts of extended mapped reads that overlap at the position i on the forward and on the reverse strand in the sample s. This value is a conservative estimate of the representation of the nucleotide i in the sequenced samples, supported by both the strands independently. By the transformationisy independent of the number of sequenced reads were obtained. The examined sample values iy1 were then normalized with respect to the mean and variance of the distribution of control values iy2. For the comparison of the observed examined and control counts at position i, the one-sided test based on the Poisson distribution was made according to the probability (test statistic) formulaiz2 is the maximum of iy2 and the global coverage obtained for the control whose value is the product of the number and the length of the extended mapped reads divided by the mappable genome length. All genomic regions consisting of nucleotides characterized by calculated probability values smaller than 0.05 and not interrupted by a gap of 100 nt or more were identified and assumed to contain a candidate enrichment peak. For presentation purposes, the calculated probabilities were transformed into scores of −loge(it), and the maximum score value for each candidate peak was used to test the significance.The data analysis was aimed at a comparison of the enrichment found in the wild-type plant and, independently, in the Permutation tests were used to estimate the FDR for the peaks. For this, each mapped read was considered as having the label “sample” when it belonged to the examined sample and “control” if it belonged to the control sample. To obtain the distribution of the test statistic under the null hypothesis of no differences between the examined and the control samples, the labels of the reads were randomly permuted, and for each permutation, the methodology explained above to test differences in distribution was applied. The permutations were run until at least 65,000 test statistic values for calculation of the null distribution were obtained.ftp://ftp.arabidopsis.org/). The gene affected by binding at each peak was selected by the following algorithm. First, for all peaks, the affected gene was selected as the one with the peak inside it and the minimum distance to the start. For the peaks outside of the genes, the affected gene was then selected as the one with the peak in its 3,000-bp upstream or 1,000-bp downstream region and the minimum distance to the start or end, respectively. Thus, it was assumed that a DNA-binding event affects the closest neighboring gene, which results in a conservative estimate of the number of genes controlled by each of the transcription factors.All peaks were characterized by their location with respect to the annotated genes , or “not annotated.” This characterization was done on the basis of the first available splicing variant for a gene; in practice, this meant using variant 1 for most of the genes and variant 2 for some.Arabidopsis reference genome . Repeat and low-complexity regions were eliminated using RepeatMasker . Sequence affinity logo representations were prepared using AffinityLogo [Default parameters for MatrixREDUCE  and MEMEnityLogo . The MatArabidopsis reference genome. The motif consensus sequences were obtained from AGRIS and Transfac databases [Perfect match motif consensus sequences were located in the 1,000-bp region around the maximum score position (defined as “peak area”) for significant peaks (FDR <0.001) using a perl script and the atabases ,74,75. EFor each significant peak, the nucleotide sequence 500 bp around the position of the maximum peak score location were extracted and associated with the peak score value. To obtain the proportion of peaks with a given DNA binding site consensus at a given peak score threshold level, among the nucleotide sequences associated with a peak score value bigger than the threshold level, the proportion of sequences with at least one DNA binding site consensus was calculated for the sample and control set. Two control sets were generated: (1) one control set was generated randomly permutating the nucleotide sequences and the peak score value, and therefore destroying any relationship between them, and (2) another control set was generated permutating the nucleotides within their sequence for each nucleotide sequence independently. Each set was characterized by the number and location (regarding the center of the sequence) of the DNA consensus binding sites studied. We considered binding sites to be overrepresented in the ChIP-SEQ data only when they were enriched relative to both controls. For simplicity, only control 2 is shown in The proportion of the distance of the DNA binding site consensus to the peak score location was calculated as the distance from the center position of the DNA consensus to the peak score location for each peak with a score bigger than the corresponding threshold at a given FDR level. Nonoverlapping distance ranges of 50 bp were considered to calculate the proportion of distance values within each range among the total number of distance values considered. All graphs were generated with R software.Arabidopsis promoters, assuming a binomial distribution. The algorithm Composite Module Analyst  was useArabidopsis genome, and (2) random permutations of the sequences described above. The control or background set using real promoters (1) makes a more stringent test, because real binding sites, including CArG boxes, can still be found in the sequence. This test shows the specific selection of a given distance between CArG box site pairs in the SEP3-dependent gene's promoters, when compared against other real promoter sequences. The second background set, made out of randomly permuted sequences (2), presents sites generated by the random variation of the genome composition, without  selection, so that this comparison shows the selection of distances between these sites, when compared with an absolutely random site and distance distribution. The MATCH algorithm was applied to these three sequence sets, using the CArG box profile described above, and the positions of the matching sites collected. The distance between adjacent sites was calculated and the distributions were compared using a binomial test, to identify overrepresented distances.In order to establish whether CArG box pairs presented conserved distances between them, a comparison between the distance distribution between sites found in the real data and those found in two control datasets was performed. The control sets generated consisted of (1) real promoter sequences from genes chosen at random from the Analysis for GO term enrichment was carried out using the AMIGO server  for the 2 gcRMA normalized expression values for experiments of interest from the AtGenExpress developmental atlas [Arabidopsis genome were ignored in the analysis. After back-transforming the log2 expression values to original scale, a two-sided Student test statistic was applied to test differential expression between sample and control. These genes with a p-value lower than 0.01 were considered as differentially expressed.We downloaded logal atlas . Controlsep3-1 mutant plants and Col-O wild-type plants. SEP3-EAR expression in wild type and sep3 mutant showed similar phenotypes; however. we focused our phenotypic analysis on pSEP3:SEP3-EAR in the sep3 mutant.To generate the dominant repressor, we used the SRDX domain , which iTUB and EF). All primers are available as supplementary information  among the genes affected by at least one ChIP-SEQ peak was calculated. In a similar way, the proportion of genes that correlate with SEP3 expression among all the genes was calculated.The expression values for development stages  were obtained as explained in “Gene expression microarray analysis” above. After back-transforming the logFigure S1(A) Western blot of in vitro–translated MADS-domain proteins probed with the SEP3 antibody used for the ChIP experiments (upper panel). The same proteins were detected using Streptavidin-Alkaline Phosphatase to test for expression of the biotin-labeled proteins .