Eukaryotic transcription factors are grouped into families and, due to their similar DNA binding domains, often have the potential to bind to the same genomic regions. This can lead to redundancy at the level of DNA binding, and mechanisms are required to generate specific functional outcomes that enable distinct gene expression programmes to be controlled by a particular transcription factor. Here we used ChIP–seq to uncover two distinct binding modes for the ETS transcription factor ELK1. In one mode, other ETS transcription factors can bind regulatory regions in a redundant fashion; in the second, ELK1 binds in a unique fashion to another set of genomic targets. Each binding mode is associated with different binding site features and also distinct regulatory outcomes. Furthermore, the type of binding mode also determines the control of functionally distinct subclasses of genes and hence the phenotypic response elicited. This is demonstrated for the unique binding mode where a novel role for ELK1 in controlling cell migration is revealed. We have therefore uncovered an unexpected link between the type of binding mode employed by a transcription factor, the subsequent gene regulatory mechanisms used, and the functional categories of target genes controlled. One of the major outstanding questions in eukaryotic gene regulation is how transcription factors with seemingly very similar DNA binding specificities elicit specific biological responses. The ETS transcription factor family provides a paradigm for investigating this phenomenon. Here, we have focused on the ETS transcription factor ELK1, and by combining genome-wide binding analysis coupled with gene expression analysis we have dissected two distinct gene regulatory activities for this transcription factor. In each of these regulatory modes, ELK1 exhibits distinct DNA binding characteristics which correlate with either positive or negative transcriptional activities and give rise to functionally distinct gene expression programmes. We demonstrate a novel function for ELK1 in controlling cell migration through one of these regulatory modes. Thus, we have demonstrated a clear link between the types of regulatory region bound by a transcription factor and its ability to control gene expression (i.e. in a positive or negative manner) and the functional downstream consequences of its target gene cohort. This work has implications for understanding how members of other multi-protein transcription factor families might function to generate different downstream functional consequences through engaging with different types of regulatory regions. This is typified by the ETS-domain containing transcription factors, with human cells encoding 28 different proteins  subfamily of ETS-domain transcription factors  are also deregulated under at least one of the conditions we tested upon ELK1 depletion (245 were changed in the absence of EGF and 223 in the presence of EGF). Roughly equal numbers of ELK1 binding regions are associated with up- and downregulated genes, irrespective of EGF treatment . ImportaTogether, these results identify a core set of direct ELK1 target genes, whose regulatory regions are bound by ELK1 and whose expression is perturbed upon depletion of ELK1. This set of genes likely represent directly regulated ELK1 target genes which we subsequently analysed further and are henceforth referred to as “ELK1 target genes”.Previous ChIP-chip studies using promoter arrays demonstrated that ELK1 binding regions fall into three broad categories; regions that are also bound by SRF, regions that can also be bound by other ETS transcription factors and regions which are apparently uniquely bound by ELK1 independently from these other transcription factors de novo search for over-represented DNA motifs within the binding regions. Three prominent motifs were identified in the “unique” dataset corresponding to ETS, SRF and AP1 binding sites, whereas the “redundant” dataset revealed only a motif corresponding to an ETS transcription factor binding site (in vitro binding site selection study in vitro. Further inspection of the ELK1 binding regions demonstrated that the frequency of the three hexamers comprising the core CCGGAAGT binding motif was much higher in the “redundant” (763 sites in 226 regions) than in the “unique” dataset (403 sites in 303 regions). This also suggests that more than one binding event might be associated with “redundant” regions. Indeed, the number of motifs per region corresponding to hexameric derivatives of CCGGAAGT is significantly higher in the “redundantly” bound regions (commonly three sites per region) than in the “unique” regions   of SRF motifs appear to form “modules” with adjacent ETS motifs in the “unique” dataset, where the two motifs co-occur within a 50 bp window  and those which can also be bound by GABPA (termed “redundant”). First, we examined their location relative to the nearest TSS. The “unique” regions showed a broad distribution with only 26% being located within 2 kb of the TSS, whereas 93% of the “redundant” regions were located in this region and these were largely tightly centered around the TSS . Next, wing site . Similar region) . We invedatasets . Howeverp window . It is pp window . Interesp window . Indeed,p window .Together, these results therefore identify two major types of ELK1 binding regions which can be independently characterized by their locations, the types and numbers of DNA binding motifs they contain and the factors which can potentially co-occupy or compete for binding to these sites. Regions which are uniquely bound by ELK1 tend to contain fewer and/or weak ETS binding motifs and are often bound by other partner transcription factors such as SRF and AP1, whereas those redundantly bound by different ETS factors more often contain multiple strong ETS binding motifs, and there is little evidence for the occurrence of common co-binding transcription factors.SCNM1 and MDM4. Overall, these results indicate that the regulatory mode attributed to ELK1 strongly correlates with the type of ELK1 binding mode adopted on the target genes.Having defined two different types of ELK1 binding regions, we next wished to investigate whether these regions are associated either with different gene regulatory mechanisms, and/or with controlling different cellular processes. First, we used unsupervised k-means cluster analysis to provide an unbiased way to identify groups of genes which responded differently to ELK1 depletion in the presence and absence of concurrent treatment with EGF. Here, we focussed only on the 273 direct target genes identified as being associated with regions bound by ELK1 in the ChIP-seq analysis. Data are depicted relative to average expression of each gene across all conditions to enable common regulatory patterns to be discerned. Eight gene expression clusters were identified which show distinct patterns of responses to these different treatments . We thenNext, we wanted to examine whether the type of binding and regulation adopted by ELK1 might also relate to functionally distinct outcomes in terms of the cohorts of genes regulated. We therefore examined whether any of the enriched GO term categories for the direct ELK1 target genes are preferentially associated with any of the different expression clusters. Importantly, we found that genes from particular functional categories are not evenly spread throughout the clusters . InsteadThese results therefore reveal an unexpected link between the mode of ELK1 binding to the regulatory region of a gene, the type of regulation of the associated genes and the functional classifications of the target genes controlled. In particular, genes associated with the cytoskeleton and migration, tend to be regulated in a positive manner by ELK1, and also are apparently bound specifically by ELK1 as they are not targets for potential redundant binding by alternative ETS transcription factors like GABPA.CTGF and ACTB, are also positively regulated by ELK1. Thus ELK1 and SRF appear to have both distinct and common targets amongst the “migration/cytoskeleton” gene network.The above results indicate that a key role for ELK1 is mediated through its “unique” binding mode where it controls the activity of genes associated with the actin cytoskeleton and cell migration. To explore the functional significance of this association, we created networks of proteins encoded by “unique” and “redundant” ELK1 target genes using coexpression, textmining, knowledge and experimental data as proximity criteria to identify interconnectivities. These networks were screened for enrichment in GO terms centred on specific cellular functions. The genes associated with “migration/cytoskeleton” formed a prominent subnetwork within the “unique” dataset; more interestingly, a large number of the genes which form the core of this subnetwork were shown by microarray analysis to be misregulated following ELK1 depletion . The rolA second subnetwork of “cell survival” was also identified which is comprised of genes associated with the “unique” ELK1 dataset , but forOur results predict that the loss of ELK1 should have profound consequences for the cellular phenotype due to the collapse of the networks involved with controlling the relevant cellular functions, especially in processes associated with the cytoskeleton and downstream effects such as cell migration. To test this, we first investigated the status of the cytoskeleton following ELK1 depletion in MCF10A cells. Following ELK1 depletion, cells became less spread and exhibited altered cytoskeletal characteristics, including a loss of membrane protrusions and enhanced levels of subcortical actin . The losThese results confirm that ELK1-mediated regulation of a target gene network associated with the actin cytoskeleton and cell migration correlates with the expected phenotypic consequences. To confirm that the key ELK1 target genes in this network play a role in controlling cytoskeletal-related activities such as cell migration, we individually depleted nine of these genes  and analGenome-wide studies are beginning to reveal the target genes for many transcription factors and one of the unanswered questions is how specific responses are generated by a particular transcription factor, especially in the presence of other proteins from the same family that can potentially bind the same sites. Here we have investigated the ETS family transcription factor ELK1 and made the unexpected discovery, that the types of binding mode exhibited by ELK1, correlate not only with the regulatory outcomes at the associated target genes but lead to the control of distinct networks of genes with defined cellular functions.CCGGAAGT. Moreover, there is little evidence for the occurrence of common co-binding transcription factors. In contrast, the “unique” regions are located in a wide distribution around the TSS and generally show a relaxed match to the ETS binding motif. Furthermore, these regions contain fewer ETS-like binding motifs than the “redundant” regions. The “unique” regions also show motif enrichment and binding of other classes of transcription factors; AP1 and SRF. The association between ELK1 and SRF has been well established previously  and binding in a distinct manner independently from these. To simplify our analysis, and concentrate on the potential interplay with other ETS transcription factor family members, we focused on two modes of ELK1 binding; regions which can be occupied by GABPA and ELK1 (“redundant” regions) or regions that can be occupied by ELK1 and apparently not GABPA (“unique regions”). While further analysis of the redundant and unique regions for binding by other ETS proteins from other ChIP-seq studies broadly supports this classification, it is important to emphasise that we cannot definitively conclude that the apparently “unique” regions cannot be bound by another ETS factor under different conditions or in another cell type. Nevertheless, this classification enabled us to ultimately define an important role for ELK1 in controlling cell behaviour where it acts through genes associated with the “unique” regions. These two types of binding regions