Conventional wisdom holds that, owing to the dominance of features such aschromatin level control, the expression of a gene cannot be readily predicted fromknowledge of promoter architecture. This is reflected, for example, in a weak orabsent correlation between promoter divergence and expression divergence betweenparalogs. However, an inability to predict may reflect an inability to accuratelymeasure or employment of the wrong parameters. Here we address this issue throughintegration of two exceptional resources: ENCODE data on transcription factorbinding and the FANTOM5 high-resolution expression atlas.Consistent with the notion that in eukaryotes most transcription factors areactivating, the number of transcription factors binding a promoter is a strongpredictor of expression breadth. In addition, evolutionarily young duplicates havefewer transcription factor binders and narrower expression. Nonetheless, we findseveral binders and cooperative sets that are disproportionately associated withbroad expression, indicating that models more complex than simple correlationsshould hold more predictive power. Indeed, a machine learning approach improves fitto the data compared with a simple correlation. Machine learning could at bestmoderately predict tissue of expression of tissue specific genes.We find robust evidence that some expression parameters and paralog expressiondivergence are strongly predictable with knowledge of transcription factor bindingrepertoire. While some cooperative complexes can be identified, consistent with thenotion that most eukaryotic transcription factors are activating, a simplepredictor, the number of binding transcription factors found on a promoter, is arobust predictor of expression breadth.The online version of this article (doi:10.1186/s13059-014-0413-3) contains supplementary material, which is available to authorizedusers. Is it possible to predict expression parameters of a gene from knowledge of thepromoter architecture of that gene? If, for example, we knew the transcription factors(TF) that bind the promoter of a gene, can we predict the breadth of expression (BoE) or themean level of expression of that gene? It is known that expression patterns of geneduplicates diverge over evolutionary time ,2, but cAt first sight the answer to many of these questions may appear rather trivial:surely if we know the TFs that bind a gene’s promoter and know when those TFs arepresent in cells then we must know the expression parameters of a gene ? Howeverr2 approximately 1% to 2%), much less than the degree of co-expression oftwo linked genes with no transcription factors in common (r2 approximately 10%) [Drosophila segmentation is wellpredicted by the knowledge of TF binding sites and TF levels [Much evidence supports a strong role for chromatin in dictating expressionprofiles. For example, insertion of the same transgene into different regions in thegenome leads to different expression levels dependent on the expression profile of theneighboring genes . Similarely 10%) .Moreoveely 10%) ; while Cely 10%) . These rF levels .cis-regulatory regions was so weakly correlated with expression divergencein a multiple regression that it was not significant after multi-test correction[cis-regulatory effects are not a major influence onexpression profile. By contrast, a promoter screen in yeast found evidence for arobust correlation between the number of shared motifs and the degree of expressiondivergence between paralogs [One approach to determine the extent to which promoter architecture determinesexpression parameters has been to consider the relationship between expressiondivergence and promoter divergence between paralogs within a genome or betweenorthologs in different genomes –22. The rrection. A furthrrection. These rWhile the consensus view is that promoter architecture does not well predictexpression parameters, there is also then a lack of perfect agreement on this. Onepossible reason the studies are not obviously in agreement is that there is much noisein both measures of expression and inference of which proteins bind any given gene’spromoter. In addition, it is not immediately clear what metric of, for example,promoter divergence would be most informative. We return to this issue employing amerge of two exceptional data sources, ENCODE and FANTOM5. We used ENCODE ChIP-seqmeta dataset derived from multi cell-line clustered experiments published in 2012. Whole-gHere, then, we employ this novel data to ask whether expression profiles can bepredicted from promoter architecture. In the first instance we wish to know whetherthe total number of transcription factors binding a promoter is a good predictor. Wefollow this up with the analysis of interactants and a more complex machine learningapproach. We start by resolving basic parameters of TF binding and promoterarchitecture.Before attempting to describe any correlations between the number of Tfbs(TfbsNo.) and expression, it is instructive to know what the distribution of thenumber of transcription factors per gene looks like. Perhaps it is normallydistributed? To determine this, proximal promoters were defined by a symmetricalwindow around the transcription start site – TSS (±500 bps). The distribution ofTfbsNo. is not normal, instead it follows a power law Figure . At the We have assumed above a given size for promoters. Can we use our data todetermine an average upper limit to the size of promoters? We expect that TF bindingsites should be concentrated near the TSS and as we move ever further away theincrease in the number of TF binding sites should tend to a linear function,indicating background/random rates. As expected, the number of Tfbs increasesprogressively with the window size, transforming gradually to a linear, background,rate of increase Figure a. Using sensu stricto, that is, expressed in one tissue only. The remaining0.19 is the fraction of transcripts which lack evidence for expression in FANTOM5tissue samples at the cutoff of 10 TPM, owing perhaps to their highly restrictedspatial and/or temporal expression in a very limited subset of cells. In comparisonto all genes, ENCODE Tfbs have higher average BoE  with the fractions oftissue-specific, intermediate, and housekeeping Tfbs at 0.32, 0.17, and 0.38. Top 10housekeeping Tfbs included Pol2, JunD, c-Fos, JunB, Rad21, GTF2F1, NELFe, SREBP2,RXRA, and HSF1 . For 17 Tfbs we found no evidence of expression in tissue samples.Is there something special about those genes with very many TF binding sites?Are they for example broadly expressed, as expected if TFs are dominantlyactivating? To analyze this we presumed, in the first instance, that a CAGE signalgreater than 10 tags per million (TPM >10) classified a gene as expressed, or‘on’ in a given tissue (this was the consensus definition accepted by the FANTOM5consortium). The BoE is the fraction of tissues or cell-lines in which the gene was‘on’, that is, in which it was transcribed. Figure rho = 0.48, Figure rho = 0.40). While the raw data show some scatter , and a negative correlation betweenBoE and ENCODE methylation signal .There was also a strong correlation of BoE with GC- and CpG-content . There was also astrong correlation between CpG and TfbsNo. . Similar results were reported by the ENCODEconsortium who found that GC content of ChIP-seq sites was 61 ± 5% for TSS-proximalpeaks [vs.approximately 250 bps).Closer scrutiny of the impact of GC content as a predictor supports the viewthat it is GC of the core promoter rather than a more regionalized GC content thatimpacts BoE. When we divided promoters into low GC  andhigh GC , the second group had more than threetimes higher average BoE (the exact ratio was 0.3379/0.099 = 3.41) and on averagebound more than four times more TFs 9.55/2.29 = 4.17). The GC content of proximalpromoters (defined as a 1 kbps window) was much higher than that of surrounding DNAsequences (20 kbps