(B) Western blot on plant extracts. Left: comparison of SEP3 antibody signal in total and nuclei extracts. Right: detection of SEP3 in wild-type and sep3 mutant lines. Riken line PST1193 has an insertion in the upstream region of SEP3 near the transcriptional start. Riken line PST20678 has an insertion in an exon.(1.57 MB PDF)Click here for additional data file.Figure S2(A) Histogram of distances between the maximum score positions of significant peaks in ChIP-SEQ and ChIP-CHIP: 90.7% of the peaks in ChIP-CHIP have a ChIP-SEQ peak nearby .(B) Distance between the top 4,000 ChIP-SEQ peaks and the top 4,000 ChIP-CHIP peaks as a function of the sum of their ranks. Peaks with low rank sums (high significance in ChIP-SEQ and ChIP-CHIP) are mostly a short distance from each other. Peaks with the higher rank sums are also mostly overlapping or only a short distance apart; however, more variation is observed.(C) Box-plot of the widths of ChIP-SEQ peaks for different ranks . The lower the rank, the more significant is a peak.(D) Box-plot of the widths of ChIP-CHIP peaks for different ranks. In (C) and (D), only the lowest 10,000 ranks are shown.(E) Scatter plot of the average number of overlapping extended reads at each nucleotide position based on the raw sequence data for the two sequencing replicates of the SEP3 wild-type ChIP sample. The average was obtained over nonoverlapping windows of length 5,000 bp .(F) Scatter plot of rank positions of ChIP-SEQ and ChIP-CHIP peaks. For each ChIP-SEQ peak, the closest ChIP-CHIP peak was identified (at a distance not greater than 500 bp). The scatter plot shows the relationship between the peak rank positions in ChIP-CHIP and ChIP-SEQ. Only the 14,000 most significant peaks are shown. The peaks that are in a similar position in CHIP-SEQ and in ChIP-CHIP (not farther apart than 500 bp) have a similar rank position in both experiments. This relationship is stronger for the more significant peaks. In order to minimize the effects of technical differences related to the different experimental platforms, we did not consider the value of the test statistics for each peak, but rather its rank (the most significant peak has a rank of one).(G) Scatter plot of the average number of overlapping extended reads at each nucleotide position based on the raw sequence data for the two biological replicates of the SEP3 wild-type ChIP sample. The average was obtained over nonoverlapping windows of length 5,000 bp .(H) Distance between the top 4,000 ChIP-SEQ peaks of two biological replicates as a function of the sum of their ranks. Peaks with low rank sums (high significance in ChIP-SEQ and ChIP-CHIP) are mostly a short distance from each other. Peaks with higher rank sums are also mostly overlapping or a short distance apart.(914 KB PDF)Click here for additional data file.Figure S3(A) Enrichment of peaks within promoters  and downstream regions  with increasing ChIP-SEQ score. Peaks within genes (red line) and peaks without any neighboring gene (blue line) are also shown in the graph.(B) Preferred position of peaks within introns (blue line), 5′ UTRs (black line) and 3′ UTRs (green line). Peaks in coding sequences (red line) are also indicated in the graph.(C) Position of the ChIP-SEQ peak maxima relative to the transcriptional start of genes (zero position). Enrichment of peaks (as indicated by proportion) depends on the FDR used.(D) As in (C) but now for the end of genes (zero position is end of the gene).(539 KB PDF)Click here for additional data file.Figure S4The frequency of ID1 binding sites in the genome and in the ChIP-SEQ data is very low, the enrichment however is supported by two types of controls. Black line: ChIP-SEQ data (wild-type); red line: Permutation of score and sequence; blue line: Randomization of sequences.(537 KB PDF)Click here for additional data file.Figure S5lfy-12, ap1-15, ap2-6, ap3-6, and ag-12) or different developmental stages .(A) Gene expression analysis of different floral homeotic mutants  Enrichment of genes correlated in expression with SEP3. The Spearman rank correlation (582 KB PDF)Click here for additional data file.Figure S6PIN1, which is itself not a target of SEP3, is also visualized.Expression in vegetative above-ground organs and in flowers and reproductive meristems is shown. The expression of (607 KB PDF)Click here for additional data file.Table S1(A) Results of Solexa sequencing and mapping of the reads to the genome.(B) Reads mapped to chromosomes (in percent) in relation to chromosome length.(46 KB DOC)Click here for additional data file.Table S2ag mutant compared to wild type.The file also contains a table with data on differential expression of potential target genes in the (921 KB XLS)Click here for additional data file.Table S3ag mutant (B), and within genes in wild type (C) and ag mutant (D).Genomic positions of ChIP-SEQ peaks relative to closest neighboring genomic loci in wild type (A) and (80 KB DOC)Click here for additional data file.Table S4agamous ChIP-SEQ peaks compared to their genome-wide frequencies using a binomial test.Overrepresentation was calculated given the observed number of these sequences in wild-type and (41 KB XLS)Click here for additional data file.Table S5p-values of the chi-square test were used to measure dependence. The table shows the dependence in −loge scale. Only values bigger than −loge(0.05) are shown.The (17 KB XLS)Click here for additional data file.Table S6(61 KB XLS)Click here for additional data file.