showed distinct characteristics. “Redundant” regions are generally centered on the TSS and at the sequence motif level, contain multiple sites which closely match the generic consensus ETS protein binding motif −6). This might reflect a more combinatorial mode of action of ELK1 when it is associated with distal binding regions.Importantly, the location of the ELK1 binding sites in proximal promoter (defined as +/−2 kb from the TSS) versus distal regions is not as effective at partitioning of the data. Indeed, the “unique” ELK1 binding sites in the proximal promoter regions share characteristics such as the distribution of binding motifs, over-represented functional categories of targets, and association of common regulatory events, with “unique” ELK1 binding sites located in the distal regions (data not shown). Thus, the mechanistic, regulatory, and functional connections that we observe are not due to binding site location and instead reflect whether ELK1 is likely working in a “unique” manner. However, when we partitioned the data according to a different definition of proximal binding (ie −7 kb to +2 kb from the TSS), we could readily detect SRF and AP1 motifs in the distal regions but ETS motifs were less obvious; the reciprocal was true in promoter-proximal regions (data not shown). Moreover, there were significantly more overlaps between distal ELK1 binding motifs with FOS ChIP-seq data Once the two types of ELK1 binding mode had been established, we were then able to correlate binding with regulatory outcomes. “Uniquely” bound regions are generally associated with genes where ELK1 functions in an activating capacity, whereas the converse is true of “redundantly” bound regions. Direct ELK1 target genes which are upregulated by EGF treatment are generally associated with “unique” ELK1 binding regions, consistent with a role for ELK1 as an EGF-responsive transcriptional activator. Strikingly, the majority of ELK1 target genes were barely affected by EGF treatment, despite the fact that ELK1 plays a regulatory role at a large proportion of these. Thus although ELK1 is generally thought to function by acting as a direct recipient of ERK pathway signaling, this appears to be the exception rather than the rule when considering its broader cellular function  and one was performed with an alternative siRNA duplex (Santa Cruz).For RNAi, MCF10A and MDA-MB-231 cells were plated out into a mixture of 83% growth medium, 17% OptiMEM (Invitrogen), 20 nM siRNA and 0.33% Lipofectamine2000 (Invitrogen) followed by replacement of the transfection mix with appropriate growth media after 12 hours. All siRNA constructs were ON-TARGETplus SMART pools from Dharmacon except for ELK1, where a custom-designed siRNA duplex corresponding to the sequence Real time RT-PCR was carried out as described previously MCF10A cells were seeded into 6-well plates , transfected with siELK1 or siGAPDH (control) and maintained in media depleted of EGF for 48 hours. Cells were then stimulated with complete media containing EGF for 30 minutes (with non-stimulated populations used as control). Three biological replicates were performed for each condition. RNA was isolated using the RNeasy kit (QIAgene) according to the manufacturer's protocol and quantifiied with a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies). Sample labeling and hybridization to Affymetrix GeneChip Human Genome U133 Plus 2.0 arrays were performed according to manufacturer's instructions.Upon collection of signal, technical quality control was performed with dChip (V2005) 10-transformed and z-scores were calculated For clustering, signal intensities assigned to each probeset were log2 and imaged for 15 hours. The area unoccupied by cells was measured at one hour intervals and normalised to values specific for the siGAPDH-transfected control. Alternatively, after 15 hours cells were fixed and stained with crystal violet. For single cell tracking, sparsely seeded cultured of MCF10A cells were treated identically as in wound healing assays (without the formation of wounds). All images were processed and analysed using ImageJ and Adobe Photoshop CS2.Cells seeded in duplicate into 12-well plates were transfected with appropriate types of siRNA in media depleted of EGF (MCF10A cells) or FBS (MDA-MB-231 cells). After 48 hours wounds were created in each well using sterile pipette tips; wounds were scanned visually to ensure similar width. Cells were washed twice with 1× PBS and media containing either 20 ng/ml EGF (MCF10A cells) or 10% FBS (MDA-MB-231 cells) was added. For MDA-MB-231 cells, after 18 hours cells were stained with crystal violet. Four images were taken for each of the tested conditions, the area unoccupied by cells in each image was measured. Average values for each biological repeat were normalised to control (siGAPDH transfection). For MCF10A cells, upon stimulation plates were transferred to a chamber heated to 37°C and aerated with 5% CO2-enriched chamber of a Leica DM IRE microscope equipped with motorised XYZ stages (Mauhauser) and a 20x/0.50 HC PlanFluotar (Ph2) objective. Images were acquired with a Coolsnap HQ CCD camera (Photometrics) through the Image Pro 6.3 software (Media Cybernetics LtD) and later processed using ImageJ.Fixed cells were visualised using a Leica DMIL upright microscope coupled with a diagnostic instruments HRP045-NIK camera and a 20x/0.30 CPlan Ph1 – air objective. Images were acquired through the SPOT Basic software (Diagnostic Instruments) and processed in ImageJ and Adobe Photoshop CS2. Live cells were imaged in a heated, COFor immunofluoresence experiments, cells were fixed with 3.7% paraformaldehyde in 1×PBS, permeabilised with 0.01% Triton X-100 in 1×PBS, washed twice with 1× PBS, stained for 20 min with a solution of AlexaFluor488 phalloidin (Invitrogen), then washed three times with PBS. For detecting DNA, cells were co-stained with a 1 µg/ml solution of the Hoechst dye. Cells were imaged using Olympus BX51 upright microscopes equipped with 20x/0.50 UPlanFln – air objectives and captured with a Coolsnap HQ camera through MetaVue Software (Molecular Devices).Western blotting was carried out with the primary antibodies; ELK1 , cFOS , ERK2 , and phospho-ERK . The proteins were detected either by chemiluminescence with SuperSignal West Dura Substrate (Pierce) and visualised with a Fluor-S MultiImager (Bio-Rad) or for infrared dye-conjugated antibodies, signal was collected with a Li-cor Odyssey Infrared Imager.6 MCF10A cells for a standard ChIP and 5.4×107 cells for a ChIP-seq experiment, grown in media depleted for EGF for 48 hrs for ELK1 ChIP or following stimulation with EGF re-addition for 2 hours for FOS. Bound promoters were detected by quantitative PCR using primers listed in ChIP assays using control IgG (Millipore) or antibodies specific to ELK1 (Epitomics) or cFOS (Santa Cruz) were performed essentially as described previously  using 3×10Samples for ChIP-seq were prepared as described above and libraries were generated and sequencing was performed on a Life Technologies SOLiD 3.5 System (Applied Biosystems) according to the manufacturer's protocols. Two repeats were performed.SOLiD_preprocess_filter_v1.pl, available at http://hts.rutgers.edu/filter/. Reads were then aligned to build 18 of human genome from March 2006 (hereafter referred to as hg18) using Corona-Lite 4.2.2 (Life Technologies) and allowing from 0 to 3 mismatches. Aligned reads were used for identification of peaks by MACS v. 1.3.7.1 SOLiD (Applied Biosystems) *.csfasta sequencer reads were truncated to 32 nt using a perl script: http://jura.wi.mit.edu/bioc/tools/compare.php).For association of ELK1 binding regions with potential regulated genes, each ChIP-seq region was assigned to its nearest gene annotated in the refGene table  of the RefSeq Genes track, downloaded from the UCSC Table Browser. This was based on the position of summit of each peak (identified by MACS) and the position of the TSS of the relevant RefSeq gene. Overlaps between ChIP-seq-associated genes and microarray-generated gene expression data were determined using an online tool (De novo motif discovery was carried out using Weeder v. 1.4.2 The genomic distribution of peaks was determined using CEAS CountRegexGFF_IUPAC_1input_nosummary.pl (identifies number of occurrences of a particular IUPAC string on one or both strands of one or more sequences provided in FASTA format) and CountRegexGFF_IUPAC_1input_simple_output.pl .Word-based motif searches were performed using two PERL scripts: For constructing networks, lists of gene names assigned to ChIP-seq regions were uploaded into STRING For functional profiling of ELK1 binding regions identified by ChIP-seq, Gene Ontology was performed using GREAT v. 1.2.6 Statistical analysis for qRT-PCR studies and ChIP assays were performed using paired, 2-tailed Student's t test. The error bars in all graphs represent standard deviation. Fisher's Exact tests and Chi-square tests were used to compare two different distributions of pairs of datasets with respect to a particular condition. Z-score analysis of overlaps between two gene name lists was performed using a PERL script using 10000 background lists of Official Gene Names of size of one of the comparator datasets. Average overlap with standard deviation was recorded and z-score was calculated as the ratio of difference between actual overlap and this average overlap over the standard deviation. Kolomogorov-Smirnov tests were used to determine the statistical significance of differences in shape and range between two distributions. The Genomic HyperBrowser was used to determine the statistical significance of overlaps of sets DNA regions with the following settings: regions of dataset 1 fixed, region sizes and number of dataset 2 fixed, tested hypothesis: overlaps more than expected, overlap tested for the whole genome, with each chromosome treated as a separate bin (maximum possible bin size).The ChIP-seq and microarray expression data have been submitted to array express (accession numbers E-MTAB-830 and E-MEXP-3407) and the data will be released upon publication.Figure S1Summary of microarray analysis of gene expression changes in either siGAPDH or siELK1-treated cells upon EGF stimulation. The numbers of genes which become up- or down-regulated upon EGF stimulation are indicated.(PDF)Click here for additional data file.Figure S2WNK1), where the maximum reaches 225. Specific binding of ELK1 to each region was validated in ChIP-qPCR experiments carried out in three biological repeats (shown on the right of the corresponding peak profiles); bars show the average percentages of input precipitated with the ELK1 antibody (blue bars) or non-specific IgG (yellow bars) with standard deviations. Titles of the graphs indicate the position of each region on a list ordered with descending tag count of ELK1 signal (prefix “L” denotes position on lower confidence list) and the name of the nearest annotated RefSeq gene. Binding regions from the lower confidence dataset are boxed. ABLIM2, CPLX1 and intron 3 of the SRF gene (SRFint3) are negative controls and EGR2 is a positive control. Data are summarized in Validation of ChIP-seq datasets. qPCR validation of a range of ELK1 binding regions identified by ChIP-seq. Binding regions were ordered by descending tag count and candidates were selected by taking binding regions approximately every fiftieth and every one-hundredth position in the lists from the high and lower confidence ELK1 ChIP-seq datasets. ChIP-seq peak profiles for the ELK1  and IgG  samples are shown on the left. Bars in profile windows indicate 500 nt; RefSeq annotated genes are shown (if present) and the TSS is indicated by an arrow. The signal range (y-axis) is 0 to 100 for all regions apart from region 2 in the high confidence dataset ((PDF)Click here for additional data file.Figure S3EGR2 – positive control, ABLIM2, CPLX1, SRFint3 – negative controls. (B) The effect of depletion of ELK1 on mRNA levels of the indicated actin cytoskeleton- and migration-associated genes. RT-qPCR analysis of the mRNA levels of the indicated genes was carried out in serum starved MDA-MB-231 cells transfected with siELK1, with normalisation