window): 0.594  = 4.17. alpeaks , these aoe) in a support vector machine (SVM) learning datasetdoes not substantially increase prediction accuracy over the simple SVM trained withdata on Tfbs numbers. . However, partial correlation between BoEand TfbsNo. was higher, after controlling exclusively for CpG (rho = 0.37), suggesting this effect was dominant. Takentogether, these results suggested that CpG was more of a place marker than a keypart of the mechanism. Promoter GC content was clearly distinct from the isochore GCcontent or GC3 . NevertWe note that we see little or no evidence for a class of genes so highly broadlyexpressed that they dispense with TFs altogether. In fact, there were only 39broadly expressed genes with fewer than 10 high-quality TFs in a broad 10 kb windowaround the TSS , when they were calculated only across tissues inwhich the gene was ‘on’ (at the cutoff of 10 TPM). As definition 1 forces the meanand median across all tissues to co-vary with the breadth, the strong correlationsfound using definition 1 were most likely just detecting the primary underlyingcorrelation with the BoE. We conclude that the Tfbs number is a poor predictor ofexpression rates when expression breadth is not a compounding factor.We can ask how these two definitions also relate to Tfbs number. Usingdefinition 1 of mean/median expression, we find that the number of transcriptionfactor binding sites correlates with the mean expression, and the median expression,but not the maximum expression of a transcript  with noise (spurious orunassociated binding). As expected Figure c, the coThe inter-relationship, while strongest for small window sizes, persisted forwindows up to 40 kb in size Figure c,well brp) between the BoE and the number of transcription factor binding sitesequaled 0.53 for primary cells, and 0.61 for cancer cell lines. The correlation isstrikingly stronger for cell lines than for tissues. It makes sense that thecorrelation was stronger for primary cells and cell lines (r2 approximately 28% to 37%) than for tissues (r2 approximately 20%), as tissues are complex mixtures of cell typeswhere some of the cell-type-specific signal might have been lost.To control for the possibility of a sample bias or differences between normaland diseased tissues, we tested whether the correlation between the BoE and thenumber of transcription factor binding sites held across the entire FANTOM5 samplespace. Figure Above we considered all bindings at promoter regions of genes, including RNApolymerase II binding sites. One might readily object that if one includes PolIIbinding then highly expressed genes may well have more bindings, if only becausethey have more PolII. Might then the correlation between the BoE and the number oftranscription factor binding sites be driven by RNA polymerase II binding sites, orbiased by another abundant transcription factor?P value <2.2e-16).The correlation was 0.435 when additionally only unique sites were counted.In comparison, the original correlation including all sites was 0.448 , and when onlyunique sites were counted 0.45 . Indeed, the three derived measures correlatedvery highly with the original measure with correlation coefficients in pairwisecomparisons of 0.994, 0.997, and 0.992 for unique sites, no PolII sites, and uniquesites excluding PolII ,respectively.To investigate this we employed four approaches for counting, these being: (1)the total number of binding sites; (2) the number of unique binding sites; (3) thetotal number of binding sites excluding RNA polymerase II; and (4) the number ofunique binding sites excluding the polymerase. We find that the correlation holdsregardless of the method . The correlation between the BoE and the number oftranscription factor binding sites persisted after controlling for the polymerasesignal .We can also turn the data the other way around and ask whether sites with moreTF bindings also have more PolII. Such a correlation would provide sound evidencethat more TFs do indeed result in more transcription. We find this to be the case.After excluding its own sites, the polymerase signal correlated strongly with thetotal number of transcription factor binding sites . However, the latter comparisons were not fully independent and theresults might have been biased by large gene families with a high number of pairwisecomparisons. For example, the core histones of H2A@, H2B@, and H3@ familiesunderwent dramatic expansions in placental mammals, resulting in paralogs which arehighly co-expressed in proliferating tissues such as the thymus and the testis(manuscript in preparation).As discussed in the introduction, a common method to approach the problem of thedegree of promoter-centered control of gene expression has been to ask about thesimilarity in gene expression of paralogs as a function of the similarity in theirpromoter domains. In addition to the correlation between the BoE and the number oftranscription factor binding sites, we found that the divergence of proximalpromoters  have average BoE almost four times lower, and averageTfbsNo. 2.7 times lower Figure  and 10 tTo investigate the origin of new tissue-specific genes, we divided duplicationevents into three subclasses: ‘housekeeping conserved’ , ‘tissue − sp. conserved’ , and‘transformative’ (where one daughter gene was housekeeping while the other wastissue-specific). The relative proportion of ‘tissue − sp. conserved’ eventsincreases for younger taxa indicating that this class of duplication events isresponsible for the majority of the increase of tissue-specificity observed foryoung taxa Figure c and d. The broad-brush correlations that we have addressed above suggest that the moreTFs bind a promoter the more broadly expressed the gene. But are there some TFs thatare especially influential in driving broad expression or is the effect simply owingto an accumulation of TFs causing increased likelihood of broad expression? Toaddress this, we clustered the BoE with a matrix of transcription factors toidentify key associations Figure .Figure 1First, the BoE was merged into one matrix with the number of ENCODEtranscription factor binding sites. Next, a heatmap was drawn for this matrix inorder to determine which transcription factors correlated closest with the BoE, thatis, which transcription factors acted as molecular switches for house-keepingexpression Figure . The hea. P valueswere calculated using Fisher’s exact test with a Bonferroni correction for multipletesting.Four broad classes of transcripts emerged through this integrative analysis.Class A genes are typical broadly expressed Tfbs-rich genes. Class B genes areunusually broadly expressed Tfbs-poor genes. Class C are unusual tissue-specificTfbs-rich genes, while class D are typical tissue-specific Tfbs-poor genes. Thesefour classes are marked A to D in Figure As expected, the BoE clustered with RNA polymerase II and the transcriptioninitiation factor TFIID – TAF1. This was perhaps unsurprising as the polymerase andTAF1 are constitutive components of the transcription apparatus. More interestingly,in human tissues, the BoE also clustered closely with Mxi1, YY1, NFKB, HEY1, Sin3A,and c–Myc  and causal (TF binding) predictors of expressionbreadth. Rather we simply wish to ask whether incorporation of the likely causalfactors in a more sophisticated statistical framework permits better understandingof the control of expression breadth.The SVM is a commonly used machine learning approach to prediction. We trainedthe SVM using a randomly chosen half of the dataset. Each row of the basic SVMtraining data-frame was a vector representing the number of Tfbs of each gene;although, we also considered SVMs trained using promoter GC and CpG contentsTable . The resrp = 0.7460 (Table rp = 0.759). Moreover, the predictor did not simply rely on summing up ofTfbs, since an SVM trained using only the sums of Tfbs as features  did not improve on the