The Myc oncoprotein, a transcriptional regulator involved in the etiology of many different tumor types, has been demonstrated to play an important role in the functions of embryonic stem (ES) cells. Nonetheless, it is still unclear as to whether Myc has unique target and functions in ES cells.myc null E7 embryos demonstrates a 70–80% decrease in H3-K4me3, with little change in H3-K27me3, compared to wild-type embryos indicating that Myc is required to maintain normal levels of histone methylation.To elucidate the role of c-Myc in murine ES cells, we mapped its genomic binding sites by chromatin-immunoprecipitation combined with DNA microarrays (ChIP-chip). In addition to previously identified targets we identified genes involved in pluripotency, early development, and chromatin modification/structure that are bound and regulated by c-Myc in murine ES cells. Myc also binds and regulates loci previously identified as Polycomb (PcG) targets, including genes that contain bivalent chromatin domains. To determine whether c-Myc influences the epigenetic state of Myc-bound genes, we assessed the patterns of trimethylation of histone H3-K4 and H3-K27 in mES cells containing normal, increased, and reduced levels of c-Myc. Our analysis reveals widespread and surprisingly diverse changes in repressive and activating histone methylation marks both proximal and distal to Myc binding sites. Furthermore, analysis of bulk chromatin from phenotypically normal c-We show that Myc induces widespread and diverse changes in histone methylation in ES cells. We postulate that these changes are indirect effects of Myc mediated by its regulation of target genes involved in chromatin remodeling. We further show that a subset of PcG-bound genes with bivalent histone methylation patterns are bound and regulated in response to altered c-Myc levels. Our data indicate that in mES cells c-Myc binds, regulates, and influences the histone modification patterns of genes involved in chromatin remodeling, pluripotency, and differentiation. ES cells must be capable of self-renewal while simultaneously retaining the capacity to commit to a wide range of differentiation lineages. The notion that the determination and maintenance of embryonic stem (ES) cell pluripotency and self-renewal is related to an epigenetic state characterized by an open chromatin conformation has received considerable support over the last several years Much recent work on ES cell pluripotency has focused on two aspects of transcriptional regulation: the actions of the Sox2-Oct4-Nanog transcription factor network and the nature of epigenetic changes associated with pluripotency It is likely that one reflection of the open chromatin conformation proposed for ES cells is the relative paucity of epigenetic marks associated with gene repression. This includes, in comparison to non-pluripotent cells, decreased DNA methylation and histone H3 lysine 27 trimethylation (H3-K27me3) as well as augmentation of positive marks such as histone H4 acetylation and H3-K4me3 myc oncogene family encodes the c-, N- and L-Myc, bHLHZ proteins that heterodimerize with Max and bind DNA at E-box sequences. Myc family proteins have been associated with cell growth and proliferation through widespread binding to DNA (on the order of 10–20% of genomic loci are bound) and modulation of the expression of hundreds to thousands of genes myc gene expression is itself regulated by many different mitogens, cytokines, and growth factors, suggesting that Myc serves as an integrator of signal transduction. As such, Myc proteins are likely to link extracellular signals to an acute cellular growth response myc loss of function in hematopoietic stem cells leads to their retention in the stem cell niche and inhibition of differentiation myc results in their ablation myc family genes have been shown to cooperate with Sox2, Oct3/4 and Klf4 in the conversion of murine embryo fibroblasts (MEFs) into induced pluripotent stem cells (iPS) myc can be replaced by other factors but at the cost of greatly diminished efficiency myc loss of function has been associated with a reversible global change in histone acetylation and methylation Another transcriptional regulatory protein that has been connected with ES cell function is Myc. The We mapped genomic binding sites for endogenous c-Myc in murine embryonic stem (ES) cells by employing DNA derived from anti-Myc chromatin-immunoprecipitation to probe DNA microarrays (ChIP-chip) comprised of 21,632 mouse gene promoters. For these experiments we employed sorted SSEA-1 positive R1 and AK7 ES cells and a pre-absorbed antibody with reactivity restricted to c-Myc protein see . We impomyc, or to introduce shRNAs designed to knockdown c-myc expression. We observed an 8-fold average increase in c-myc RNA levels following infection with c-myc expressing Lentiviruses and a 4-fold decrease in c-myc RNA after introduction of shRNA directed against c-myc  in ES cells  see  as well ES cells , an inteES cells . Moreoveby shRNA . As expeby shRNA . BecauseDrosophila, dmyc and polycomb (PcG) interact genetically In myc overexpression and repressed following c-myc knockdown  decrease in H3K4me3 levels, but no significant change in H3K27me3 compared to wildtype. Our results suggests that c-Myc expression is involved in maintaining H3K4me3 levels in ES cells and in early embryonic development (To determine whether loss of c-Myc expression has a significant effect on H3K4me3 level during embryonic development we examined embryos from wildtype and c-elopment .myc (mES ↑c-myc) and mES cells in which c-myc has been knocked down (mES ↓ c-myc). 2 enrichment profiles to permit us to more easily compare wildtype ES cells with the mES ↓ c-myc and mES ↑c-myc cells. Here negative enrichment ratios indicate that the signal from the anti-Myc ChIP was less than the input DNA against which it was normalized. Ebf1, Ebf2, Olig3, Gata6, and Sox 2 display significant endogenous c-Myc binding at several sites within their promoters in mES cells  displayed changes in the intensity and position of H3-K27me3 and/or H3-K4me3 peaks in response to altered Myc levels , and Smarca2  or genes highly expressed in differentiated cells such as very low density lipoprotein receptor (VLDLR), glutamate receptor (Grm7), dopamine receptor D4 (Drd4), and the protocadherin cluster see . Within myc cells display a nearly complete loss of the H3-K27me3 and a diminished intensity of H3-K4me3 in the Jak2 promoter region. Onecut1 and Hes1 also display changes in both H3-K27me3 and/or H3-K4me3 in response to changes in Myc levels. These effects are likely due to secondary or long-range effects of Myc on chromatin  to reprogram mouse and human fibroblasts While this work was in progress three reports were published describing Myc binding sites in ES cells sox2, encoding a critical transcription factor regulating pluripotency, is regulated and bound by Myc. Moreover histone H3 methylation at the sox2 promoter is altered in response to changes in c-myc abundance. This result is consistent with a previous study demonstrating elevated Sox2, among other pluripotency factors, in an ES cell-like expression module discovered in diverse human epithelial cancers It is noteworthy that myc null mice  have strikingly reduced H3-K4me3, while H3-K27me3 was essentially unchanged , potential chromatin remodelers , ubiquitin ligases , the H3-specific kinase Pim1, the HDAC1 histone deacetylase, and the JARID1A/LID histone H3-K4 demethylase , and β-mercaptoethanol. ES cells were thawed on plates with a mitomycin C-treated mouse embryonic fibroblast feeder layer and then grown and expanded on 0.1% gelatin-coated plates (without feeder cells) in medium containing LIF for subsequent experiments.floxed/floxed mice (a gift of I.M. de Alborán) to delete the floxed c-Myc allele and then back-crossing to wild-type to remove more-Cre. The resulting c-Myc heterozygous mice were bred to obtain the c-Myc null embryos for further experiments.The c-Myc constitutive knock-out mice were generated by crossing more-Cre and c-Myc All research involving mice has been conducted according to the Institutional Animal Care and Use Committee Guidelines and has been