to an siGAPDH-transfected control; bars show average values from three biological repeats with standard deviations. Levels of ELK1 mRNA indicate the efficiency of depletion and RBL2 and EFR3A are control genes not associated with ELK1 Binding regions; * P<0.05, ** P<0.01 (Student's paired t-test). (C) Representative images of wounds created in monolayers of siGAPDH- or siELK1-transfected MDA-MB-231 cells, 18 hours post-stimulation with media containing 10% FBS. Lines show borders of areas which were used for quantification. (D) Areas of wounds in MDA-MB-231 cells treated as in (C) were measured in duplicates and normalised to control (siGAPDH). Bars show the average of three biological repeats with standard deviation. P-value was obtained from a two-tailed paired Student's t-test. (E) Numbers of MDA-MB-231 cells in a siELK1-transfected population were counted at 48 and 96 hours post-initial transfection and normalised to control (siGAPDH). Bars show average values of three biological repeats (performed in duplicates) with standard deviations. * P<0.05 (Student's paired t-test).Role of ELK1 in MDA-MB-231 cells. (A) ChIP-qPCR experiments were carried out in MDA-MB-231 cells for regions bound by ELK1 in MCF10A cells (see (PDF)Click here for additional data file.Figure S4Overlaps between ELK1 binding regions in MCF10A cells and HeLa cells. (A) Overlap between regions identified by ChIP-seq as bound by ELK1 in MCF10A cells, and by ChIP-chip as bound by ELK1 in HeLa cells (PDF)Click here for additional data file.Figure S5Gene Ontology analysis of regions bound by ELK1. GREAT Gene Ontology analysis was carried out for all regions from the high confidence dataset using default settings. Significantly enriched categories are shown. Categories associated with the actin cytoskeleton and migration are indicated by arrows.(PDF)Click here for additional data file.Figure S6Depletion of ELK1 changes the expression of distinct functional categories of genes. (A) DAVID functional clustering analysis was carried out for genes assigned to ELK1 ChIP-seq regions which show a significant change in expression levels in MCF10A cells depleted of ELK1 (as compared to an siGAPDH-transfected control), at either time point of EGF stimulation. Functional assignments of the top ten clusters are shown. GO terms were classified as gene expression-related (grey), actin cytoskeleton/migration-related (green) and cell survival related (orange). (B) DAVID functional clustering results for genes upregulated upon ELK1 depletion; the top five clusters are shown. (C) As in (B), but for downregulated genes.(PDF)Click here for additional data file.Figure S7WNK1, ITGAV, PAPLN) and “redundant” (CAP1) regions was determined in MCF10A cells two hours post-EGF stimulation in ChIP-qPCR assays. Numbers above bars indicate fold enrichment of FOS signal over IgG. KLF9 – positive control.Overlaps between ELK1 binding regions and binding of other transcription factors; FOS/AP1 binding associates with “unique” ELK1-bound regions. (A) Overlap between regions identified by ChIP-seq as bound by ELK1 (MCF10A cells) and ELK4 (HeLa cells) (PDF)Click here for additional data file.Figure S8in vivo and in vitro. TFBS logos obtained through STAMP-assisted visualisation of Weeder-derived position weight matrices of motifs overrepresented in “unique” and “redundant” ELK1-bound regions from the ChIP-seq experiment in MCF10A cells (see in vitro site-selection study Comparison of ELK1 binding motifs found ells see . For com(PDF)Click here for additional data file.Figure S9“Unique” and “redundant” ELK1-bound regions regulate distinct sets of target genes. Summary profiles of the target genes in clusters 1, 3, 5 and 6 see . The dat(PDF)Click here for additional data file.Figure S10ELK1, GABPA and SRF mRNAs indicate the efficiency of depletion; RBL2, EFR3A are negative controls which do not associate with ELK1. Significantly altered expression is depicted with orange bars; * P<0.05, ** P<0.01 (Student's paired t-test).The role of ELK1, GABPA and SRF in regulating “unique” ELK1 target genes. (A–C) mRNA levels of nine actin cytoskeleton- and migration-associated genes were measured by RT-qPCR from MCF10A cells grown in the absence of EGF (B and C) or additionally treated with EGF for 30 mins (A) and transfected with siRNAs against ELK1 (A), GABPA (B) or SRF (C); these were then normalised to an siGAPDH-transfected control (taken as 1). Bars show average values from two to three biological repeats with standard deviations. Levels of (PDF)Click here for additional data file.Figure S11PFN1 is associated with a redundant region). Pink circles indicate that gene expression is changed upon ELK1 depletion. Circles to the right of the figure show ELK1 target genes attributed to these functional terms but which do not have catalogued connections to the other genes in the network.ELK1 controls a network of actin/migration-related genes. Network formed by proteins encoded by actin cytoskeleton/migration-related genes associated with “unique” ELK1-bound regions (each protein denoted by a circle). Asterisks mark genes tested in (PDF)Click here for additional data file.Figure S12ELK1 controls a network of survival-related genes. Networks formed by proteins encoded by survival-related genes associated with “unique” or “redundant” ELK1-bound regions (each protein denoted by a circle). Asterisks mark genes tested in (PDF)Click here for additional data file.Figure S13ELK1 controls a network of gene expression-related genes. Networks formed by proteins encoded by gene expression-related genes associated with “unique” (A) and “redundant” (B) ELK1-bound regions. Asterisks mark genes tested in (PDF)Click here for additional data file.Figure S14Depletion of ELK1 impairs MCF10A cell survival. The numbers of MCF10A cells were determined 48 h and 96 h post-initial treatement with ELK1 siRNA and normalised to siGAPDH-transfected control. Cells were stimulated with EGF at t = 48 h (together with the second transfection). The experiment was performed in duplicate; average values and standard deviations from three biological repeats are shown. Significance was determined in a Student's paired t-test, * P<0.05.(PDF)Click here for additional data file.Figure S15ELK1-regulated genes control the actin cytoskeleton and motility of MCF10A cells. (A) Depletion levels of each of the indicated mRNA species following siRNA treatment were determined in RT-qPCR reactions. Bars show average values from three biological repeats with standard deviations, and are shown relative to cells treated with siGAPDH (taken as 1). (B) Representative images of MCF10A cells transfected with the indicated siRNA species, starved for EGF for 48 h and subsequently stimulated with EGF for 24 h and stained with phalloidin (green) and the Hoechst dye (blue). (C) The percentage of cells exhibiting membrane protrusions was calculated for each of the indicated siRNA transfections, as well as for siGAPDH (negative) and siELK1 (positive) – transfected controls. Bars show average values from three biological repeats with standard deviations; three fields were scored for each repeat. (D) Representative images of wounds created in monolayers of MCF10A cells transfected with the indicated siRNAs, are shown 15 hours post-stimulation with EGF . (E) Areas of wounds in MCF10A cells treated as in (D) were measured in duplicates and normalised to control (siGAPDH). Bars show average values of three biological repeats with standard deviations. P-values were calculated in Student's paired t-tests, * P<0.05(PDF)Click here for additional data file.Figure S16Depletion of ELK1 target genes impairs MCF10A cell migration. Migratory trajectories of MCF10A cells transfected with the indicated mRNA species, manually tracked between t = 1 h and t = 7 h of EGF stimulation. Each coloured line represents the path travelled by an individual cell.(PDF)Click here for additional data file.Table S1Microarray analysis of gene expression changes upon EGF treatment and/or depletion of ELK1 in MCF10A cells. Data are shown for (A) all the genes which show significant changes in expression upon EGF treatment and/or depletion of ELK1 and (B) genes which show significant changes in expression upon EGF treatment and/or depletion of ELK1 and are also associated with ELK1 binding regions. Only probesets with Q values lower than 0.1 and P values lower than 0.05 were retained, and only one probeset per gene was considered, see (XLSX)Click here for additional data file.Table S2ChIP-seq analysis of ELK1 binding regions in MCF10A cells. Data are shown for (A) high confidence binding regions which have a FDR<10% and appear in two independent experiments and (B) lower confidence binding regions which have a FDR<10% but appear in only one experiment. Genomic coordinates, summits, tag numbers and significance values are shown for each region from the experiment (repeat 2) which gave peaks with more stringent P-values.(XLSX)Click here for additional data file.Table S3Z-transformed mRNA expression data for clustering analysis. The mRNA expression changes for genes associated with ELK1-bound regions that show a significant response to siELK1 and/or EGF treatment are shown as z-transformed data for each of four different experimental conditions, i.e. plus/minus siELK1 treatment, plus/minus EGF treatment for 30 mins. This data was used for generating the k-means clustering of expression levels shown in (XLSX)Click here for additional data file.Table S4“Unique” and “redundant” ELK1-bound genes associate with distinct sets of GO terms. DAVID Gene Ontology was performed on lists of genes associated with “unique” or “redundant” ELK1-bound regions that changed expression upon depletion of ELK1. Lists were sorted with ascending P-values of individual terms. These data are shown as a heatmap in (XLSX)Click here for additional data file.Table S5List of PCR primers used in (A) ChIP and (B) RT-PCR experiments. Nucleotide sequences are given in both cases, and the chromosomal locations of the primers used for ChIP (according to the hg18 genome assembly).(PDF)Click here for additional data file.


Aspergillus parasiticus. Electrophoretic mobility shift assays demonstrated that AtfB, a basic leucine zipper (bZIP) transcription factor, is a master co-regulator that binds promoters of early (fas-1), middle (ver-1), and late (omtA) aflatoxin biosynthetic genes as well as stress-response genes (mycelia-specific cat1 and mitochondria-specific Mn sod) at cAMP response element motifs. A novel conserved motif 5′-T/GNT/CAAG CCNNG/AA/GC/ANT/C-3′ was identified in promoters of the aflatoxin biosynthetic and stress-response genes. A search for transcription factors identified SrrA as a transcription factor that could bind to the motif. Moreover, we also identified a STRE motif (5′-CCCCT-3′) in promoters of aflatoxin biosynthetic and stress-response genes, and competition EMSA suggested that MsnA binds to this motif. Our study for the first time provides strong evidence to suggest that at least four transcription factors  participate in a regulatory network that induces aflatoxin biosynthesis as part of the cellular response to oxidative stress in A. parasiticus.The mycotoxin aflatoxin is a secondary metabolite and potent human carcinogen. We investigated one mechanism that links stress response with coordinate activation of genes involved in aflatoxin biosynthesis in  Reactive oxygen species (ROS) is produced either by exposure to environmental agents such as UV and nutrient starvation or endogenously as by-products of metabolic pathways in different cellular compartments   transcription factor Atf1, a member of the ATF/cAMP response element binding (CREB) protein DNA-binding family, is activated by the stress-activated MAPK Sty1/Spc1 through a MAPK cascade. Atf1 then positively regulates entry of the cell into stationary growth phase  binds to a cAMP-response element consensus sequence (5′-TG/TACGTC/AA-3′) in promoters of stress-response genes -signaling cascades, resulting in induction of defense gene expression  in their promoters. Specific AtfB binding to these promoters was not demonstrated.Filamentous fungi use similar stress-response signaling