simple correlation. The number of support vectors was high (approximately 60% ofthe training cases) suggesting that no simple discriminatory features could befound. Taken together, these results underlined a cooperative effect of many Tfbsacting together and clustering in a narrow window around the TSS to control theBoE.As noted above, in cancer cell lines, the correlation between the BoE and thenumber of transcription factor binding sites is 0.61. The SVM’s prediction improvedon this and correlated with the observed value of the BoE with 60 Table . ResultsEven though specific interactions can be identified, that a simple correlationapproach can capture so much is striking. The fact that a correlation approach worksis consistent with the notion that most transcription factors in eukaryotes areactivators. There is, however, an alternative interpretation of the correlationbetween number of TFs and increasing expression, this being that it reflects more apassive process of spurious TF binding. A simple model in which open/transcribedchromatin is to some degree ‘sticky’ could in principle predict the samecorrelation. At the limit there might be a single TF that forces broad expression,but because of its presence other TFs are recruited, not because they are needed,but because TFs might be attracted to open chromatin and transcriptional hotspots asiron filings are attracted to a magnet. In this context, were for example GC richsequence ‘sticky’ for transcription factors, this might explain the GC-TFcorrelations. However, such a ‘sticky’ model would require strong binding of the TFsto the DNA, rather than weak and short-lived non-specific interactions which aremost unlikely to resist the processing of the TF-DNA interaction in ChIP-seqmethodology.rp = 0.42), than it is for transcripts with more than twenty (rp = 0.098). We would expect the opposite if Tfbs simply accumulated in arunaway positive feedback loop.Several facts argue against the ‘sticky’ model. First, our approach hasrecovered known interacting complexes, such as cohesin and CTCF ,41, suggt-test, P value <2.2e-16). These results are in agreement withthe general trend demonstrated by ENCODE for almost all transcription factor bindingsites to have highest densities very close to the TSS [. This locus on chromosome 17  has the highest number of Tfbs in our dataset, which are clearlyoverlapping or ‘stacked’. The SRSF2/MFSD11 locus has 71 Tfbs in thewindow ± 1,000 bps from the TSS and drives bidirectional transcription ofserine/arginine-rich splicing factor 2 (SRSF2) and major facilitator superfamilydomain containing 11 (MFSD11).In addition, we can ask whether the TF binding sites are clustered withinpromoters. Were the ‘sticky’ spurious binding model correct, we might also expectthat TF bindings sites are randomly located within promoters. By contrast, a modelof cooperative binding to DNA and the synergistic mode of action in attractingpolymerase and activating transcription, might predict TF bindings sites to clusterand overlap more than expected by chance. These two alternative concepts areillustrated in Figure  the TSS ,42. As arho = 0.3176. Tissue-specific Tfbsbind on average more tissue-specific genes (mean BoE of targets 0.394), thanhousekeeping Tfbs (mean BoE = 0.468), Wilcoxon rank sum test P value = 6.805e-05. Under the pure ‘sticky’ model, we would expect nocorrelation. The picture is not clear-cut, however. Clearly, tissue-specifictranscription factors can have targets in promoters of housekeeping genes. It ispossible that housekeeping expression in certain tissue-types demands activation bythis tissues unique transcription factors in addition to the standard set of TFsmediating housekeeping expression. Finally, TFs may differ in the degree of‘stickiness’. Some TFs may be ‘sticky’ towards open chromatin, while others bindvery specifically, perhaps playing a key role in the initial act of the opening ofthe chromatin.A further argument against the ‘sticky’ model of Tfbs binding comes from theexamination of the BoE of ENCODE Tfbs and their targets. We detected a strongcorrelation between the average BoE of Tfbs targets and the BoE of respectiveregulating Tfbs . Which TFs are actually bound will depend on a particular tissue and mayvary greatly. This is because ENCODE inputs were derived datasets, where individualpeaks from different tissues were merged if tagged to the same transcription factorbinding site. In fact, counting Tfb sites across different tissues would be a sourceof a logical error, as a circular association between the BoE and the presence ofTfb sites in multiple tissues would obscure any causal connections.adhoc than the ‘sticky’ chromatin model, having to make the extraassumption that only TFs associated with broad expression are ‘sticky’, for which wesee no a priori defense. Moreover, this modelcannot simply explain some of the above results, such as the stronger correlationfor the less broadly expressed genes.While multiple lines of evidence argue against the ‘sticky’ chromatin model asthe best explanation for the correlation between BoE and number of TF binding siteswe cannot entirely refute the hypothesis. Indeed scrutiny of the number of TFsbinding the very most broadly expressed genes . In principle a machine learning approach mightbe devised to employ the data that we have assembled to tackling such an issue. Wetook a similar approach to the SVM given above to predict tissues-specificexpression . The main difference was that the continuous responsevariable was the preferential expression measure (PEM) in a given tissue instead ofthe BoE.avg). This provides a metric of the degree to which genes arepreferentially expressed in tissue X. We then ask about our ability to predict PEMvalues. To this end we train our SVM against PEM values and ask it then to predictPEM values of genes outside of the training set. If the SVM works, our predictorshould correlate with our observed results. Within each tissue we then correlatepredicted PEM for all genes against observed PEM for the same genes in the sametissue. These correlation scores are on the Y-axis in Figure For a given transcript Y we consider its expression level in tissue X and dividethat by Y’s mean expression in all tissues. This ratio is PEM for that transcript inthat tissue. So a high PEM for gene Y in tissue X means it is preferentiallyexpressed in tissue X. For all transcripts we then consider the average PEM for agiven tissue  for genes expressed in any given tissue (rho = 0.54). That is to say, the tissues for which we fail to predictthe preferential expression of transcripts are the tissues with few preferentiallyexpressed transcripts .Overall, we conclude that at best we have only a moderate ability to predict degreeof preferential expression of genes in any given tissue and that this diminishesgreatly when the tissues themselves have few tissue specific genes.We find that SVMs trained using a matrix of TF frequencies can at best modestlypredict preferential expression for some tissues  with the correlation between thepredicted and the observed expression of up to n Figure expression is to divide the transcripts into two categories: tissue-specific and nottissue-specific, at the cutoff of the BoE of 0.33 .The approach taken here is identical to the one described above  exceptthat the input and output measures of the BoE were discretized. Standard validationcharts for such a SVM-based binary classifier are shown in Additional fileThe results above support the view that, to a considerable degree, components ofthe expression profile, notably expression breadth, and in turn expression breadthdivergence, can be predicted from knowledge of the TF binders of a given gene. Thatsuch a result was not, for the most part, captured previously, may be explained aslimitations in the prior data. That we can recover the BoE result using the previouslyavailable Gene Expression Atlas ,suggestHere, while touching on expression level, we have concentrated on expressionbreadth and divergence in breadth. Our results, however, suggest a series of furtherquestions. How, for example, are we to interpret the evidence for cooperation betweenpartners not previously known to be cooperative? Assuming antibody cross-reactivity isnot the explanation, these statistically associated TFs suggest experimental tests forapparent cooperativity. Our analysis of whether it is possible to predict the tissueof expression of tissue specific genes suggests that a support vector approach haslimited success at best. Given that we have the best success for the tissue (brain)with the most data, it may yet prove to be the case that with better techniques andmore data this problem may become more tractable.A further open issue is whether we can extrapolate our results to non-humanspecies. An ability to infer mutational changes in promoters that are likely to have amajor impact on expression, in poorly studied close relatives would be of considerablevalue in determining those promoters and TFs that might be (or have been) underselection in humans to switch gene expression ‘on’ or ‘off’ in tissues key to humanuniqueness. Given the above result (or the moderate ability to predict brain specificgenes) there may be some prospect of identifying some lineage specific changes thatmight have affected human brains.We present evidence that expression breadth and paralog expression divergence arestrongly predictable with knowledge of transcription factor binding in the proximalpromoter. A simple metric, the number of binding transcription factors found on apromoter, is a robust predictor of expression breadth in human tissues. However,prediction of the tissue of expression is moderate at best.human.tissue.hCAGE.hg19.tpm.refgene.osc.txt - humantissues;(T) human.primary_cell.hCAGE.hg19.tpm.refgene.osc.txt - humanprimary cells;(PC) human.cell_line.hCAGE.hg19.tpm.refgene.osc.txt - human cancercell lines.(CCL) Primary FANTOM5 CAGE data . The links to the files and description of inputscan be found in the following webpage references ,26,31,32Gene Expression Atlas (GEA) data  were useencodeMergeReplicates, regClusterBedExpCfg, and hgBedsToBedExps). Crucially, this procedure merges peaks for the sameTF across replicates, cell lines, and from different labs into one. Peak scoresvaried between zero to 1,000 (proportionately to the Tfbs prediction reliability).We used either all data, or only high-quality peaks with the score over half themaximum  to avoid noisy data associated with multi-mappingnext-generation sequencing tags. For the correlation between the BoE and the numberof transcription factor binding sites, we verified that results analogous to thosefor the January, 2011, data-freeze [Tfbs_x_length_unique). The correlation between Tfbs_x_length and Tfbs_x_length_unique was almost perfect (rp = 0.9929) and these two measures supported identical biologicalconclusions in all analyses (ensuring that no single Tfbs could introduce abias).The 2011 meta dataset included 2,750,490 ChIP-seq peaks for 148 transcriptionfactors, derived from 71 cell-types with 24 additional experimental cell cultureconditions . Peaks wa-freeze were obta-freeze . The 201a-freeze . The ENCJI = I/U). JI equaled 1when the intersection equaled the union (that is when there was a perfect overlap).JI equaled 0 when there was no overlap.The JI measured the overlap between two sets of genomic features . The indBEDtools [CAGE tags were mapped to RefSeq transcripts ±500 bps from their transcriptionstart sites. The numbers of tags were normalized to tags per million (TPM). Finally,the TPM value of 10 was chosen as the default cutoff for a gene to be ‘on’ (unlessstated otherwise). We downloaded RefSeq in BED format from the hg19 UCSC genomebrowser using the table browser tool. This dataset included 40,856 humantranscripts, including messenger RNAs (NM-accession) and non-coding transcripts(NR-accession). Non-coding transcripts included structural RNAs and transcribedpseudogenes. The dataset was later processed with BEDtools .R annotation object hgu95aREFSEQ from the hgu95a.db package. ENCODE transcription factors were mapped to RefSeqjust as in the FANTOM5 analysis.Gene Expression Atlas  wasemplTreeFam  used evoTreeBeST was the tree-building engine behind TreeFam. TreeBeST merged a maximumlikelihood tree from PHYML  withneiTreeFam release eight was based on Ensembl version 54 and included 79 species,1,539,621 genes, and 16,064 families. Phylogenetic timing was used to associate geneduplications with the emergence of different taxa. The Vertebrata and the Bilateriawere consistently linked with the most numerous waves of duplications in animals[n elements, the number ofk-combinations was equal to the binomial coefficient (‘n choose k’). K equals two forpairwise comparisons. For example, there were 10 possible pairwise comparisons in afamily of five paralogs. There were 45 legitimate pairwise comparisons in a familyof 10 paralogs. However, these comparisons might not be regarded as independent datapoints, and the results might have been biased by a few large gene families. Thebest alternative was to perform a comparison between each paralog with only itsclosest relative .Combinatorics explains why there are more possible paralog comparisons thanparalogs. In a set of R package called the Duplicator[R data-frames with data on duplications mapping to different taxa canbe found in R helper environments for the package(env_duplicator_base and env_duplicator_vectors), which are located in the pathduplicator/data/duplicator. Naming conventionsindicate the species of origin. For instance, the R data-frames containing human data are called: dupEvent12_genes_LL_hs  and dupEvent12_genes_LL_hs2 .The dataset used in the preparation of this study was released to the publicdomain as an plicator. R data-R data-frame dupEvent12_genes_LL_hs2 has the following fields: family (TreeFamfamily ID), node (unique identifier for each gene duplication node), taxon (taxon of duplication), gene.x , familySide.x, primary_acc.x , gene.y , familySide.y, primary_acc.y . FamilySide was a flag with values of one or two defining on which sideof the duplication node the gene was located; this flag was used to prevent multiplecomparisons on the ‘same side’ of the primary duplication node if later duplicationsoccurred.Data-frames in the package follow the denormalized data model. For example, theBEDtools stage). This stage consisted of data retrieval fromTreeFam and remodeling into Rdata-frames. Helper environments held these data-frames for later use, andstored intermediate results.Data retrieval and remodeling (the pre-BEDtools stage). Wechecked if two sets of genomic features overlapped using BEDtools. We used mostly coverageBed and intersectBed.CoverageBed calculates the depth of coverage of features in thefile A against the file B. Herein, coverageBed was used to calculate the depth of coverage byENCODE transcription factor binding sites in promoters (coverageBed -a ENCODE_Tfbs -bRefSeq_promoters > result). IntersectBed writes the original entry in the file A for each overlap when used with the ‘-wa’option (intersectBed -a ENCODE_Tfbs -bRefSeq_promoters -wa > result). This identified the exacttype of transcription factor binding sites in the overlap. An alternativeanalysis pipeline was constructed using R/BioC packages GenomicRanges and IRanges.However, the R/BioC pipeline provedprohibitively slow even on SNIC Supercomputers. The alternative pipeline wasused only to verify the results of the main BEDtools-based pipeline.The detection of the overlap between promoters and transcription factorbinding sites (the BEDtools stage). Postprocessing was performed inR and Bioconductor (version 2.11). Standard Bioconductor packagessuch as Biodist, gplot, ggplot, rtracklayer, TxDb.Hsapiens.UCSC.hg19.knownGene, and GOstats