pre-approved by the Institutional Review Board at the Fred Hutchinson Cancer Research Center.ES cells were purified by SSEA-1 flow sorting and treated with 1.1% formaldehyde for crosslinking which was terminated with 0.125M glycine before lysis and sonication to produce 0.2∼1kb DNA fragments. Protein-A- agarose and protein-G-sepharose beads were blocked with 1 µg/ml sonicated salmon sperm DNA and 0.5 mg/ml BSA and washed twice in lysis/sonication buffer . For ChIP with mouse monoclonal or rabbit polyclonal antibody, chromatin was pre-cleared with appropriate beads before immunoprecipitation overnight with antibody. Antibodies used for chromatin immunoprecipitation were c-Myc , H3K4me3 , and H3K27me3 , EZH2 , and Suz12 (Upstate #07-379). The N262 antibody antibody has been used extensively for ChIP of c-Myc http://www.R-project.org), we merged normalized data with the corresponding gene identifiers. Genes bound by Myc were identified as those in which any probe in the 5 kb promoter segment had a log2-ratio (ChIP vs. input DNA) >2. Data was further analyzed by the ACME package (www.bioconductor.org), a computer program developed for analysis of data obtained from NimbleGen-tiled microarrays, using a window of 500 bases and a threshold  = 0.95 Chromatin immunoprecipitation and amplification (ligation-mediated PCR) for ChIP-chip analysis were carried out according to the Roche/NimbleGen protocol. Input DNA represents an aliquot of chromatin from the same sample used for ChIP and was crosslinked, sonicated, and processed identically to the ChIP sample. Duplicate samples were applied to the arrays. Our initial mouse promoter DNA microarray comprised 21,632 putative promoters from annotated mouse genes . Using R software  on ABI7900HT detection system. The Ab5, c-Myc specific antibody was used for qChIP-PCR validation. The enrichment ratio was calculated by ChIP versus input and the positive targets were determined by criteria of 2-fold enrichment. We evaluated enrichment from duplicates of 2 independent ChIP assays obtained from both the R1 and AK7 ES cell lines. The sequences for all primers for ChIP-PCR are available upon request.Custom-design DNA microarrays for a subset of genes (see text) were tiled through >5 kb upstream and 1 kb downstream of transcription start sites using isothermal probes of 70–80 nucleotides. Triplicate samples were applied to the array. Genomic sites enriched for H3-K4me3 or H3-K27me3 binding were identified using ACME  and the given p values for each probes from ACME were plotted into graphs using the SAS 9.1 program.et. al. http://www.pantherdb.org/) To determine overlap among genes bound by c-Myc in mES cells (this paper) as well as by PcG and H3-K27me3 in mES cells we merged the list of genes on our arrays with those on the platform used by Boyer, c-Myc cDNA was amplified from ES-derived cDNA library and cloned into lentiviral vector pLenti6/V5-DEST (Invitrogen) for overexpression. The shRNA sequences against c-Myc was designed by using Dharmacon siDESIGN and then hairpin shRNA oligonucleotides were annealed and cloned into modified FUGW lenti-vector with H1 promoter . Lentiviral production was followed the protocol described in Lois C. et. al., Science, 2002, then drug selection for positive integration in ES cells followed by 3 days infection. Positive clones were picked and expanded for custom ChIP-chip array, qChIP-PCR, gene expression analysis by RT-PCR, and differentiation subsequent to LIF withdraw.Total RNA was extracted by TRIzol (Invitrogen) reagent and cDNA was synthesized by SuperScript II kit (Invitrogen) according to the manufacturer's instructions. The transcript expression level was measured by Real-Time PCR with SYBR Green detection on ABI7900HT system (Applied Biosystems). S16 RNA was used as internal control for normalization (ΔCt). The expression level was measured by subtraction of ΔCt from either overexpression or knock-down to vector control (ΔΔCt). Primers for Real-Time PCR are available upon request.MTT assays: ES cells were seeded 1000 cells per well of 0.1% gelatin-coated 96-well plate supplied with LIF and analyzed 24, 48, 72, 96 hours after plating. At each time point, ES cell medium was replaced with 100 µl 1 mg/ml MTT (Molecular Probe) for labeling. Subsequently, 100 µl of SDS-HCl was added to each well and mixed thoroughly after 3 hours labeling at 37°C. Following 4 hours incubation at 37°C in a humidified chamber, absorbance was recorded at 570 nm. Triplicate analysis was applied to each time point.5 cells was plated on 1 well of 6-well plate and counted by SSEA-1 positive population after 24 and 48 hours later. Briefly, ES cells were harvested by trypsin and washed with PBS prior to SSEA-1  antibody incubation for 2 hours. After washing with PBS twice, cells were incubated with FITC-conjugated secondary antibody for 1 hour. Finally, cells were washed twice and resuspended in PBS before FACS analysis. The population (%) of positive SSEA-1 stained cells was compared to unstained cells, FITC-isotype control, undifferentiated ES cells, and cells stained with secondary antibody alone. The SSEA-1 positive cell numbers were subjected to the calculation of ”cell population doubling time“ with the equation Yend = Ystart×2 (t/T).Population doubling time assay: 10Figure S1Identification of Myc binding sites in ES cells. In addition to the log2 ratio >2 we also applied the widely used ACME/R anaylsis which employs a sliding window = 500 b.p. and a threshold = 95% across gene promoters to determine significance  of each probe. Our identification of Myc binding sites is based on significant peaks  that take into account binding events on neighboring probes. Shown are a few typical promoters in which are plotted log2  and -log10 .(0.15 MB PDF)Click here for additional data file.Figure S2Validation of anti-Myc ChIP-chip results in the R1 mES cell line We randomly selected 44 genes for validation and confirmed the enrichment of 44 genes on the array by qChIP-PCR. Known Myc target genes such as Bmi1 served as a positive control. Genes Evx2 and Sox21 not identified in our ChIP-chip analysis and not previously reported as c-Myc targets were used as negative controls for validation by qChIP-PCR. Bar heights represent the mean “Fold” enrichment from 2 independent sets of anti-Myc ChIP-enriched versus total input genomic DNA. Six genes were not consistent in both our ChIP-chip assay and qChIP-PCR. From this we calculate the average “false discovery rate” in R1 ES cells to be 11.3%.(0.01 MB PDF)Click here for additional data file.Figure S3Determination of ES cells proliferation rate in response to c-Myc level. c-Myc levels were manipulated by Lentiviral-delivered overexpression or shRNA knock-down of c-Myc in ES cells. (A) BrdU labeling of a representative ES colonies. (B) Mean growth rate was measured by MTT assay. Mean was measured from triplicate experiments. (C) Mean population doubling time obtained from 4 independent experiments.(0.03 MB PDF)Click here for additional data file.Figure S4Sox2 and HirA protein expression in mES cells. Antibodies against Sox2 and HirA were used for immunoblots prepared from mES cells infected with empty lentiviral vector alone ; with a lentiviral vector expression shRNA against c-myc (middle panel); or with a lentiviral vector expressing c-myc (right panel). γ-tubulin was used for loading control.