pathways to those in yeast  in response to oxidative stress are not fully understood. However, our recent studies shed light on the regulatory mechanisms that integrate secondary metabolism and cellular response to oxidative stress  and secondary metabolism genes  as a mean of cellular response to oxidative stress in A. parasiticus. We demonstrate using EMSA that the stress-related transcription factor AtfB binds to the promoters of early, middle, and late aflatoxin biosynthetic genes as well as stress-response genes that carry at least one CRE motif, under aflatoxin-inducing conditions, but AtfB does not bind to the vbs promoter, which lacks a CRE site under the same growth condition. Based on nucleotide sequence and EMSA analyses, we also propose that three other transcription factors, MsnA, SrrA, and AP-1, participate in transcriptional co-regulation of stress-response and aflatoxin biosynthetic genes. Furthermore, we found a conserved motif 5′-T/GNT/CAAGCCNNG/AA/GC/ANT/C-3′ in the promoters of fas-1, ver-1, nor-1, mycelial cat1, and Mn sod, which extends the conserved 8-base motif 5′-AGCCG/CTG/CA/G-3′ identified in the promoter of the nor-1 in our previous study; the core consensus sequence 5′-AAGCC-3′ in the conserved motif is a sequence-specific-binding motif for the transcription factor SrrA. We also found a motif (5′-CCCCT-3′) for a stress-response element (STRE) in the promoters of the aflatoxin biosynthetic genes, fas-1 and ver-1, as well as antioxidant genes, mycelial cat1 and Mn sod. Competition EMSA analysis suggests that MsnA, which binds to STREs, is one player in transcriptional co-regulation. These data contribute to our understanding of the molecular mechanisms of transcriptional activation of aflatoxin biosynthetic genes and stress-response genes in response to oxidative stress in A. parasiticus.In this study, we investigated the mechanism of transcriptional co-regulation of antioxidant genes (mycelia-specific Aspergillus parasiticus SU-1 (ATCC 56775) was used as a wild-type strain for aflatoxin production in this study. YES liquid medium  was used as an aflatoxin-inducing medium. A 100 mL of YES medium in a 250-mL flask with five 6-mm glass beads was inoculated with 1 × 104 spores mL−1 and incubated at 30°C in the dark with shaking at 150 rpm as described previously  using a partial sequence of A. parasiticus AP-1  at +4°C or −80°C.Aspergillus parasiticus SU-1 was grown in YES medium for designated periods of time and mycelia were harvested by filtration through Miracloth. Mycelia were frozen in liquid nitrogen. Frozen mycelia were ground in liquid nitrogen with a mortar and pestle, and resuspended at a 1:1 (w/v) ratio in TSA buffer  containing one tablet of Complete mini protease inhibitor mixture , 50 μL of proteinase inhibitor mixture , and 0.125 mmol/L phenylmethylsulfonyl fluoride per 10 mL. Total protein extracts (90 μg per lane) or enriched nuclear protein extracts (200 μg per lane) were separated by electrophoresis on 12% SDS-polyacrylamide gels, transferred onto polyvinylidene difluoride (PVDF) membranes , and probed with anti-AP-1 antibodies  as a primary antibody. The membranes were incubated with goat anti-rabbit secondary antibody conjugated to the fluorescent tag IRDye 680 . Protein bands were detected using an Odyssey infrared imaging system (Li-Cor Biosciences) at 795 nm.A. parasiticus SU-1 for EMSA was performed as described previously  and incubated for 24, 48, or 60 h at 30°C with shaking at 150 rpm. The mycelia were harvested by filtration through Miracloth, ground in liquid nitrogen using a mortar and pestle, and resuspended in lysis buffer as described previously. The proteins were precipitated by ammonium sulfate (10% and then 70%), pelleted by centrifugation, resuspended in dialysis buffer, and dialyzed against the dialysis buffer. The dialyzed protein solution was aliquated and stored at −80°C.Preparation of cell extracts enriched for nuclear proteins from fas-1, ver-1, omtA, vbs, mycelial cat1, and Mn sod were generated by PCR and gel purified using a Wizard SV gel and PCR cleanup system . Primers used to generate PCR fragments are shown in 32P]ATP using USB OptiKinase , purified using a Micro Bio-Spin P-30 chromatography column , and used as probes. Two alternative methods were used to generate double-stranded DNA competitor fragments: (1) A 51 bp NorR4 fragment was generated by PCR using pUC19GUS as a template and gel purified using a Wizard SV gel and PCR cleanup system (Promega) , 7.5 μg of bovine serum albumin, and competitor (if desired) with 5 μg of nuclear protein extract (added last) in a DNA-binding buffer  in 25 μL of final binding reaction. After separation, the gels were dried and exposed to x-ray film.Electrophoresis on 5% nondenaturing polyacrylamide (80:1 acrylamide/bisacrylamide) gels was used to separate DNA–protein complexes. Twenty femtomole of each −1) or preimmune serum obtained from the same rabbit as the antibody was generated. Then, a 32P-labeled probe was added and the mixture was incubated for an additional 15 min at 30°C. Finally, DNA–protein complexes were resolved by electrophoresis.Polyclonal anti-AtfB antibodies (YSR) were used to block formation of DNA–protein complexes (shift inhibition EMSA) as described previously  as described above.fas-1, ver-1, omtA, vbs, mycelial cat1, and Mn sod was conducted at the Research Technology Support Facility (RTSF) at Michigan State University. Comparison of promoter sequences in A. parasiticus and A. flavus was performed using EMBOSS pairwise sequence alignment.DNA sequence analysis of PCR products carrying promoter regions of Aspergillus parasiticus SU-1 was cultured in YES liquid medium, and the mycelia were harvested and frozen in liquid N2 at 24, 48, or 60 h. Total RNA was isolated from two biological replicates by the TRIzol method . Total RNA was treated with RNase-free DNase  and RNA quality was examined by an Agilent 2100 Bioanalyzer . For reverse transcription, 1 μg of total RNA was treated with gDNA WipeOut, and cDNA was prepared with the QuantiTect reverse transcription kit (Qiagen). Primers were designed using Primer Express software 3.0  and PrimerQuestSM . Primers used for quantitative real-time PCR (qRT-PCR) analysis are listed in n = 4, and were analyzed by one-way repeated measure analysis of variance.Transcript levels in fungal cells were analyzed using real-time PCR as described previously , the norA/ver-1 genes , the divergently transcribed omtA/ordA genes , the mycelial cat1 gene (577 bp upstream from ATG), and the Mn sod gene (455 bp upstream from ATG) (fas-2/fas-1 intergenic region was divided into Fas2 (298 bp) and Fas1 (399 bp). Fas1 was further split into Fas1-b (230 bp) and Fas1-a (189 bp). The norA/ver-1 intergenic region was divided into Ver1-b (311 bp) and Ver1-a (499 bp). The omtA/ordA intergenic region was divided into OmtA (596 bp) and OrdA (692 bp). The Mcat1 fragment (577 bp upstream from ATG) was divided into Mcat1-b (339 bp) and Mcat1-a (259 bp) (Preliminary EMSA studies detected DNA–protein complex formation between EMSA protein extracts from rom ATG) . In orde(259 bp) . FragmenA. parasiticus grown in YES aflatoxin-inducing medium for 24 h (prior to aflatoxin gene expression), 48 h , or 60 h  , the most abundant DNA–protein complexes were observed using the 24-h nuclear protein extract while DNA–protein complexes were observed at significantly reduced levels and some complexes disappeared using 48- or 60-h nuclear protein extracts  and it competed efficiently for complex formation with a 170-bp NorR promoter fragment using a 48-h nuclear protein extract  and middle (ver-1) aflatoxin genes as well as stress-response genes  carry similar CRE sites, AP-1 sites, or both sites as observed in the nor-1 promoter.To locate specific  extract A and B. fas-1 and nor-1 using ChIP analyses; the data suggested that the conserved 8-base motif may contribute to AtfB binding  and ver-1 (which does not contain the motif) promoters using competition EMSA. A 28-bp Fas fragment containing the 8-base motif, which was derived from the Fas1-b promoter fragment  within promoters of eight aflatoxin genes by sequence-based MEME  motif analysis and showed AtfB binding to five of these eight promoters including fragment B, competfragment C. The danor-1 promoter using EMSA protein extracts from A. parasiticus grown for 48 h in YES aflatoxin-inducing medium and antibodies that specifically recognize AtfB , which lacks CRE sites , middle (ver-1), and late (omtA) aflatoxin biosynthetic gene promoters that carry CRE sites.cat1 and Mn sod promoters. A Mcat1-a-protein complex was observed in EMSA using 24-, 48-, or 60-h nuclear protein extracts –protein complex was detected by EMSA using a 24-, 48-, or 60-h nuclear protein extracts . To demonstrate antibody specificity, we conducted Western blot analysis of the A. parasiticus nuclear protein extract. AP-1 antibodies detected a 64-kDa protein in the A. parasiticus nuclear protein extract (A. flavus (GenBank accession no: EED48711.1). AP-1 antibody pretreatment of the nuclear protein extracts did not affect DNA–protein complex formation within the Fas1-a, Ver-1b, Mcat1-a, or Msod promoter fragments (As previous data suggested that an AP-1 site (5′-TGAGTAC-3′) played a role in activation of the  extract , and thiragments  suggestinor-1 promoter in previous work . We observed that the AP-1 and SrrA motifs are located in close proximity to each other, lying within 60 bp in the promoters of aflatoxin (fas-1 and ver-1) and stress-response genes   . Our fin2H2-type zinc-finger regulators) bind to a conserved STRE (5′-CCCCT-3′) in promoters of stress-response genes  in response to oxidative stress (H2O2) and activate their expression in S. cerevisiae   C to compGTCA-3′) A–C. HoweGTCA-3′) D. To ideGTCA-3′) C. EMSA uGTCA-3′) . These dcat1 described above  and minor DNA–protein complexes . The inted above B. Interecat1 at 24 h in order to activate gene expression in response to oxidative stress (H2O2). However, although MsnA binds to promoters of aflatoxin genes such as fas-1 and ver-1 at 24 h, it is unable to activate these genes , antioxidant genes , and oxidative stress-related transcription factor genes  in A. parasiticus SU-1 grown in aflatoxin-inducing YES medium at 24, 48, or 60 h using real-time PCR. Transcripts of fas-1, ver-1, and Mn sod were barely detected at 24 h but increased significantly at 48 h and stayed unchanged at 60 h  were all expressed at relatively high levels at 24 h and remained unchanged at 48 and 60 h (fas-1 and ver-1) and Mn sod is regulated by a similar mechanism, whereas expression of mycelial cat1 and the oxidative stress-related transcription factor genes  is regulated by a different mechanism.We analyzed levels of transcripts of early and middle aflatoxin biosynthetic genes ( at 60 h . In contand 60 h C and E–GA. flavus genome). The aflatoxin gene cluster that carries fas-1 and ver-1 is located on chromosome 3, the antioxidant gene, mycelial cat1, is located on chromosome 5, and Mn sod is located on chromosome 2. Nucleotide sequence analysis demonstrated that the promoters of mycelial cat1 and Mn sod in A. parasiticus were highly conserved with each other and also showed 90% identity to the corresponding promoter regions in A. flavus  forms a heterodimer with Jun (a homolog of Yap1 in S. cerevisiae) or it forms a homodimer at CRE sites while ATF-1 (a homolog of AtfB in A. nidulans) binds as a homodimer in mammalian cells  contributes to ability of AtfB to bind the CRE site. Based on the data presented in sod in A. parasiticus is activated by AtfB later than mycelial cat1, in agreement with findings by others  via association with Skn7 (Prr1 ortholog) in response to H2O2  to activate mycelial cat1 and aflatoxin biosynthetic genes. In support of this model, promoters of the aflatoxin and antioxidant genes  located in close proximity to each other and within 500 bp of the ATG translation initiation codon. Each of the five promoter regions contained 3–6 AtfB recognition sites, one SrrA site, one or two MsnA sites, and multiple AP-1 sites. The relative position of these specific cis-acting elements was similar but unique in each promoter (fas-1 and