were used.Parsing, statistical tests, and figure generation  data retrieval andremodeling; (2) the detection of the overlap between promoters and transcriptionfactor binding sites; and (3) parsing, statistical tests, and figure generation.BEDtools analysis was divided intothe four steps listed below.Step (1)BEDtools output into anR list.Parsing BEDtools output in the browser extensibledata (BED) file format was read into R asa data-frame. The data-frame was then remodeled into an R list called resJaccard_ENCODE_Tfbs_substrate. Each element of the list wasindexed with a RefSeq transcript id as the accession key. A vector of ENCODEtranscription factor binding sites was contained within each element of thelist. Transcription factor parsing and creation of the list were performedby the script called env_bed_jaccard_make_1.R. Intermediate results were stored inthe R helper environment named env_promoter_bed_500_res_env.Step (2)The Jaccard index calculation.resJaccard_ ENCODE_Tfbs_substrate. Additionally, a randomizeddataset was generated using sampling without replacement. The randomizeddataset was used to calculate a control distribution of the JI.The JI was calculated using the script Step (3)Additional calculations.env_jaccard_analyse_result_bed_sampled.R and env_jaccard_analyse_random_bed_sampled.R.The following additional variables were calculated: relative frequenciesof different transcription factor binding sites, intersection, union, andthe JI depending on the taxon of duplication. These calculations wereperformed by scripts called Step (4)Figure generation.ggplot2 from intermediate results stored in Rhelper environments.Manuscript figures were generated automatically using The post-R package e1071, an R implementation oflibsvm. Each row of the basic SVM trainingdata-frame was a vector representing the number of Tfbs for each gene; although, wealso considered SVMs trained using promoter GC- and CpG-contents . Low-GCpromoters formed a clearly distinct group from high-GC promoters in termsof average BoE when a correlation between core promoter-GC and BoE wasplotted on a scatterplot .Promoter GC-content was a place marker distinct from isochore GCcontent or GC3. This figure consists of four parts. In the upper-leftpart, we show that isochore GC-content and GC3 corresponded closely toeach other. However, promoter GC-content was quite distinct from theisochore GC-content with a large population of high GC promoters locatedin low-GC chromosomal regions (the upper-right part). There was no simpledirect relationship between methylation and BoE  withfewer than 10 TFs in the promoter (defined as a broad 10 kb window aroundthe TSS). Concerted action of multiple TFs appears necessary forhousekeeping expression. The columns of the table encode as follows:RefSeq, EntrezID, gene symbol, and gene name.Additional file 3: Figure S1.- p). Four measures related to the BoE wereconsidered:  the BoE at the cutoff of 10 TPM,  theBoE at the cutoff of 100 TPM,  the mean expression, and  the maximum expression. The number of transcription factor bindingsites was estimated in four different approaches:  the totalnumber,  the number of unique binding sites,  thetotal number excluding polymerase binding sites, and  thenumber of unique binding sites excluding the polymerase. The red linesignified the linear model for the smoother line, while the blue linesignified the non-linear model. This figure confirms the robustness of thefindings presented in Figure The relationship between the BoE, the mean and the maximumexpression in human primary cells, and the number of transcription factorbinding sites. This figure consists of 16 parts identified as . Four measures related to the BoE were considered:  the BoEat the cutoff of 10 TPM,  the BoE at the cutoff of 100 TPM, the mean expression, and  maximum expression. Thenumber of transcription factor binding sites was estimated in fourdifferent approaches:  the total number,  thenumber of unique binding sites,  the total number excludingpolymerase binding sites, and  the number of unique bindingsites excluding the polymerase. The red line signified the linear modelfor the smoother line, while the blue line signified the non-linear model.This figure confirms the robustness of the findings presented inFigure Additional file 5: Table S3.P values for pairwise BoE comparisonsusing Wilcoxon rank sum test for data in Table P valueadjustment method: holm.Additional file 6: Table S4.P values for pairwise TfbsNo.comparisons using Wilcoxon rank sum test for data in Table P value adjustment method: holm.Additional file 7: Figure S5.Tfbs_x_length), sum of unique sites (Tfbs_x_length_unique), sum of all sites withoutRNA polymerase II (Tfbs_x_length_noPol2), and finally the sum of unique siteswithout the polymerase (Tfbs_x_length_unique_noPol2). The BoE was also transformedin several ways which proved equivalent by forming a tight cluster.Namely, the BoE was encoded as either a continuous variable (marked asbreadth_continuous), discretized intothree bins (breadth_discrete_3),discretized into 10 bins (breadth_discrete_10), or transformed and expressed as asquare root (breadth_sqrt_1).The clustering of the BoE with the number of transcriptionfactor binding sites in human tissues . Transcription factors clustering closest with the BoE weremarked as A. Other transcription factors formed two clusters with low andhigh distance to the BoE . To test the robustness of this analysis, the number oftranscription factors was measured in several different ways, whichreassuringly clustered together and proved indistinguishable. Thedifferent measures were: sum of all sites , sum of unique sites (Tfbs_x_length_unique), sum of all sites withoutRNA polymerase II (Tfbs_x_length_noPol2), and finally the sum of unique siteswithout the polymerase (Tfbs_x_length_unique_noPol2). The BoE was also transformedin several ways which proved equivalent by forming a tight cluster.Namely, the BoE was encoded as either a continuous variable (marked asbreadth_continuous), discretized intothree bins (breadth_discrete_3),discretized into 10 bins (breadth_discrete_10), or transformed and expressed as asquare root (breadth_sqrt_1).The clustering of the BoE with the number of transcriptionfactor binding sites in human tissues (Spearman’s rank correlationcoefficient). Transcription factors clustering closest with the BoE weremarked as A. Other transcription factors formed two clusters with low andhigh distance to the BoE . To test the robustness of this analysis, the number oftranscription factors was measured in several different ways, whichreassuringly clustered together and proved indistinguishable. Thedifferent measures were: sum of all sites  in respect to the BoE and the number of TFs.Because of the large number of rows, this is a supplementary tableavailable as a text file in the supplementary material. NOTE: The fourclasses of transcripts were illustrated in Figure Additional file 10: Figure S3.Tfbs_x_length), sum of unique sites (Tfbs_x_length_unique), sum of all sites withoutRNA polymerase II (Tfbs_x_length_noPol2), and finally the sum of unique siteswithout the polymerase (Tfbs_x_length_unique_noPol2). The BoE was also transformedin several ways which proved equivalent by forming a tight cluster.Namely, the BoE was encoded as either a continuous variable (marked asbreadth_continuous), discretized intothree bins (breadth_discrete_3),discretized into 10 bins (breadth_discrete_10), or transformed and expressed as asquare root (breadth_sqrt_1).The clustering of the BoE with the number of transcriptionfactor binding sites in human primary cells. In human primary cells, theBoE clustered with the polymerase, TFIID, NFKB, HEY1, Sin3A, and c–Myc(this cluster was marked as A). Other transcription factors formed twoclusters with low and high distance to the BoE . To test the robustness of this analysis, thenumber of transcription factors was