(0.04 MB PDF)Click here for additional data file.Figure S5H3-K27me3 (red) and H3-K4me3 (green) patterns at promoters of genes activated following c-Myc overexpression. DNA immunprecipitated with anti-H3-K4me3 or anti-H3-K27me3 was applied to the custom-designed array (see text). Enrichment ratios  for ChIP-enriched versus total input genomic DNA for 427 genes were processed by ACME and assigned p-values  identifying significant sites were plotted see . Red pea(1.49 MB PDF)Click here for additional data file.Figure S6H3-K27me3 (red) and H3-K4me3 (green) patterns at promoters of genes repressed following c-Myc overexpression. DNA immunprecipitated with anti-H3-K4me3 or anti-H3-K27me3 was applied to the custom-designed array (see text). Enrichment ratios  for ChIP-enriched versus total input genomic DNA for 427 genes were processed by ACME and assigned p-values  identifying significant sites were plotted see . Red pea(1.14 MB PDF)Click here for additional data file.Figure S7H3-K27me3 (red) and H3-K4me3 (green) patterns at promoters in which bivalency is partly lost upon c-Myc overexpression. DNA immunprecipitated with anti-H3-K4me3 or anti-H3-K27me3 was applied to the custom-designed array (see text). Enrichment ratios  for ChIP-enriched versus total input genomic DNA for 427 genes were processed by ACME and assigned p-values  identifying significant sites were plotted see . Red pea(4.21 MB PDF)Click here for additional data file.Figure S8H3-K27me3 (red) and H3-K4me3 (green) patterns at promoters in which bivalency is gained upon c-Myc overexpression. DNA immunprecipitated with anti-H3-K4me3 or anti-H3-K27me3 was applied to the custom-designed array (see text). Enrichment ratios  for ChIP-enriched versus total input genomic DNA for 427 genes were processed by ACME and assigned p-values  identifying significant sites were plotted see . Red pea(1.11 MB PDF)Click here for additional data file.Figure S9H3-K27me3 (red) and H3-K4me3 (green) patterns at promoters showing only small or no changes in H3-K27me3 and H3-K4me 3 pattern. DNA immunprecipitated with anti-H3-K4me3 or anti-H3-K27me3 was applied to the custom-designed array (see text). Enrichment ratios  for ChIP-enriched versus total input genomic DNA for 427 genes were processed by ACME and assigned p-values  identifying significant sites were plotted see . Red pea(5.05 MB PDF)Click here for additional data file.Figure S10c-Myc binding sites on a subset of gene promoters in mES cells. c-Myc levels were manipulated by Lentiviral-delivered overexpression or knock-down of c-Myc in mES cells followed by anti-Myc ChIP-chip analysis on custom- designed arrays (see text). Unprocessed enrichment ratios  present ChIP-enriched versus total input genomic DNA  (y axis) for all probes within a genomic region 2kb upstream and 1kb downstream of the TSS. The TSS and direction of transcription are denoted by arrows at the bottom of the figure. Blue bars: endogenous c-Myc binding in WT mES cells, Red bars: Myc overexpressing mES cells, Green bars: Myc knock-down mES cells. Negative values occur when there is no enrichment in ChIP DNA relative to input.(0.16 MB PDF)Click here for additional data file.Table S1List of Myc associated promoters in AK7 ES cells.(0.58 MB XLS)Click here for additional data file.Table S2Myc associated promoters showing H3-K27me3 Myc associated promoters showing Suz12 binding Myc associated promoters showing Suz12 binding and H3-K27me3 Note: data on Suz12 and H3-K27me3 from Boyer et al. 2006 (ref 16).(0.15 MB XLS)Click here for additional data file.Table S3List of genes organized into groups according to whether they are activated or repressed by c-Myc overexpression; whether they lose or gain bivalent H3-methylation marks; or show little or no change in methylation pattern. The individual pattern for each gene on the list is presented in (0.02 MB XLS)Click here for additional data file.Table S4List of genes included in custom-design microarray(0.50 MB XLS)Click here for additional data file.


HIV-1 provirus, either as a chromosomal integrant or as an episomal plasmid in HeLa cells, forms a transcription-dependent gene loop structure between the 5′LTR promoter and 3′LTR poly(A) signal. Flavopiridol-mediated inhibition of RNA polymerase II elongation blocks 5′ to 3′LTR juxtaposition, indicating that this structure is maintained during transcription. Analysis of mutant or hybrid HIV-1 plasmids demonstrates that replacement of the 5′LTR promoter with CMV or the 3′LTR poly(A) signal with a synthetic element (SPA) permits gene loop formation, suggesting that these interactions are not retroviral specific. In addition, activation of the 5′LTR poly(A) signal or inactivation of the 3′LTR poly(A) signal abolishes gene loop formation. Overall, we demonstrate that both ongoing transcription and pre-mRNA processing are essential for gene loop formation, and predict that these structures represent a defining feature of active gene transcription. Transcription of protein-encoding genes in eukaryotes is mediated by RNA polymerase II (Pol II) holoenzyme, where drastic alterations to its composition and activity occur during the transcription cycle. Pol II is recruited to promoters at initiation through interaction with multiple transcription factors bound to adjacent or distant regulatory elements (enhancers) by direct enhancer-promoter association, resulting in “looped” conformations that juxtapose otherwise distant genetic elements . InitiatMany features of this process are defined for HIV-1 . The lonSeveral Pol II-transcribed yeast genes adopt a circular conformation associated with transcriptional activation . This reUsing p3C we show that loop formation was unaffected by replacing either the 5′LTR U3 promoter with the CMV promoter or the 3′LTR with a heterogeneous synthetic poly(A) (SPA) site. Consequently, these interactions are not uniquely a property of retroviruses such as HIV-1. We also show that switching polyadenylation from the 3′LTR to 5′LTR (by MSD mutation) causes a loss of proviral looping. Processing elements involved in 5′-3′ juxtaposition were defined by mutating the endogenous 3′LTR or SPA element, both of which specifically affect looping. Our studies reveal that ongoing transcription and pre-mRNA processing are essential for gene loop formation.trans-activator Tat mRNAs . Integr factors . U1 late factors  or expos factors . Both st factors . Taqman  factors Aa withinU1 to int-Chr2 and Tat2U1 to int-ChrX  Ac. UntreFMP27 gene of S. cerevisiae  signal interaction occurs across the provirus, signifying that loop formation is separate to the site of transcriptional termination, which occurs at least 1 kb downstream of both integrate 3′LTRs as determined by RT-PCR (data not shown). A second interaction is observed between both LTRs and a proviral sequence adjacent to the 5′LTR containing the MSD. These results are summarized as a ribbon diagram A.We initially considered that HIV-1 proviral gene loops may depend on their chromatin environment. However, difficulty in manipulating specific sequences in a genomic context encouraged us to determine if looping could be demonstrated on a transfected template. Plasmids have