Mn sod); in four of five promoters analyzed, an MsnA site was detected adjacent (within 100 bp) to ATG (the exception was the nor-1 promoter).The temporal formation of DNA–protein complex in the Mn promoter A. In thrnor-1 and ver-1 promoters . The Mn sod promoter contained two such motifs (TCGTAGGTGGA and TCGTGCGCCAA), and the fas-2/fas-1 intergenic region possessed one motif TCGAACACCTA. Currently, the functional significance of these motifs is unknown. Further analyses are required to determine the binding specificity of AtfB to these promoters in vitro and in vivo, and also how the landscape of transcription factor recognition sites affects interaction of AtfB with SrrA, AP-1, MsnA, and AflR, its interaction with the promoter, and the transcriptional impact during the response to oxidative stress.We previously proposed that AtfB aids AflR binding to its recognition site in the promoter  (Reverberi et al. cat1 is activated by MsnA binding at a STRE site and AtfB binding at CRE sites in the promoter at 24 h. Based on this observation, we propose that antioxidant genes including mycelial cat1 are activated as the first line of defense against ROS formation, and aflatoxin genes are activated at a later time point as the second line of defense to excessive levels of ROS in agreement with findings presented by Reverberi et al. (Aflatoxin biosynthesis is triggered as a part of the cellular response to oxidative stress. What is the role of aflatoxin biosynthesis in this response? Does it protect the cell from oxidative stress? In plants and fungi, secondary metabolites play a protective role in adaptation to various biotic and abiotic stressors in the environment. In particular, plants produce many compounds such as phenylamides, polyphenol, and flavonoids including anthocyanin and quercetin, which have ROS-scavenging properties, for their defense (Edreva et al. i et al. .Our data obtained in this study and the observations of others (Toone and Jones In conclusion, in fungi, cellular response to oxidative stress relies on a complex and dynamic network of transcription factors that bind in a highly coordinated manner to the promoters of the target genes and regulate their expression. Understanding the mechanisms that underlie the temporal regulation of a transcription factor binding to its recognition sites in the promoter is one key to establishing the link between intracellular ROS formation, cellular antioxidant defense, and the benefits for survival offered by secondary metabolism.


Genome-wide maps of DNase I hypersensitive sites (DHSs) reveal that most human promoters contain perpetually active cis-regulatory elements between −150 bp and +50 bp (−150/+50 bp) relative to the transcription start site (TSS). Transcription factors (TFs) recruit cofactors  to these elements in order to organize the local chromatin structure and coordinate the balance of post-translational modifications nearby, contributing to the overall regulation of transcription. However, the rules of TF-mediated cofactor recruitment to the −150/+50 bp promoter regions remain poorly understood. Here, we provide evidence for a general model in which a series of cis-regulatory elements  prefer acting individually, rather than in fixed combinations, within the −150/+50 bp regions to recruit TFs that dictate cofactor signatures distinctive of specific promoter subsets. Subsequently, human promoters can be subclassified based on the presence of cardinal elements and their associated cofactor signatures. In this study, furthermore, we have focused on promoters containing the nuclear respiratory factor 1 (NRF1) motif as the cardinal cis-regulatory element and have identified the pervasive association of NRF1 with the cofactor lysine-specific demethylase 1 (LSD1/KDM1A). This signature might be distinctive of promoters regulating nuclear-encoded mitochondrial and other particular genes in at least some cells. Together, we propose that decoding a signature-based, expanded model of control at proximal promoter regions should lead to a better understanding of coordinated regulation of gene transcription. Human cells exploit different mechanisms to coordinate the expression of both protein-coding and non-coding RNAs. Elucidating these mechanisms is essential to understanding normal physiology and disease. In our attempt to identify new regulatory layers acting particularly at proximal promoters, we have computationally analyzed the genomic sequences located from −150 bp to +50 bp relative to the transcriptional start site (TSS), which are often at the center of ‘open’ chromatin regions in human promoters. We have confirmed the presence of a series of cis-regulatory elements  that show a strong preference for these short regions. Interestingly, these elements tend to act independently rather than in fixed combinations. Therefore, we propose that they confer unique regulatory features to the human promoter subsets that contain each of these particular elements. In agreement with this model, we have identified a large repertoire of preferential partnerships between transcription factors recognizing cardinal motifs and their associated proteins (cofactors), thus decoding a signature-based model that distinguishes distinctive regulatory types of promoters based on cardinal motifs. These signatures may underlie a new layer of transcriptional regulation to orchestrate coordinated gene expression in human promoters. DNase I hypersensitive sites (DHSs) mark ‘open’ chromatin regions in the human genome The genome-wide profiling of transcription factors (TFs) and cofactors  has recently provided valuable information that may change our understanding of how chromatin organization is established in promoters. Proximal promoters have been traditionally viewed as the main targets of TFs in the human genome; however, most TF binding profiles consistently reveal preferential binding to distal, rather than proximal, genomic sites e.g. . In an aHere, we have analyzed 21,000 human promoters from −150 bp to +50 bp relative to the TSS to investigate the role of cis-regulatory elements and their cognate TFs in recruiting histone/protein-modifying activities, particularly KDMs, to these sites. Co-occurrence analysis of the most highly enriched of these elements  confirms that they tend to occupy these regions in patterns that are independent from one another, thus suggesting the existence of promoter subclasses based on the independent presence of these motifs. To validate this model, we profiled NRF1 and subunit B of NFY (NFYB), which constitutively recognize two of the most abundant cardinal elements, NRF1 and NFY/CCAAT. Our data confirmed that both ‘cardinal TFs’  occupy two largely independent promoter subsets. Furthermore, we screened for KDM activities that may selectively act via one TF but not the other, finding that LSD1 acts as a specific and pervasive cofactor of NRF1. We further explored the binding profiles of approximately 60 other cofactors reported in the literature, which resulted in the identification of other strong cardinal motif-cofactor signatures. Together, we propose that an important function of cardinal cis-regulatory elements at promoter DHSs is to dictate a selective regulatory code of histone/protein-modifying activities and other cofactors that distinguishes promoter subclasses. Intriguingly, each subclass shows qualitative and quantitative differences with regard to the type and number of cofactors recruited, thus suggesting that there is a complex regulatory layer depending on the presence of cardinal elements that might contribute to the chromatin organization and regulation of DHS promoters.de novo motif discovery analysis. This analysis resulted in the identification of nine highly enriched motifs, which we defined as ‘cardinal’ cis-regulatory elements. As expected, these nine elements included the TATA-box, as well as sequences recognized by well know ubiquitous TFs common in promoters: Sp1/GC-rich, NFY/CCAAT-box, ETS/GABP/NRF2, NRF1, CREB/CRE-MYC/E-box, and YY1 . In addition, we performed co-occurrence analysis using experimentally defined promoter DHSs in breast cancer MCF7 cells and derived essentially the same conclusion, although positive co-occurrences between different cardinal motifs were even less significant .To guide the discovery of new regulatory mechanisms acting via core/proximal promoter regions, we analyzed −150/+50 bp regions (relative to the TSS) based on previous studies showing that these genomic coordinates overlap with the center of DNase I hypersensitivity in active human promoters  and S1B). ChIP-seq analysis in MCF7 cells revealed 1,264 and 1,522 high confidence NRF1 and NFYB peaks, respectively (Supplementary Tables S1 and S2), and as expected, these peaks were found preferentially at promoter regions , particularly within −150/+50 bp regions . We also performed analyses of ChIP-seq datasets available in the literature  and established a similar relationship between the set of predicted motifs, the actual TF peaks, experimentally defined DHSs, and the profiles of histone marks H3K4me2 and H3K4me3 . Importantly, -returning to the case of NRF1 and NFYB- both TFs rarely co-localized at −150/+50 bp , thus confirming their apparent binding antagonism in promoters across the human genome.To test the predictive value of these analyses we focused on motifs other than TATA-box and Sp1/GC-rich because these two are well documented in the literature. Thus we performed chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq) using antibodies that recognize nuclear respiratory factor 1 , which binds as a homodimer to the NRF1 site Figure S2B). Using the most stringent analysis (p<0.0001), the ChIP-DSL assay identified large subsets of NRF1 and NFYB positive promoters that were not identified by ChIP-seq , which were also highly enriched in NRF1 or NFY/CCAAT motifs. However, consistent with our ChIP-seq analyses, they showed relatively poor NRF1 and NFYB co-localization . The only exception to this observation was a small subset of NRF1 and NFYB co-occupied promoters , in which NRF1 and NFY/CCAAT motifs also co-occurred with abnormal high significance . Overall, therefore, our ChIP-seq and ChIP-DSL results with NRF1 and NFYB confirm the predictive value of our computational analysis, which suggests that cardinal motifs (and their cognate TFs) tend to be present independently rather than in fixed pairs within these regions. In fact, combined analysis of the NRF1 and NFYB ChIP-DSL datasets showed that almost 35% of all human promoters tested on the Hu20K array contain one or the other TF, although they still poorly coincide. We also performed gene ontology (GO) analysis of genes associated with −150/+50 bp NRF1 and NFYB ChIP-seq peaks to associate them with biological functions. As expected, based on the known functions of NRF1, genes associated with this TF were linked to RNA processing and metabolism, translation, mitochondria, and intracellular transport of proteins . Therefore, the tendency to occupy different promoters may also be associated with their specific biological functions.Since NRF1 and NFYB occupy only 25% and 18% of their respective predicted sites at −150/+50 bp regions , we could not exclude the possibility that their poor co-localization may be a result of technical limitations associated with the ChIP-seq assay. To assess this possibility, we alternatively assessed genomic binding of NRF1 and NFYB using the highly sensitive ChIP-DSL assay Luc) or three canonical NFY sites (3×NFY-Luc) in HEK293T cells . By performing these comparisons, we identified 13 siRNA treatments that had selective influences  on luciferase transcription depending on whether the motifs were NRF1 or NFY (summarized in KDM5C siRNA) had significant effects on both 3×NRF1- and 3×NFY-dependent transcriptional units when compared to siRNA control, but with opposite effect on each reporter. Three other siRNAs . Overall, 8 siRNA treatments induced down-regulation of gene expression  recruitment of cofactors via TFs. To test this hypothesis, we focused on KDMs because these histone-modifying enzymes have been repeatedly shown to bind preferentially to promoters in genome-wide