measured in several different ways,which reassuringly clustered together and proved indistinguishable. Thedifferent measures were: sum of all sites , sum of unique sites (Tfbs_x_length_unique), sum of all sites withoutRNA polymerase II (Tfbs_x_length_noPol2), and finally the sum of unique siteswithout the polymerase (Tfbs_x_length_unique_noPol2). The BoE was also transformedin several ways which proved equivalent by forming a tight cluster.Namely, the BoE was encoded as either a continuous variable (marked asbreadth_continuous), discretized intothree bins (breadth_discrete_3),discretized into 10 bins (breadth_discrete_10), or transformed and expressed as asquare root (breadth_sqrt_1).The clustering of the BoE with the number of transcriptionfactor binding sites in human cancer cell lines. In cancer cell lines, theBoE only clustered with RNA polymerase II and TFIID (this cluster wasmarked as A) suggesting that cancerous transformation disables most normalcontrol switches for the BoE. Other transcription factors formed twoclusters with low and high distance to the BoE . To test the robustness of this analysis, thenumber of transcription factors was measured in several different ways,which reassuringly clustered together and proved indistinguishable. Thedifferent measures were: sum of all sites .The unweighted mean of the BoE of all target genes  was plotted on theY-axis (signified by Mean_target_BoE).As both the independent and dependent variables were highly non-normallydistributed, we used non-parametric correlation (Spearman’s rho = 0.3176). An alternative measure, aweighted mean, in which that data points were weighted according to theactual number of transcription factor binding sites was also consideredand gave almost exactly the same correlation. The blue line is the fittedloess curve.There was a positive correlation between the BoE oftranscription factors and the average BoE of their targets. The BoE oftranscription factors in FANTOM5 tissues was plotted on the X-axis of thescatterplot , withAUC = 0.816.Performance of SVM models as the predictor of tissue-specific expression. Tissue-specific expression was defined asBoE lower than 0.33  and transcripts were categorized as eithertissue-specific or not . This figureconsists of four panels. The panels display standard predictor validationcharts for the basic SVM model (SVM-Tfbs): (a) the ROC curve, (b) theprecision/recall graph, (c) the sensitivity/specificity plot, and (d) thelift chart. The curves were averages from 10 different cross-validationruns. The area under the ROC curve equaled 0.816 . The parameterization of the curves was performed usingthe value of the linear SVM output and visualized by printing cutoffvalues at the corresponding curve positions . The curves were plotted using


Linker histone H1 is a protein component of chromatin and has been linked to higher-order chromatin compaction and global gene silencing. However, a growing body of evidence suggests that H1 plays a gene-specific role, regulating a relatively small number of genes. Here we show that H1.2, one of the H1 subtypes, is overexpressed in cancer cells and contributes to gene silencing. H1.2 gets recruited to distinct chromatin regions in a manner dependent on EZH2-mediated H3K27me3, and inhibits transcription of multiple growth suppressive genes via modulation of chromatin architecture. The C-terminal tail of H1.2 is critical for the observed effects, because mutations of three H1.2-specific amino acids in this domain abrogate the ability of H1.2 to bind H3K27me3 nucleosomes and inactivate target genes. Collectively, these results provide a molecular explanation for H1.2 functions in the regulation of chromatin folding and indicate that H3K27me3 is a key mechanism governing the recruitment and activity of H1.2 at target loci. Genomic DNA in eukaryotic cells is stored in the nucleus via its hierarchical compaction into chromatin. The basic unit of chromatin is the nucleosome, in which approximately 147 base pairs of DNA are wrapped around a core histone octamer composed of H2A, H2B, H3, and H413in vitro studies indicated that H1 binding to chromatin impairs transcription events by stabilizing the nucleosome, controlling nucleosome spacing, and/or folding nucleosome arrays into 30 nm chromatin fiber7910910111213Early Related to the current report, EZH2 is the catalytic subunit of the Polycomb repressive complex 2 (PRC2) that mediates H3K27 trimethylation (H3K27me3) and controls transcriptional activity at target loci16182023Here we identify and characterize H1.2, one of the H1 subtypes, as a new effector protein that recognizes EZH2-mediated H3K27me3 to trigger chromatin compaction and gene silencing in cancer cells. We show that the flexible C-terminal tail is essential for the high affinity binding of H1.2 to H3K27me3-enriched chromatin and the establishment of inactive transcription state. Supporting these results, our transcriptome analyses of cancer cells demonstrate that multiple growth suppressive genes are re-activated upon knockdown of H1.2 and EZH2, and that more than half of the genes up-regulated after H1.2 knockdown are also trans-activated in response to EZH2 knockdown.in vivo. In these experiments, we noticed that ectopic H3 tails undergo dynamic cellular modifications and that linker histone H1 interacts with the modified H3 tails. To determine whether any particular tail modifications are required for the observed interaction with H1, we transfected 293T cells with an empty vector or expression vectors for wild type H3 tails or H3 tails individually mutated at K4, K9, K14, K18, K23 and K27. After confirming that wild type and mutant H3 tails were expressed at similar levels (data not shown), ectopic H3 tails and their bound proteins were purified from nuclear extracts by immunoprecipitation with anti-Flag antibody. Western blotting of wild type H3 tail-associated proteins with H1 antibody detected two bands: a faster migrating band corresponding to H1.0 and a slower migrating band corresponding to H1.1-H1.5 which are replication-dependent somatic linker histones  assays employing chromatin isolated from control cells and cells depleted of H1.2 or EZH2. The precipitated DNA was amplified by quantitative PCR (qPCR) using primers specific for the promoter region (PR), transcription start site (TSS) and coding region (CR) of the target genes, as summarized in Having established the importance of H3K27me3 in H1.2 localization and function, we next wished to determine whether the observed recruitment of H1.2 to target loci affects chromatin structure. To address this issue, we first expressed H1.2 fused to green fluorescent protein (GFP) in MCF7 cells and examined its nuclear localization by fluorescence microscopy. GFP signals for wild type H1.2 were primarily detected in the regions that were heterochromatin-dense, as indicated by the stronger staining with DAPI . By contin situ hybridization (FISH) on MCF7 cells and tested whether H3K27me3 could direct H1.2 to drive chromatin reorganization at the target genes. For these studies, we used the probe 1 that is specific for PGR locus and the probe 2 that spans approximately 182 kb of sequence upstream of the PGR locus . Similar FISH analyses with cells depleted of H1.2 or EZH2 detected larger spatial distance between the PGR gene and upstream region (H1.2 sh and EZH2 sh), which supports the architectural reorganization of chromatin across these regions. Expectedly, however, ectopically expressing wild type, but not mutant, H1.2 and EZH2 proteins restored the original distance between the two probes . Moreover, and further indicative of highly specific functions of H1.2 and EZH2 at the target genes, FISH using non-target gene probes failed to show the effects of knockdown and expression of H1.2 and EZH2 , indicating that H1.2, if used at non-physiological concentrations, may reorganize overall chromatin structure