been shown to adopt nuclear plasmid transcription domains separate from chromosomal territories . We trancis. These results are consistent with the lack of intrachromosomal 3C signal between int-ChrX and int-Chr2 in U1 cells , which requires additional purification steps by repeated centrifugation through sucrose cushions necessary to remove excess transfected cytoplasmic plasmid accumulated around the nuclear periphery. We performed BanI p3C with internal HIV-1 primers previously used for chromosomal 3C analysis A with pUU1 cells B. CombinU1 cells B.Pol II CTD phosphorylation changes through the transcription cycle and is involved in several postinitiation steps . We therUpon transcriptional activation, very low amounts of Ser2 were detected within the provirus Ab, with (13L-substituted Tat (int-ChrX) retains some of its trans-activating activity, TPA-induced proviral expression results in production of endogenous Tat protein in U1 cells, effectively making these cells a complete HIV-1 transcription environment. We predict that Tat activity shifts CDK9 phosphorylation from Ser2P to Ser2P/Ser5P, explaining the marked similarity of their ChIP profiles with CDK9  site usage, the ability of Pol II to reach the 3′LTR may be affected. Loss of loop products could therefore be linked to the lack of an intact RNA extending between the 5′ and 3′LTR. We therefore analyzed a number of 3′ end constructs and their effect on loop formation, while maintaining the 5′LTR B. Using We have demonstrated that a specific gene loop conformation is imposed on integrated HIV-1 provirus after transcriptional activation and that different chromosomal contexts of these two integrates modulates both transcriptional levels and the consequent capacity to form gene loop structures. U1 int-Chr2 provirus is only weakly inducible by TPA or Tat and forms lower levels of gene looping that only increase a few fold after induction C and 2D.S. cerevisiae  factors (Ssu72p and Pta1) are required for loop formation  signal is required for gene loop formation, and this is itself dependent on coupled splice site recognition. We thus propose that gene loop structures constitute an integral part of defining a successful vertebrate gene transcription unit.Chromosomal regions flanking U1-(U937) cell line proviral integrates were defined according to Recombinant HIV-1 pNL4-3 plasmid  used in l-glutamine, 10% FCS). U1 cells (density 5 × 105) were treated with 10−7M TPA (Sigma) for the times indicated. HeLa cells (2 × 107) were transfected with 5 μg pNL4-3/pNL4-3.R-E- proviral constructs or pUC18 control using Fugene (Roche). 0.5 μg Tat expression vector (7) cells  with transfection efficiency measured by FACS analysis. At time points indicated, cells were washed twice with 1×PBS for RNA purification or resuspended in fresh medium and crosslinked for ChIP and 3C analysis. Flavopiridol-treated samples were obtained by treating cells with TPA (1 × 10−7M) for 16 hr and then adding 500 nM flavopiridol for 5 hr . HIV-1 transcript levels were measured with TaqMan (qRT-PCR) using HIV-1 nuc1 primers and housekeeping gene controls (GAPDH/18SrRNA) on an AbiPrism 7000 machine (Applied Biosystems).U1 and HeLa cells were maintained at low-passage level in RPMI 1640 (+20 mM n vector  was cotrn vector  were eleChIP was performed as described . To dete7 U1 cells with modifications (7) were crosslinked as described (3C  was perfications . For p3Cescribed . Nuclei escribed , except


Histone deacetylase Rpd3 is part of two distinct complexes: the large (Rpd3L) and small (Rpd3S) complexes. While Rpd3L targets specific promoters for gene repression, Rpd3S is recruited to ORFs to deacetylate histones in the wake of RNA polymerase II, to prevent cryptic initiation within genes. Methylation of histone H3 at lysine 36 by the Set2 methyltransferase is thought to mediate the recruitment of Rpd3S. Here, we confirm by ChIP–Chip that Rpd3S binds active ORFs. Surprisingly, however, Rpd3S is not recruited to all active genes, and its recruitment is Set2-independent. However, Rpd3S complexes recruited in the absence of H3K36 methylation appear to be inactive. Finally, we present evidence implicating the yeast DSIF complex (Spt4/5) and RNA polymerase II phosphorylation by Kin28 and Ctk1 in the recruitment of Rpd3S to active genes. Taken together, our data support a model where Set2-dependent histone H3 methylation is required for the activation of Rpd3S following its recruitment to the RNA polymerase II C-terminal domain. Acetylation of histone N-terminal tails occurs on nucleosomes as a gene is being transcribed, therefore helping the RNA polymerase II traveling through nucleosomes. Histone acetylation, however, has to be reversed in the wake of the polymerase in order to prevent transcription from initiating at the wrong place. Rpd3S is a histone deacetylase complex recruited to transcribed genes to fulfill this function. The Rpd3S complex contains a chromodomain that is thought to be responsible for the association of Rpd3S with genes since it interacts with methylated histones, a feature found on transcribed genes. Here, we show that the recruitment of Rpd3S to transcribed genes does not require histone methylation. We found that Rpd3S is actually recruited by a mechanism implicating the phosphorylation of the RNA polymerase II C-terminal domain and that this mechanism is regulated by a transcriptional elongation complex called DSIF. We propose that the interaction between the Rpd3S chromodomain and methylated histones helps anchoring the deacetylase to its substrate only after it has been recruited to the elongating RNA polymerase. Histone acetylation was the first covalent histone modification shown to be involved in transcription regulation. Indeed, histones at the promoter of active genes tend to be hyper-acetylated while repressed genes have promoters with hypo-acetylated nucleosomes. It is now well established that this is due to the recruitment of histone acetyltransferases (HATs) and histone deacetylases (HDACs) by transcriptional activators and repressors, respectively in vitro studies have shown that Rpd3S is recruited to H3K36-methylated nucleosomes and that its Rco1 and Eaf3 subunits are essential for this recruitment in vitro. These studies also indicate that genome-wide histone acetylation levels on promoters and coding regions are altered when either H3K36 methylation or Rpd3S is disrupted.Recent proteomic studies have determined that Rpd3 is found in two distinct complexes: the large (Rpd3L) and the small (Rpd3S) complex in vitro observation that the Eaf3 CHD binds preferentially to H3K36me3 peptides or nucleosomes in vivo. In addition, whether Rpd3S is recruited to all transcribed genes or to only a subset of them has never been assessed. In order to address these questions, we performed genome-wide ChIP-chip experiments looking at both Rpd3S- and Rpd3L-specific subunits in wild type cells and in various mutants, including set2Δ and H3K36A . Finally, we show that the yeast DSIF transcription elongation factor negatively regulates Rpd3S recruitment. Based on these results, we propose that the recruitment and activity of Rpd3S on ORFs depend on a two steps mechanism: an initial recruitment to the elongation complex -coordinated by DSIF