tests LSD1 siRNA alters NRF1- but not NFY-motif mediated transcription . Surprisingly, this reveals that LSD1 is a genuine promoter DHS-specific factor, similar to NRF1 and NFYB , as well as a high correlation of LSD1 binding with NRF1 on a genome-wide scale . In the context of a third ‘cardinal’ TF, Sp1, we also observed a strong associative preference for LSD1 contingent on co-localization with NRF1 in a limited analysis of ∼2,000 human promoters . Not surprisingly, de novo motif discovery analysis of LSD1 ChIP-seq peaks revealed overwhelming enrichment of NRF1 sites . We also observed enrichment of the estrogen responsive element (ERE) in agreement with our own previous studies showing that estrogen receptor alpha (ERα) recruits LSD1 to ERα-regulated regions via EREs Figure S4B and S4C). In contrast, the co-association between NRF1 and LSD1 was characteristic of promoter (or H3K4me3-positive) regions .Because the results just described could be either the result of direct or indirect effects, we capitalized on our specific result revealing that Figure S5A). U2OS cells showed almost identical motif enrichment to that observed in MCF7 cells , and more than 80% of the LSD1-positive promoters in these cells were also LSD1-positive in MCF7 cells. Based on this finding, we included U2OS cells in some of the experiments reported later in this study. To confirm the high binding coincidence of LSD1 in U2OS and MCF7 cells, we performed standard ChIP analysis on random targets , and on a few classic NRF1-regulated promoters , and observed almost identical LSD1 binding patterns in both cell lines. The LSD1 binding program has also been recently reported in mouse embryonic stem cells (mESCs) Figures S5D and S6). Taken together, our results show that the strong co-association of NRF1 motifs, NRF1 TF, and the LSD1 cofactor at −150/+50 bp regions can be observed in different cell lines and organisms, which supports a model in which cis-regulatory elements in these regions dictate strong and common cofactor signatures.Next, we examined whether strong NRF1 and LSD1 co-association is a cell type-specific feature. We performed LSD1 ChIP-DSL analyses in human mammary epithelial (HMEC), prostate cancer (LNCaP), osteosarcoma (U2OS), and neuroblastoma (SH-SY5Y) cells. Our results showed significant NRF1 motif enrichment in LSD1 peaks in all four examined cell lines, although the levels of enrichment were slightly different among them ( and S7) and on a genome-wide scale  and three new NRF1/LSD1 targets uncovered in this study . For these five genes, both LSD1 and NRF1 knockdown resulted in decreased expression when compared to control siRNA, suggesting that LSD1 is in fact a coactivator of NRF1-mediated transcription , or 22% and 47% of all NRF1 and LSD1 siRNA-affected genes, respectively  are bona fide NRF1 motif-containing promoters , which are two components of the trimer that constitutes NFY, thus potentially affecting NFY/CCAAT motif-containing promoters indirectly.The almost pervasive association of NRF1 with LSD1 at −150/+50 bp regions does not directly imply a functional relationship between them or, if such relationship exists, that it is functionally universal at every single promoter. In fact, any functional relationship between these two factors may be complex because LSD1 may act as either a coactivator or corepressor of transcription, depending on the context of its binding Class I included genes down-regulated by both siRNA treatments (n = 193); Class II included genes up-regulated by both siRNAs (n = 272); Class III included genes down-regulated by NRF1 siRNA, but up-regulated by LSD1 siRNA treatment (n = 35); and Class IV included genes up-regulated by NRF1 siRNA, but down-regulated by LSD1 siRNA . A ratio greater than one (>1) with respect to background might be associated with a higher frequency of direct effects mediated by the siRNA treatment, while a ratio lower than one (<1) might be associated with a higher frequency of indirect effects . Following our analysis, we observed that genes down-regulated by both NRF1 and LSD1 siRNAs tend to show a higher frequency of NRF1 and LSD1 binding at their promoters than background (Class I). In contrast, genes up-regulated by both siRNA treatments tend to show a higher frequency of LSD1, but not NRF1, binding at their promoters (Class II). The ratio obtained for genes down-regulated by NRF1 siRNA and up-regulated by LSD1 siRNA suggests a general enrichment in direct NRF1, but indirect LSD1 effects (Class III), while the ratio obtained for genes up-regulated by NRF1 siRNA and down-regulated by LSD1 siRNA suggests that both NRF1 and LSD1 affect these genes indirectly (Class IV). These results are consistent with the current view that NRF1 binds to promoters to activate gene transcription , and that LSD1 either acts as a co-activator (Classes I and V) or a co-repressor of transcription (Classes II and VI). The only classes in which both NRF1 and LSD1 show a tendency to co-bind are I, V, VI, and VII, thus suggesting that in the context of the whole human genome, LSD1 may act as a NRF1 coactivator in some cases (Class I), or remain inactive in others (Class VII), at least under the experimental conditions that we tested. Class VI represents an interesting case in which LSD1 may inhibit the NRF1 activity, thus LSD1 knockdown impairs the negative effect on NRF1-mediated activation, but NRF1 knockdown per se has no effect on gene expression under already the condition of LSD1-mediated NRF1 inhibition. More difficult to explain are the Class V promoters. In this case, it is possible that other TFs (besides NRF1) may recruit LSD1 to these promoters, even if NRF1 is present, thus resulting in LSD1-dependent genes that are associated with, yet functionally independent of NRF1. Overall, this functional analysis suggests that even if a motif/TF/cofactor signature is pervasive in promoters across the genome, the functional relevance could be rather complex, thus emphasizing that these signatures should not be interpreted as representative of universal functional outcomes.Our genome-wide analysis supports the idea that NRF1 and LSD1 co-regulate gene transcription, but to establish the homogeneity or heterogeneity of this functional partnership we classified the full set of genes altered by both siRNA treatments into four classes: KDM5C siRNA treatment altered the expression of the 3×NRF1-luciferase construct in our screen . According to our model, they should show at least two main  basic patterns of KDM siRNA-mediated effects as result of their cardinal NRF1 or NFY motif composition. In fact, we observed that the global effects of the KDM siRNA treatments on the three NRF1-driven promoters clustered together, while the global effects of the same treatments on the three NFY-driven promoters did the same . Analyses of the mixed promoters  resulted in two of the promoters clearly clustering with NRF1-driven promoters, while the third promoter still clustered with them, but less clearly . Thus these results (based on a limited test) suggest that cofactor signatures at promoters that contain a combination of cardinal motifs resemble those of promoters with single cardinal motifs, instead of representing a mix of effects induced by the individual motifs, or showing a completely new regulatory signature. More in general, this test with nine constructs reinforces our proposed model that cardinal motifs are strong determinants of cofactor signatures at proximal promoter regions.To also test the model of cardinal motif-cofactor regulatory signatures experimentally, we engineered nine constructs to contain NRF1 and/or NFYB motifs in the context of the sequence of their ‘natural’ proximal promoter (−150/+50 bp), but upstream the luciferase gene, and determined the effect on this gene of depleting the same 27 KDMs that we tested in our original screen with multimerized sites . Perhaps the major differences between this report and this study are quantitative in terms of motif co-occurrences . With respect to the few qualitative differences, this previous study identified an additional motif, USF, which we did not find in our analysis; in contrast, this previous study did not include the YY1 and GFY elements, which we observe as highly enriched in our dataset . These differences might be explained in part by the facts that we used a different algorithm and that analyzed a different number of promoters and length of sequences in our tests . In any case, the model and its functional implications were not experimentally tested in this previous study, which is what we attempt here.For decades, promoters have been known to be critically important in gene transcription regulation dominance of a single cardinal motif at a single −150/+50 bp region might be a feature rather ‘exclusive’ of ‘open’ chromatin regions at promoters. Our model, however, does not exclude the possibility that cardinal motifs (via the TFs that recognize them) may act combinatorially via short- or long-range interactions in the nuclear space, or in trans, with other motifs (or TFs). It does not contradict either previous reports indicating that cardinal motifs act synergistically or cooperatively in many proximal promoter regions . But we propose that these cases do not represent the most general rule ( and S2C). Furthermore, we tested whether the preference to occupy different promoter subsets is maintained if the margins of the promoter sequence for analysis are wider  or are defined experimentally based on DHSs , achieving the same conclusion. We also tested whether this preference is maintained when proximal promoters are subclassified based on the mode of transcription initiation . Dispersed promoters show higher preference to contain ETS and NRF1 motifs . Despite these preferences, the observed co-occurrences between TATA and YY1 or ETS and NRF1 are not enriched within each group .In a way, the model that cardinal motifs (or TFs) prefer acting independently than in fixed combinations contrasts with the observation that motifs (or TFs) often operate as combinations at distal ‘open’ chromatin regions ; 3) that three copies of a single motif could dictate a complex regulatory pattern of KDMs (Based on: 1) our analysis of >60 ChIP-seq experiments revealing that each cardinal motif might be associated with the selective recruitment of a subset of cofactors  was the first KDM discovered Figures S5C, S5D and S6). Furthermore, genes in which LSD1-acts as a coactivator of NRF1 . Although similar functions were also associated by GO analysis of genes regulated  independently by NRF1 and LSD1 . In fact, it is remarkable that LSD1 occupies an impressive 1/3 of all active, nuclear-encoded mitochondrial genes in MCF7 cells . Similarly, an analysis of protein-protein interacting networks of LSD1 peaks suggests that LSD1 regulates “mitochondrial metabolism”, as well as “RNA metabolism/translation” and “cell cycle” . Previous studies already associated LSD1 to mitochondrial functions in fission yeast (S. pombe) PGC1β/PPARGC1B, PDK4, FATP1/SLC27A1, and ATGL/PNPLA2 genes in an LSD1-dependent manner Class VI; Figures S10D). In summary, the pervasive association between NRF1 and LSD1 cannot be interpreted as associated with a single or universal functional outcome, although when acting as NRF1 coactivator seems strongly associated with control of mitochondrial metabolism and biogenesis .Based on the relatively low number of cofactors we currently know that act via cardinal motifs other than Sp1/GC-rich or ETS . This result is in agreement with the finding that LSD1 is a component of the >2MDa MLL1 complex In MCF7 cells, we found that 76% of LSD1 peaks occur at or near H3K4me3-marked regions, which corresponded to epigenetically defined promoter regions  in the case of MCF7 and HMEC cells, or with 100 nM dihydrotestosterone  in the case of LNCaP cells, as previously reported Human breast cancer MCF7 cells were cultured in DMEM(1×)+GlutaMAX-I medium (Life Technologies) supplemented with 10% fetal bovine serum . Human prostate cancer LNCaP cells were cultured in Advanced DMEM/F12(1×) medium (Life Technologies) supplemented with 10% FBS. Human osteosarcoma U2OS cells, human embryonic kidney (HEK) 293T cells, and human neuroblastoma SH-SY5Y cells were cultured according to The American Type Culture Collection (ATCC) protocols. Primary normal human epithelial cells  were cultured using media and protocols provided by Lonza Bioproducts, commercial supplier of these cells. Cell lines were maintained in cell incubators at 37°C and 5% COCore promoter sequences from −150 to +50 bp relative to TSS were extracted from the UCSC genome browser by using genome assembly hg16 and mm3. 