and leads to non-specific repression. On the other hand, H1.2 is able to abrogate transcription from H3K27me3 nucleosome arrays at a ratio of one H1.2 per nucleosome, consistent with its ability to bind H3K27me3 nucleosomes. Because H1 proteins interact dynamically with chromatin, continuously exchanging between nucleosomes39in vitro system occurs independently of PRC1 complex. Hence, although H3K27me3 is known to recruit PRC1 for chromatin condensation and gene silencingAn unsolved question in our study is how H1.2 plays such a specific role in H3K27me3-mediated gene silencing. The C-terminal tail of H1 appears to adopt a distinct secondary structure upon binding to nucleosomes for the continuous control of chromatin structure4142Misregulation of H1 expression and EZH2 function has been linked to tumorigenesis232432441346Interestingly, we also found that the ability of EZH2 to methylate target nucleosomes is dependent on chromatin structure; comparison of levels of H3K27me3 showed a dramatic decrease in H3K27me3 at the target genes after H1.2 knockdown that makes chromatin less compact. Consistent with our results, a recent study showed that the enzymatic activity of EZH2 in PRC2 complex is stimulated by chromatin condensationIn addition to the transrepressive function of H1.2 shown here, our previous study using 293T cells linked H1.2 to transcriptional activation, especially at the level of elongationSee All the details regarding the preparation of recombinant histone proteins and histone tail peptides can be found in Mononucleosome and nucleosome arrays were reconstituted by salt gradient dialysis and purified by glycerol gradient centrifugation, as described in For shRNA-based gene knockdown, DNA oligonucleotides encoding shRNAs specific for H1.2 mRNA coding region (5′-CCGAAGAAGAGCGCTAAGAAA-3′), EZH2 mRNA 3′ UTR region (5′-GAAACAGCTGCCTTAGCTTCA-3′) and H1.4 mRNA coding region (5′-CCAAGAAGAGCGCCAAGAAGA-3′) were annealed and ligated into the lentiviral expression vector pLKO.126Binding experiments using synthetic H3 peptides and reconstituted nucleosomes were performed as explained in detail in in vitro transcription assays. Typical transcription reactions contained Gal4-VP16 (15 ng), p300 (20 ng), AcCoA (10 μM) and/or H1.2 (100 ng). The radiolabeled transcript products from reactions were resolved on 5% polyacrylamide gels containing 7 M urea and detected by autoradiography.G5ML601 nucleosome arrays (100 ng) containing semisynthetic H3K27me0 or H3K27me3 were used for A detailed description of restriction enzyme and micrococcal nuclease digestion assays using reconstituted nucleosomes can be found in Standard procedures were used, and details can be found in 2, 0.5 mM DTT, and 0.5 mM PMSF). After stopping digestion reactions by adding 1/10 vol of solution containing 250 mM EDTA and 5% SDS, samples were incubated at 65 °C for overnight to reverse the cross-linking. The DNA was cleared of protein/RNA by proteinase K/RNase H digestion and phenol extraction before ethanol precipitation. The enrichment of MNase digested DNA over 1.5 kb upstream or 2 kb downstream of TSS was determined by qPCR with the primers listed in GAPDH that does not show any altered expression after H1.2 and EZH2 knockdown, serving as a control region unchanged in MNase protection between experiments. The nuclease accessibility data were analyzed using EpiQ chromatin kit data analysis tool (www.bio-rad.com/epiq).Assays were carried out as previously describedThe salt extract experiments were performed as describedCells were fixed with 4% paraformaldehyde for 10 min and permeabilized with 0.5% TritonX-100/PBS for 20 min. After washing with PBS, slides were incubated with 100 ug/ml RNase A for 1 h and 40 units/ml pepsin for 10 min at 37 °C, and dehydrated for 2 min each in 70%, 80% and 100% ethanol. Slides were denatured in 70% formamide for 5 min at 73 °C. DNA probes were prepared and labeled with rhodamine-dUTP and fluorescein-dUTP. Probes were denatured for 5 min at 73 °C and hybridized to the denatured slides for 24 h at 37 °C. Slides were washed for 5 min each in 50% formamide/2x SSC and 0.1% Tween 20/2x SSC at 42 °C, dehydrated for 2 min each in 70%, 80% and 100% ethanol, and mounted on glass slides. FISH signals were detected by a Zeiss imager Z1 Apotome Microscope. DNA probes used in these assays were the BAC RP11–599H8 (155 kb), RP11–942D19 (182 kb), BAC RP11–956H14 (185 kb) and RP11–96K4 (168 kb). Interprobe distances under each condition were calculated as described previouslyMCF7 cells were incubated with 0.5 mg/mL MTT  (Sigma) for 2 h at 37 °C. The MTT formazan was dissolved in 200 μL of DMSO. The absorbance of the solution was quantified at 570 nm against 650 nm by a microplate reader (Bio-Rad).2, 0.02% NP40, and 1 mM DTT). After centrifugation at 25,000 rpm for 5 h, and the fractions (150 μl) were analyzed by 1% agarose gel.Reconstituted nucleosome arrays were incubated with wild type or mutant H1.2 for 1 h, and were loaded on a 15%–40% glycerol gradient .


Pax6 in the differentiating RPE revealed that PAX6 functions in a feed-forward regulatory loop with MITF during onset of melanogenesis. PAX6 both controls the expression of an RPE isoform of Mitf and synergizes with MITF to activate expression of genes involved in pigment biogenesis. This study exemplifies how one kernel gene pivotal in organ formation accomplishes a lineage-specific role during terminal differentiation of a single lineage.During organogenesis, PAX6 is required for establishment of various progenitor subtypes within the central nervous system, eye and pancreas. PAX6 expression is maintained in a variety of cell types within each organ, although its role in each lineage and how it acquires cell-specific activity remain elusive. Herein, we aimed to determine the roles and the hierarchical organization of the PAX6-dependent gene regulatory network during the differentiation of the retinal pigmented epithelium (RPE). Somatic mutagenesis of  Mitf and importantly, at the same time, PAX6 functions together with MITF to directly activate the expression of downstream genes required for pigment biogenesis. These findings provide comprehensive insight into the gene hierarchy that controls RPE development: from a kernel gene (a term referring to the upper-most gene in the gene regulatory network) that is broadly expressed during CNS development through a lineage-specific transcription factor that together with the kernel gene creates cis-regulatory input that contributes to transcriptionally activate a battery of terminal differentiation genes.It is currently poorly understood how a single developmental transcription regulator controls early specification as well as a broad range of highly specialized differentiation schemes. PAX6 is one of the most extensively investigated factors in central nervous system development, yet its role in execution of lineage-specific programs remains mostly elusive. Here, we directly investigated the involvement of PAX6 in the differentiation of one lineage, the retinal pigmented epithelium (RPE), a neuroectodermal-derived tissue that is essential for retinal development and function. We revealed that PAX6 accomplishes its role through a unique regulatory interaction with the transcription factor MITF, a master regulator of the pigmentation program. During the differentiation of the RPE, PAX6 regulates the expression of an RPE-specific isoform of  The retinal pigmented epithelium (RPE) is a monolayer of polarized and highly specialized pigmented cells that are located between the outer segments of the photoreceptors and the choroid layer in the eye. This strategic location demands multiple functions of the RPE during the development and homeostasis of the adjacent tissues, the neuroretina and choroid Most pigmented cells in the body originate from the neural crest. In contrast, the RPE is derived from the neural epithelium of the