and RNAPII phosphorylation-, followed by the H3K36me3-dependant modulation of Rpd3S activity through the Eaf3 chromodomain.Quite interestingly, our data show that Rpd3S specifically binds to the coding region of actively transcribed genes whose promoters are also bound by Rpd3L. Surprisingly, the binding of Rpd3S to active genes is not dependant on Set2-mediated H3K36 methylation. However, methylation by Set2 is required for the activity of Rpd3S, as assayed by histone acetylation and RNAPII levels. We also provide −8), cell cycle , etc.), shows binding of both Rpd3 and Sds3 to the promoter. The presence of Rco1 is not detectable on these genes. This cluster therefore represents genes repressed by Rpd3L, which is consistent with the fact that these genes have low level of RNAPII  was similar to wild type, suggesting that deletion of SET1 and/or DOT1 can partially suppress the set2Δ phenotype. Taken together, these data demonstrate that H3K36 methylation is not required for the recruitment of Rpd3S to most genes.First, we looked at Rco1 binding in a et genes . The delet genes , while dSince Rpd3S occupancy seems to be more dependent on H3 methylation at some genes than others, we looked more closely at clusters 5 and 6. As shown in set2Δ mutants, along with null mutants of Rpd3S subunits Eaf3 or Rco1, exhibit a cryptic transcription phenotype It is known that set2Δ, H3K36A or Rpd3S mutants  by ChIP-chip. We used H4K5 acetylation to score for Rpd3S activity because it was shown previously to be a robust read out for Rpd3 activity in ChIP-chip assays  mutants . Histone mutants . These rThe effect of the loss of Rpd3S activity on histone acetylation and RNAPII distribution on ORFs is more complex than previously described. Our data indeed suggest that both histone acetylation and RNAPII occupancy are redistributed in a more even manner across the genome than expected. A plausible explanation of the genome-wide averaging of RNAPII and histone acetylation levels in Set2 and Rpd3S mutants would entail aberrant recruitment of the transcriptional apparatus to low-expression genes whose coding regions were not reset properly by a, now inactive, Rpd3S complex. Assuming a limited pool of transcription machinery in a cell, this would result in a lower abundance of RNAPII at the more active ORFs, and aberrant genomic acetylation levels.rco1Δ, rco1-PHDΔ and eaf3-CHDΔ mutants, suggest that the loss of histone H3 methylation at lysine 36 affects ORF identity through a modulation of Rpd3S deacetylase activity rather than through altered recruitment of the Rpd3S complex on coding regions as previously thought.These results, combined with the Rpd3S occupancy profiles in spt4Δ cells . As shown in SPT4 leads to massive changes in Rco1 binding across the genome. Notably, the effect is far more dramatic compared to the deletion of SET2 . Overall, these effects lead to a distribution of Rpd3S that correlates better with RNAPII occupancy than in wild type cells  suggesting that DSIF acts as a general elongation factor. Moreover, it is present across the whole ORF, indicating that it travels with RNAPII, but is further enriched in the 3′ end of genes  and found strong differences between the wild type and henotype  leading 1Δ cells , whereas1Δ cells  or spt4Δ4Δ cells , suggestCTK1, the major serine 2 kinase, has a clear effect on Rco1 occupancy (compare solid line with dashed line). To test the effect of serine 5 and 7 phosphorylation, we used a strain carrying ATP-analog-sensitive alleles of KIN28 since the deletion of the KIN28 gene is lethal. As shown in in vitroin vitro experiments, suggest that phosphorylation of the RNAPII CTD stimulates the recruitment of Rpd3S to transcribed genes while the elongation factor DSIF counteracts this recruitment.The data presented above demonstrate that Spt4 negatively regulates the association of Rpd3S to highly transcribed genes. The data also suggest that phosphorylation of the RNAPII CTD, notably at serine 2, plays some role in the recruitment of Rpd3S to active genes in the absence of Spt4. We next tested whether the phosphorylation of the CTD is implicated in the association of Rpd3S with transcribed genes in wild type cells and performed ChIP-chip experiments of Rco1 in mutants for the known CTD kinases. As shown in bur1AS) has a profound effect on Rco1 occupancy. In the absence of a functional Bur1, Rco1 is depleted from the coding region and redistributed to promoter regions (spt5ΔC), the region phosphorylated by Bur1, caused a similar, although milder, phenotype  Rpd3S targets a subset of transcribed genes; 2) Rpd3L is present at the promoter of genes where Rpd3S is bound; 3) The activity of Rpd3S requires methylation of H3K36 by Set2 but its association with active genes does not; 4) The recruitment of Rpd3S on ORFs requires the phosphorylation of the RNAPII CTD by Kin28 and Ctk1; and 5) The DSIF elongation complex counteracts the recruitment of Rpd3S to transcribed genes, a phenomenon that is regulated by the phosphorylation of Spt5 by Bur1. We therefore propose a model where the opposing effects of CTD phosphorylation and DSIF on Rpd3S recruitment together allow for the complex occupancy profile of that HDAC in vivo  would only exist in solution and would represent a module that joins Rpd3L after initiation. This hypothesis is supported by data provided by Collins et al. set2Δ and H3K36A mutants. Moreover, although the methylation state of H3K36 does not affect Rpd3S binding at highly transcribed genes, it has an effect on the occupancy of Rpd3S at ORFs showing lower transcription levels. This suggest that, despite not providing the main recruitment signal, H3K36 methylation provides some stabilizing effect on the association of Rpd3S with chromatin. While Rpd3S recruitment is largely independent on H3K36 methylation, the activity of Rpd3S, however, depends on the integrity of the Set2/Rpd3S pathway, namely Set2-dependant H3K36 methylation, the Rco1 PHD and the Eaf3 CHD. We therefore propose that the main function of H3K36 methylation is to “activate” Rpd3S after it has been recruited by virtue of its association with the RNAPII CTD. This may involve the anchoring of Rpd3S on chromatin via the Eaf3-H3K36me interaction.We show that the recruitment of Rpd3S to active genes does not require H3K36 methylation, Set2 or the Eaf3 chromodomain. Interestingly, Rco1 binding is not abolished in a triple deletion mutant for all the known yeast HMTs , ruling out the possibility that the previously described interaction between H3K4me3 and Eaf3 ctk1Δ strain, we also show that phosphorylation of serine 2 also contributes -although to a lesser extend than serine 5- to the association of Rpd3S with transcribed genes. These results are in perfect agreement with a recent paper from the Hinnebusch group who showed that RNAPII CTD peptides harboring a phosphate group on both serines 5 and 2 have a high affinity for Rpd3S in vitro while a single phosphate on serine 5 has a reduced affinity Our data clearly show that phosphorylation by Kin28 is absolutely required for the association of Rpd3S with the coding region of transcribed genes. This suggests that phosphorylation of serine 5 provides the signal for the association of Rpd3S with early elongating RNA polymerases