5′ RNA-seq data from MCF7 cells is available at the Database of Transcription Start Sites (DBTSS). The following were the sources of DNaseI-seq, MNase-seq, ChIP-seq, and ChIP-DSL data generated in previous studies and used here: RNA PolII ChIP-seq in E2-treated MCF7 cells reported in http://biowhat.ucsd.edu/homer/), as described in previous studies http://weblogo.berkeley.edu). Motif enrichment heatmaps and dendrograms were created by clustering Hypergeometric log P-values using open source software Cluster (http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/software.htm#ctv).De novo motif discovery analysis was performed by HOMER . The vector of motif enrichment for each experiment was then normalized and centered on the mean value to reveal the preferences of each experiment for cardinal motifs.For enrichment analysis of cardinal motifs in publically available cofactor ChIP-Seq datasets, we first identified promoter regions containing ChIP-Seq-identified cofactor peaks within 500 bp of the annotated TSS (we used peak lists provided by the authors reporting each ChIP-seq dataset). We determined the enrichment of each cardinal motif by HOMER in each set of promoters  focused and n = 5,220 (65%) dispersed promoters in these cells. We then analyzed the preference of each group to contain a specific cardinal cis-regulatory element by HOMER.FMR1-forward 5′-CCAGGCCACTTGAAGAGAGA-3′ and FMR1-reverse 5′-TGCGGGTGTAAACACTGAAAC-3′; TFAM-forward 5′-ACCGGATGTTAGCAGATTTCC-3′ and TFAM-reverse 5′-CCTCCTGGCAATACACAACTC-3′; FXR2-forward 5′-CAAGGTTAGAGCCCCAGCTA-3′ and FXR2-reverse 5′-GCGGTGAAGAAAGAAGGCTA-3′; GAPD-forward 5′-TCCTCCTGTTTCATCCAAGC-3′ and GAPD-reverse 5′-TAGTAGCCGGGCCCTACTTT-3′; and, UMODL1-forward 5′-CCTTCAGTTCCCGGGAGTA-3′ and UMODL1-reverse 5′-CTGGAAGGAAATACGTCCACA-3′. For ChIPs in plasmids, we engineered a construct with 3×NRF1 sites upstream the minimal promoter in the pGL2 plasmid  and a mutant 3×NRF1 version that contains the following fragment: 5′-TGTTTATTTTCAGacgtacgtTGTTTATTTTCAGacgtacgtTGTTTATTTTCAG-3′. Chromatin for ChIP to test plasmids was prepared as for standard ChIPs. Analysis of ChIP-seq experiments was previously described Supplementary Chromatin immunoprecipitations (ChIPs) for standard RT-qPCR analysis and ChIP-seq/DSL experiments were performed as previously described DNA, Selection, and Ligation, or ChIP-DSL) were performed and analyzed as previously described Supplementary ChIPs followed by DSL (All transient transfections (siRNAs and plasmids) were performed with Lipofectamine 2000 following the manufacturer's protocol (Invitrogen). Transfections were performed one day after seeding of cells, and the effects induced by the siRNA treatments were measured after 2/3 days. Transient transfections in HEK293T and U2OS cells were performed at 70–90% confluency. Transient transfections in MCF7 cells were performed at 10–20% confluency. Specifically for the siRNA screen with multimerized 3×NRF1 or 3×NFY sites and for the gene expression analyses based on RT-qPCR and microarrays, transient transfections were performed in 6-well plates. For the siRNA screen with natural promoters and for transient transfections, experiments were performed in 96-well plates. In 96-well plates, we transfected 8.67 pmols of siRNA (purchased from Sigma-Aldrich), 0.1 µg of pGL2-Luciferase constructs (see below), and 0.01 µg pRL(Renilla)-TK.5′-TGCGCATGCGCAGacgtacgtTGCGCATGCGCAGacgtacgtTGCGCATGCGCAG-3′, which contains three copies of the NRF1 site found in the FMR1 promoter (in uppercase) GDPD1 promoter, −158/+30 bp from the ASNSD1 promoter, −150/+50 bp from the ZBTB17 promoter, −150/+50 bp from the ZNF695 promoter, −150/+50 bp from the STIP1 promoter, −150/+50 bp from the AKAP8L promoter, −150/+37 bp from the ZWINT promoter, −400/+50 bp) from the RFX1 promoter, and −150/+100 bp from the CCT3 promoter. These regions and their margins were selected based on our NRF1 and NFYB ChIP-seq datasets, and contain NRF1 peaks and recognizable NRF1 motifs , NFYB peaks and recognizable NFY/CCAAT motifs , or NRF1 and NFYB peaks and recognizable NRF1 and NFY/CCAAT motifs . The position of the TSS was based on the gene annotation in the UCSC browser. For data visualization of the siRNA screen results with natural promoters, we represented normalized luciferase signal in heatmaps created by clustering Hypergeometric log values using Euclidean distance and average linkage with TMEV 4.9 Software (Dana-Farber Cancer Institute).We measured dual Luciferase/Renilla expression with the Dual-Glo Luciferase Kit (Promega) using a Veritas Microplate Luminometer (Turner Biosystems/Promega) and following standard procedures. Collected data were normalized  and referred to the control (scrambled siRNA) value. We engineered two constructs for the siRNA screen with multimerized sites containing 3×NRF1 or 3×NFY/CCAAT sites upstream the luciferase gene in the pGL2 plasmid harboring the TK minimal promoter. The two cloned fragments were the following: TFAM-forward 5′-GTGATTCACCGCAGGAAAAG-3′ and TFAM-reverse 5′-CTGGTTTCCTGTGCCTATCC-3′, FXR2-forward 5′-AACCGTGGTAATCGGACTGA-3′ and FXR2-reverse 5′-GGTGCAGGTTGGAGGTTTTA-3′, for CDC42-forward 5′-TACTGCAGGGCAAGAGGATT-3′ and CDC42-reverse 5′-CCCAACAAGCAAGAAAGGAG-3′, CDC2-forward 5′-CCATGGGGATTCAGAAATTG-3′ and CDC2-reverse 5′-CCATTTTGCCAGAAATTCGT-3′, SAP18-forward 5′-TGGATGCAACCTTGAAAGAA-3′ and SAP18-reverse 5′-TGGAATCATCAGTCCCCTTT-3′, and ACTB-forward 5′-GTGGGCATGGGTCAGAAG-3′ and ACTB-reverse 5′-TCCATCACGATGCCAGTG-3′. Cycle threshold (Ct) values were extracted with MxPro qPCR Software (Agilent) to calculate difference Ct (ΔCt) values with respect to control. For microarray analysis, cDNA quality was assessed by the Agilent Bioanalyzer (Agilent). Gene expression profiling was performed using the Human Illumina Sentrix Expression BeadChips system, as previously described Total RNA was extracted with the RNeasy Kit (Qiagen) and DNA was eliminated with on-colum DNase treatment (Qiagen) following the manufacturer's protocols. Total RNA was converted into cDNA with the SuperScript First-Strand Synthesis Kit (Invitrogen) following the manufacturer's protocol. Real-time PCR was conducted in a Mx3000P Real-Time PCR Instrument (Agilent). Brilliant qPCR Master mix (Stratagene). PCR settings were the following: 10 min at 95°C followed by 40 cycles of 95°C for 15 sec, 58°C for 15 sec, and 25 sec for 72°C. Primer sequences were the following: Co-immunoprecipitation (co-IP) experiments were carried out as previously described Nuclear extracts from MCF7 cells were prepared fresh similarly as in Proteins were loaded and run on 4–12% Bis-Tris gels with MES running buffer (Life Technologies). After transfer onto 0.2 µm-pore PVDF (BioRad) or nitrocellulose (Whatman) membranes, membranes were blocked with 5% milk/TBST for 30 min and probed with antibodies diluted in 5% BSA/TBST overnight at 4°C. Immunodetection was achieved after incubation with HRP-conjugated (Invitrogen) goat anti-mouse or goat anti-rabbit diluted 1∶5,000 in blocking solution. HRP signal was detected by ECL (Amersham-GE) and autoradiography film.p<0.0001) based on ChIP-DSL data; and genes affected by different siRNA treatments (specified in figure panels) based on gene expression microarrays. A representative selection of the most enriched functional terms and the p-value of their enrichment were extracted for visualization using Excel.The analysis of functional gene annotations was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) 2.1 website Networks of annotated biological functions associated with LSD1 targets were constructed with open source Cytoscape version 2.8.3 P-values<0.05 were considered statistically significant. Additional information about statistical analyses is provided in independent subsection (see above).Non-sequencing experiments were performed in triplicate (including ChIP-DSL experiments), while sequencing experiments were performed in triplicate but pooled before DNA library preparation. Data is presented as mean ± standard error of the mean (s.e.m.) of replicates. Comparisons between two groups were performed using paired two-tailed t-test. Figure S1A) A summary  of positive (left) and negative (right) motif co-occurrences identified in −150/+50 bp regions, based on B) Co-occurrence matrix of cardinal motifs in n = 10,063 DHS regions defined experimentally by DNaseI-seq in human MCF7 cells that overlapped with RefSeq promoters. Co-occurrence log2p-values are shown as a gradient of blue-to-red for positive-to-negative co-occurrence, and as white for no significant co-occurrence. (C) Distribution of NRF1 (left) and NFYB (right) ChIP-seq peaks with respect to the genome annotation. The numbers included in the pie charts refer to the fraction of peaks associated with each annotated region. The total number of peaks analyzed is also indicated. (D) Distribution of sequencing read density, based on H3K4me3 ChIP-seq, around NRF1 and NFYB ChIP-seq peaks (centre of the panel) in MCF7 cells. (E) Distribution of computationally predicted cardinal motif densities (black) and sequencing read density of: TF ChIP-seq data (red), DNaseI-seq data (light blue), H3K4me2 ChIP-seq data (light green), and H3K4me3 ChIP-seq data (dark green) with respect to TSS . On top, the name of the specific motif and TF (in parenthesis) analyzed is shown. Doted vertical lines indicate −150 bp and +50 bp positions. DNaseI-seq, H3K4me2/3 ChIP-seq, NFYB ChIP-seq, GABPa ChIP-seq, NRF1 ChIP-seq, and MAX ChIP-seq data were obtained in MCF7 cells. Sp1, YY1, ZBTB33, and TBP ChIP-seq experiments were obtained in K562 cells. Source of sequencing datasets: all but H3K4me3, NRF1, and NFYB ChIP-seq experiments were produced by ENCODE and are publically available.Cardinal motifs tend not to co-occur. ((PDF)Click here for additional data file.Figure S2A) Venn diagram depicting the overlap of NRF1 (red circle), NFYB (blue circle), and RNA PolII (grey circle) in MCF7 cells. NRF1 and NFYB data was based on ChIP-seq and peaks found between −800 bp and +200 bp relative to TSS. We considered as ‘overlap’ the coincidence of NRF1 and NFYB peaks in the same −800/+200 bp region. Also, we considered as ‘overlap’ the coincidence of RNA PolII peaks within ±1 kb of a TSS containing NRF1 or NFYB peaks at −8000/+200 bp. (B) Comparison of NRF1 (left) and NFYB (right) ChIP-seq peaks and ChIP-DSL positive hits in MCF7 cells. We compared the lists of −800/+200 bp genomic regions containing NRF1 or NFYB ChIP-seq peaks and the lists of NRF1 or NFYB ChIP-DSL positive hits (or promoters) to determine the number of coincident peaks/hits between both types of analyses. The analysis of ChIP-seq data was limited to the set of genomic regions present on the Hu20K array. We compared three levels of ChIP-DSL stringency based on p-values of positive hits: p<0.01, p<0.001, and p<0.0001. The percentage and number of promoters in each case are indicated. (C) Venn diagram depicting the overlap of NRF1 (red circle), NFYB (blue circle), and RNA PolII (grey circle) ChIP-DSL positive hits (p<0.0001) in MCF7 cells. The ChIP-DSL assay limits the analysis to −800/+200 bp regions relative to TSS. (D) Matrix of motif enrichment compared to background of the subset of n = 332 NRF1 and NFYB co-occupied promoters identified by ChIP-DSL. Motif analysis limited to −150/+50 bp regions. Motif enrichments higher than background are shown as a gradient of blue, while motif enrichments lower than background are shown as a gradient of red. No significant enrichment  is shown as white. (E) Functional or GO analysis of genes with promoters occupied by NRF1 (left), NFYB (middle), or NRF1 and NFYB (right) based on ChIP-DSL analysis (p<0.0001). Selected GO terms are shown