II molecules. Since Kin28 was recently shown to phosphorylate serine 7 in addition to serine 5, we cannot rule out the possibility that serine 7 phosphorylation also contributes to the binding of Rpd3S to RNAPII. Using a SPT4, a subunit of DSIF, has a profound effect on Rpd3S occupancy in vivo. In the absence of Spt4, Rpd3S associates with the genome in a manner that correlates better with transcription than in wild type cells. This suggests that DSIF is involved in regulating the amount of Rpd3S on transcribed genes. DSIF appears to prevent the association of Rpd3S to a subset of transcribed genes, and even at genes occupied by Rpd3S, DSIF also plays a role since it prevents the hyper-accumulation of the HDAC in the 3′end of the gene. How exactly an elongation factor can regulate the association of a HDAC with elongating RNAPII remains obscure but we envision several mechanisms by which it may operate: 1) Rpd3S may directly or indirectly interact with DSIF; 2) The association of DSIF with the elongation complex may prevent the association of the HDAC; 3) DSIF may modulate the speed of the elongation complex in a way that makes it less favorable for Rpd3S binding; 4) DSIF may also impinge on the phosphorylation of the RNAPII CTD. More complex mechanisms may also be envisioned. For instance, Pin1, a proline isomerase that may modify the RNAPII CTD, binds to both, phosphorylated DSIF Interestingly, we found that deletion of in vivo. As expected, both the catalytic inactivation of Bur1 and the removal of its substrate (by deleting the CTD of Spt5) have a dramatic impact on Rpd3S occupancy. Both these mutants indeed show a decreased in Rpd3S occupancy along genes. These data suggest that phosphorylation of Spt5 by Bur1 negatively regulate the activity of DSIF on Rpd3S recruitment. In addition, the inactivation of Bur1 also causes Rpd3S to redistribute to promoter regions, a phenomenon that we also observed in set2Δ cells. Since Bur1 phosphorylates Rad6, which is also required for H3K36 methylation by Set2, it appears likely that this redistribution in Bur1 mutants is due to its effect on H3K36 methylation via Rad6. Why a lack in H3K36 methylation leads to an association of Rpd3S with promoters remains unknown, but is in agreement with the previous observation that histone acetylation decreases at promoters in these mutants.In human and yeast, DSIF is regulated by phosphorylation of the CTD of its Spt5 subunit. In yeast, this phosphorylation is mediated by the Bur1 cyclin-dependent kinase in vivo. Our study highlights a complex network of protein-protein interactions mediated by phosphorylation of several substrates by at least three kinases. Interestingly, there is previous evidence in the literature linking these kinases to the function of DSIF. First, P-TEFb (Cdk9) and Bur1 can phosphorylate DSIF Taken together, our in-depth analysis of Rpd3S genomic occupancy has revealed several key insights about its recruitment to genes 600 of 0.6–0.8 before crosslinking, unless otherwise indicated. For ChIP-chip, most strains were crosslinked with 1% formaldehyde for 30 min at room temperature on a wheel. The Rco1-9myc strains were crosslinked with 1% paraformaldehyde for 30 min at room temperature followed by 90 min at 4°C on a wheel. ATP analog-sensitive strains were treated with 6 µM of NAPP1 for 15 minutes prior to crosslinking.All strains used in this study are listed in 7 pan-mouse IgG DynaBeads (Invitrogen) per sample. For histone H4K5 acetylation ChIP, we used 4µL of a rabbit serum (Upstate 07-327) coupled to 2×107 protein G DynaBeads per sample. Histone H3K36me3 was immunoprecipitated with 4µL of antibody (Abcam Ab9050) coupled to 2×107 protein G DynaBeads per sample. Histone H4 levels were assayed with 2µL of an antibody raised against recombinant yeast histone H4 (a gift from Alain Verreault) coupled to 2×107 protein G DynaBeads per sample. RNAPII ChIPs were done using 2µL of 8WG16 antibody coupled to 2×107 pan-mouse IgG DynaBeads per sample. Note that in our ChIP-chip assays, 8WG16 generates profiles that are nearly identical as using a tagged RNAPII (data not shown). 8WG16 is therefore used here to measure total RNAPII levels on genes. RNAPII CTD serine 2 phosphorylation was assayed using 5µL of H5 antibody (Covance Research MMS-129R-200) coupled to 2×107 protein G DynaBeads per sample.ChIP experiments were performed as per 2 (Histone H4/input) from Log2 (H4K5 acetyl/input). All microarray experiments described in this work are listed in The microarrays used for location analysis were purchased from Agilent Technologies  and contain a total of 44,290 Tm-adjusted 60-mer probes covering the entire genome for an average density of one probe every 275 bp (±100 bp) within the probed regions . Myc-tag ChIPs were hybridized against ChIPs from isogenic strains that did not contain the tag as controls. Acetylation, RNAPII and histone H4 ChIPs were hybridized against a sample derived from 400ng of input (non-immunoprecipitated) DNA. Acetylation levels were normalized to histone H4 levels by subtracting Log2 ratio of each spot of combined datasets was then converted to Z-score, similar to Hogan et al. http://genome.ucsc.edu/). All data analyses described here were done using data from protein-coding genes longer than or equal to 500 bp. Median Z-score values for promoter and complete length of each annotation from SGD (version Feb. 02 2008) were calculated without interpolation Click here for additional data file.Dataset S2A UCSC Genome Browser-ready file containing all the log2 enrichment ratios for all the H3K5ac localization data from this study.(4.06 MB GZ)Click here for additional data file.Dataset S3A UCSC Genome Browser-ready file containing all the log2 enrichment ratios for all the RNAPII localization data from this study.(4.52 MB GZ)Click here for additional data file.Dataset S4A UCSC Genome Browser-ready file containing all the log2 enrichment ratios for all the experiments not already in (2.26 MB GZ)Click here for additional data file.Dataset S5A file containing the average Z-score calculated on each genome annotation.(4.88 MB ZIP)Click here for additional data file.Figure S1A complement to (0.23 MB PDF)Click here for additional data file.Figure S2in vivo. (A–D) Mapping of Rco1 occupancy on genes contained within clusters 5  and 6  from A complement to (0.45 MB PDF)Click here for additional data file.Figure S3A complement to (0.17 MB PDF)Click here for additional data file.Figure S4spt4Δ (dashed lines) cells on genes contained within clusters 7 , 8 , 9 , and the non-identified following clusters 10  and 11  of the spt4Δ (dashed line) cells on the 222 genes contained within clusters 4 from A complement to (0.55 MB PDF)Click here for additional data file.Figure S5spt4Δ/ctk1Δ mutant. (A–D) Mapping of Rco1 occupancy on genes grouped according to their lengths in WT (A), set2Δ (B), ctk1Δ (C), spt4Δ (D), spt4Δ/set2Δ (E), and spt4Δ/ctk1Δ (F) cells .Rpd3S ORF occupancy level is gene length-dependant in a (0.39 MB PDF)Click here for additional data file.Table S1A list of all the yeast strains used in this study.(0.09 MB DOC)Click here for additional data file.Table S2A list of all the microarray hybridizations done in this study.(0.07 MB DOC)Click here for additional data file.