in the y-axis. The x-axis refers to p-values of enrichment. Orange lines indicate p = 0.001.Poor rate of colocalization between cardinal TFs NRF1 and NFYB at −150/+50 bp regions and beyond. ((PDF)Click here for additional data file.Figure S3A) SiRNA screen based on the luciferase assay to identify KDMs (listed at the bottom) that may act selectively via 3×NRF1 sites (red) or 3×NFY sites (blue). A schematic overview of this screen and a summary of the results are shown in P-values≤0.05 are shown as grey boxes, p-value>0.05 are shown as white boxes. (B) Distribution of sequencing read density based on DNaseI-seq (top) and H3K4me2 MNase-seq  around LSD1 ChIP-seq peaks in MCF7 cells. (C) Venn diagram depicting the overlap of LSD1 (orange), NRF1 (red), and NFYB (blue) ChIP-DSL positive promoters in MCF7 cells based on the Hu20K array (−800/+200 bp). (D) Venn diagram depicting the overlap of LSD1 (orange), NRF1 (red), and NFYB (blue) ChIP-seq peaks in MCF7 cells without restriction of genomic localization (whole genome). (E) Percentage (top) and relative number (bottom) of LSD1-occupied promoters in NRF1- (red), NFYB- (blue), or Sp1- (green) occupied promoters in MCF7 cells, based on ChIP-DSL data. The percentage (top) and relative number (bottom) of LSD1-occupied promoters in cases that are not occupied by NRF1, NFYB, or Sp1 are also shown (large square). Analysis restricted to the n = 2,000 promoters present on the Hu2K array. The top panel shows percentage of LSD1-occupation relative to the total number of TF-positive promoters. The bottom panel shows absolute numbers of the same analysis. Data for Sp1 ChIP-DSL was obtained using the Hu2K array. Data for LSD1, NRF1, and NFYB ChIP-DSL was obtained using the Hu20K array, but their analyses were restricted to the set of promoters contained in the Hu2K array. Note: the Hu2K array is mostly constituted of cell cycle-regulated promoters, therefore, the percentages/frequencies of occupied promoters by the different TFs/LSD1 in the Hu2K array might be different than those observed in the analysis of the ‘unbiased’ Hu20K array.Strong co-association between cardinal TF NRF1 and cofactor LSD1 at genome-wide scale. ((PDF)Click here for additional data file.Figure S4A) Top-enriched motifs found in LSD1 ChIP-seq peaks in MCF7 cells identified by de novo motif discovery analysis. The panel includes: rank of motif enrichment, name of TFs associated with each motif, and p-value of enrichment. (B) LSD1 binding at genomic regulatory regions of representative examples of well-known ERα-regulated genes. The panel shows ChIP-seq tracks for LSD1 (orange), NRF1 (red), NFYB (blue), ERα (light green), FoxA1 (dark green), and H3K4me3 (black) at ERα-regulated loci: TFF1, CTSD, and GREB1. Refseq annotations are shown at the bottom of each panel. Coincident ERα and LSD1 peaks are enclosed in dotted boxes. (C) Heatmap analysis of LSD1, H3K4me3, NRF1, and ERα ChIP-seq signal ±3 kb around LSD1 ChIP-seq peaks (center of the columns). Two sets of LSD1 peaks were separately analyzed: LSD1 peaks nearby H3K4me3-marked regions ; and, LSD1 peaks nearby H3K4me3-negative regions .Strong co-association between LSD1 and NRF1 at proximal promoters and between LSD1 and ERα at distal sites in MCF7 cells. ((PDF)Click here for additional data file.Figure S5A) Percentage of NRF1 and NFYB occupied promoters that contain computationally predicted NRF1 or NFY motifs  in MCF7, HMEC, LNCaP, U2OS, and SH-SY5Y cells. Data based on ChIP-DSL analysis. ‘Background’ corresponds to the fraction of approximately 20,000 promoters in the Hu20K array that contain predicted NRF1 or NFY motifs. (B) ChIP validation of randomly selected LSD1 target promoters identified by ChIP-DSL in MCF7 cells (top), and ChIP analysis of the same promoters in U2OS cells (bottom). The results are shown as binding fold change over IgG signal. Three promoters were included as negative control (based on ChIP-DSL experiments): LUC7L2, KIF11, and ESR1 promoters. (C) ChIP analysis of well-known NRF1-regulated promoters  and negative controls (GAPD and UMODL1 promoters) in MCF7 and U2OS cells. NRF1 (left panel), LSD1 (middle panel), and RNA PolII ChIP (right panel) analyses are shown. (D) LSD1 (orange or green), NRF1 (red), and H3K4me3 (black) ChIP-seq tracks in MCF7 cells (top) and mESCs (bottom) for well-known NRF1-regulated promoters: TFAM (left panel), FMR1 (middle panel), and FXR2 (right panel). LSD1 ChIP-seq data in mESCs was obtained from Whyte et al., 2012. Refseq annotation is shown at the bottom of each panel.Strong binding co-association between NRF1 and LSD1 in different cell lines/types. ((PDF)Click here for additional data file.Figure S6Binding of LSD1 to promoters of nuclear-encoded components of the mitochondrial electron transport chain. Top, left: scheme of multiprotein complexes involved in the electron transport chain (ETC) in the inner mitochondrial membrane. Panels: ChIP-seq tracks depicting loci of representative examples of nuclear-encoded components of the mitochondrial electron transport chain. LSD1 (orange or green), NRF1 (red), and H3K4me3 (black) ChIP-seq tracks from MCF7 cells (three top tracks) and mESCs (bottom track) are shown. LSD1 ChIP-seq data in mESCs was obtained from Whyte et al., 2012. Refseq annotation is shown at the bottom of each panel.(PDF)Click here for additional data file.Figure S7Fine co-localization of LSD1 and NRF1 in multiple mammalian promoters. ChIP-seq tracks of representative loci showing co-alignment of NRF1 (red) and LSD1 (orange) peaks. These particular examples were selected from the small list of promoters in which NFYB (blue) binds nearby NRF1 to help emphasize the good co-alignment between LSD1 and NRF1 using NFYB as reference. The H3K4me3 ChIP-seq track (black) is also shown as reference. Refseq annotation is shown at the bottom of each panel.(PDF)Click here for additional data file.Figure S8A) Three sets of pGL2(basic) constructs were engineered to contain NRF1 (left), NFY (right), or NRF1 and NFY (middle) sites in the context of the sequence of their natural promoter cloned upstream the luciferase gene. The specific promoters were selected based on NRF1 and NFYB ChIP-seq data in MCF7 cells (promoter/gene names are listed in the figure), and all contain recognizable NRF1, NFY, or NRF1 and NFY motifs.  Hierarchical clustering of luciferase levels relative to control (scrambled) siRNA induced after the different siRNA treatments (listed in the figure). Constructs are listed on top: those containing NRF1 motifs are indicated by a red dot; while those containing NFY motifs are indicated by a blue dot. In (B), it is shown the clustering analysis of the three NRF1- and the three NFY-regulated promoters. In (C), it is shown the clustering analysis of the six promoters shown in (B) plus the three NRF1/NFY-regulated promoters.SiRNA-based screen in HEK293T cells to test KDM siRNA-mediated effects on luciferase expression under control of NRF1 and/or NFY motifs in the context of the sequence of their natural promoters. ((PDF)Click here for additional data file.Figure S9(A) Comparison of co-occurrence matrixes of the seven cardinal motifs identified in this study  and the most-enriched motifs identified in a previous analysis by Fitzgerald et al., 2004 . The values of motif co-occurrence in the right panel were directly derived from Fitzgerald et al., 2004. Co-occurrence log2p-values are shown as a gradient of blue-to-red for positive-to-negative co-occurrences, and as white for no significant co-occurrence. (B) Analysis of cardinal motif preferences for promoters classified based on their mode of transcription initiation: focused (left) and dispersed (right). Number of promoters are indicated. We used 5′ RNA-seq data in MCF7 cells obtained from the DBTSS database  Co-occurrence matrixes of cardinal motifs in focused and dispersed promoters identified in MCF7 cells. Co-occurrence log2p-values are shown as a gradient of blue-to-red for positive-to-negative co-occurrence, and as white for no significant co-occurrence.Distribution of cardinal motifs and analysis of motif co-occurrences in promoters classified based on the mode of transcription initiation. base see . The x-a(PDF)Click here for additional data file.Figure S10LSD1 and/or NRF1 depleted cells. (A–F) GO analysis of six of the eight classes of genes identified by expression microarray (as shown in NRF1 and/or LSD1 siRNA treatments in U2OS cells: genes down-regulated (A) or up-regulated (B) by both treatments ; genes exclusively down-regulated (C) or exclusively up-regulated (D) by LSD1 siRNA ; and, genes exclusively down-regulated (E) or exclusively up-regulated (F) by NRF1 siRNA . The two remaining categories (Class III and Class IV) do contain a number of genes that is too low for reliable GO analysis (n = 18 and n = 35). (G) Analysis of NRF1 and LSD1 binding to active promoters regulating nuclear-encoded genes in MCF7 cells. LSD1 and/or NRF1-positive promoters are shown in the left cylinder, and LSD1 and NRF1-negative promoters are shown in the right cylinder. There are three subcategories in the first group: LSD1/NRF1 common-positive promoters (brown), LSD1 only-positive promoters (orange), and NRF1 only-positive promoters (red). Only H3K4m3-positive (active) promoters identified by ChIP-DSL in MCF7 cells and regulating genes in the category of ‘mitochondrion’ in the NCBI database were included in this analysis . Data based on H3K4me3, NRF1 and LSD1 ChIP-DSL experiments.Functional categories (GO terms) associated with genes differentially expressed in d cells. A–F GO a(PDF)Click here for additional data file.Figure S11p-value<0.001) compared with randomly generated networks with the same number of nodes. The three missing subnetworks in the figure included proteins that are included within the subnetwork shown in the right bottom of the figure. Functional categories are indicated next to each (sub)network. Gene names are indicated inside each node. Node color, node shape, and node size codes are indicated in the top right side of the figure.Protein interaction network of genes potentially regulated by LSD1. Cytoscape analysis of gene products associated with LSD1 binding in MCF7 cells (based on LSD1 ChIP-seq data). Full network analysis is shown on top, and two of the five high-score subnetworks are shown at the bottom. The full network and these two subnetworks show a clustering coefficient that is highly significant ((PDF)Click here for additional data file.Figure S12NRF1 and/or LSD1 siRNA treatments) are functionally active. It excludes, therefore, the situation in which both NRF1 and LSD1 remain apparently inactive (based on the observation of no-effects by microarray upon NRF1 and LSD1 siRNA treatments). Classification (Class number) based on Summary of proposed NRF1-LSD1 functional partnerships acting via −150/+50 bp regions. This summary only considers partnerships in which either NRF1 or LSD1, or both Click here for additional data file.Table S1NRF1 ChIP-seq peaks identified in MCF7 cells. Positions are expressed in hg18 coordinates.(XLSX)Click here for additional data file.Table S2NFYB ChIP-seq peaks identified in MCF7 cells. Positions are expressed in hg18 coordinates.(XLSX)Click here for additional data file.Table S3LSD1 ChIP-seq peaks identified in MCF7 cells. Positions are expressed in hg18 coordinates.(XLSX)Click here for additional data file.Table S4p<0.0001 (red cells), as “3” when reached p<0.001 (orange cells), as “2” when reached p<0.01 (yellow cells), or as “1” when p>0.01 . All results result from co-hybridization of ChIP and input samples. Note: the Hu20K array contains n = 20,000 promoters, but we limited our analyses to n = 17,288 promoters after eliminating n = 2,713 promoters (grey cells in the Excel file) in which the ChIP-DSL hybridization signal was too low or unreliable in at least one of the experiments.NRF1, NFYB, LSD1, and Sp1 ChIP-DSL positive promoters identified in MCF7 cells. NRF1, NFYB, and LSD1 ChIP-DSL experiments were performed in the Hu20K (in triplicate). The Sp1 ChIP-DSL experiment was performed in the Hu2K array . We classified promoters as “4” when a positive promoter reached (XLSX)Click here for additional data file.