optic vesicles (OV), which are lateral protrusions of the ventral forebrain. The OV undergo patterning and morphogenesis to give rise to the bilayer optic cup (OC) with an inner layer of retinal progenitor cells and an outer layer populated by the progenitors of the pigmented epithelium. The distal regions of the OC differentiate into the epithelial layers of the ciliary body and iris Mitf gene encodes a family of at least 10 distinct isoforms generated from a common gene by alternative promoter/exon usage M-Mitf is expressed in the neural crest derived pigment cells A-Mitf, H-Mitf and D-Mitf are highly expressed in the developing RPE where they are equally distributed Mitf isoforms M, A, H and D contain different non-coding and coding 5′ sequences and the corresponding proteins thus differ in their N-terminal sequences Extrinsic cues trigger the expression and activity of intrinsic factors that execute the differentiation program. A pivotal intrinsic mediator of the RPE fate is microphthalmia-associated transcription factor (MITF), a member of the basic-helix-loop-helix leucine zipper family known to be essential for melanin-bearing pigment cells across species and tissue types M-Mitf isoform Pax3 is not expressed, data suggest that PAX6 plays a role in regulating the onset of Mitf expression Pax6, the homolog of the eyeless (ey) gene in Drosophila, is pivotal for development of eye cell types derived from the neuroepithelium or from the surface ectoderm , RPE progenitors begin to accumulate melanin DctCre activity and the location of the loxP sequences, the PAX6 paired domain was lost from the optic cup as evident from labeling with an antibody that specifically identifies the N-terminus of PAX6 , as well as factors involved in melanosome biogenesis  or melanosome transport (RAB27a) and factors implicated in melanosome biogenesis . Corresponding to the phenotype observed, the transcriptome analysis indicates an arrest in the melanogenesis program following Pax6 loss.To determine the global change in gene expression following Tyr, Tyrp1, Si and Mlana were significantly reduced in the mutant RPE as compared with control, whereas the level of the mRNA encoding the enzyme DCT, which is involved in melanin synthesis, was slightly reduced, and the level of Myo7a mRNA, which encodes a protein involved in cellular transport of melanosomes in the RPE Si transcript  in Pax6-deficient RPE compared to the wild-type. By indirect immunofluorescence (IIF) analysis we indeed detected reduced levels of MITF protein in the RPE of loxP/loxP;DctCrePax6 embryos as compared with control loxP/loxPPax6 embryos at E12.5 and at E15.5 (Mitf isoforms by QRT-PCR of RNA extracted from control and Pax6-deficient RPE (E15.5). This analysis revealed a significant reduction in the expression of D-Mitf, which was over 3-fold lower in the mutants, and slight elevations in levels of A-Mitf and H-Mitf in the mutant RPE, compared to wild-type   was performed using different combinations of Pax6, Pax6ΔPD and Mitf expression vectors  . An elecin vitro . This biin vitro . Lucifer vectors . This anD-Mitf truncated promoters was analyzed . These findings suggest that in tissue culture, the MITF binding sites are essential and sufficient for the transactivation of mTyrp1and hTyr promoters by PAX6 and MITF.We next examined the contributions of the putative binding sites of PAX6 and the binding sites of MITF (M- and E-box) to the transactivation of the promoters of 3.5 fold . Similarectively . In contand PAX6 . To examPax6 and MitfThe reporter assays revealed that PAX6 transactivation effects are largely dependent on MITF expression and on its binding sites. This mode of action suggests a physical interaction between PAX6 and MITF. We therefore conducted co-immunoprecipitation assays (co-IP) to evaluate this possibility. First, a reciprocal co-IP experiment was performed in ARPE19 cells that endogenously express both mTyrp1 promoter by MITF and PAX6  and Gli2 (p = 0.003) and of the retinal promoting gene Msx2 (p = 0.016). These alterations in gene expression suggest partial changes in the differentiation program of the Pax6-deficient RPE and point to additional regulators of MITF that are controlled by PAX6. However, these changes were not sufficient to completely disrupt RPE differentiation, in contrast to the complete disruption observed following inhibition of the Wnt/β-catenin pathway in specified RPE A-Mitf isoform in vitroPax6 during RPE differentiation resulted in up-regulation of A-Mitf. These findings suggest stage-dependent roles for PAX6 during various stages of RPE development, from pattering to differentiation.A role for ithelium . Accordiithelium  or elevaithelium  in the PPax6 is essential for the proper expression of Mitf and its melanogenic target genes. This activity requires both PAX6 and MITF to act synergistically, as shown by luciferase reporter assays on the promoters of mD-Mitf, mTyrp1 and hTyr , and this minor down-regulation may have also contributed to the overall reduction in pigmentation, as TFEC is capable of trans-activating mTyrp1 and hTyr promoters alone and synergistically with PAX6 . However, in that study transfection of the two proteins caused a reduction in MITF transactivation of Tyr promoter Pax6/Mitf levels used in the reporter assays, as Planque et al. used a ratio of 20∶1, whereas in our study the ratio was 1∶1. The importance of the ratio between PAX6 and MITF has also been demonstrated in vivo. The transcript levels of Tyr and Tyrp1 were lower in mice that over-express Pax6 (i.e. Yac/YacPax6 mice) on the background of a ΔD/ΔDMitf transgenic mice compared to either Yac/YacPax6 or ΔD/ΔDMitf alone Tyr and Tyrp1  was performed on 14 µm cryo-sections using DIG-labeled RNA probes as previously described Pax6 intron 7 probe was generated from a 849bp PCR fragment . Plasmid for antisense transcription of silver was kindly provided by the laboratory of Dr. William Pavan (NIH) Flat-mount samples were prepared as follows: Eyes at E19.5 were enucleated and immediately fixed in 4% paraformaldehyde for 30 minutes. The RPE was carefully dissected from the rest of eye structures, sliced radially to four pieces and flattened on membrane filters . Samples were blocked and stained with phalloidin . Thereafter, RPE was flattened on its basal side on a slide and sealed for observation.The heads of E15.5 embryos and perforated eyes of P1 neonatal mice were fixed in 0.1 M cacodylate-buffered fixative containing 2.5% paraformaldehyde and 2% glutaraldehyde and further processed as described previously loxP/loxP;DctCrePax6 male with loxP/loxPPax6 females. Pregnant females were harvested on day E15.5 and the RPE of the embryos was dissected as previously described http://www.ncbi.nlm.nih.gov/geo) under series accession no. GSE56548.Exact timed matings were performed by overnight cohabitation of an inbred Ppia and Tbp, after verifying that their levels were consistent in normal and mutant RPE. Raw data was processed using the comparative Ct method by the formula 2−ΔΔCT. Each amplification reaction was performed in triplicate using 20 ng of cDNA for each sample. Primers used to amplify and sequence the two Pax6ΔPD transcript variants are listed in Reverse transcription of 1 µg of RNA from each sample was performed using the SuperScript III First Strand kit (Invitrogen). cDNA was amplified using the Power SYBR Green Mix (Applied Biosystems) in a 384-well optical reaction plate using ABI Prism 7000 Sequence Detection System (Applied Biosystems). All primer pairs were first tested for specificity and amplicon size using end-point PCR. Formation of a dimer structure was refuted by analyzing the dissociation curve at the end of each amplification reaction. Results were calibrated in relation to an average of two house-keeping genes, Pax6, Pax6ΔPD or A-Mitf, 400 ng of each; 3) 10 ng normalizing vector (pRL-TK). Cells were harvested 48 hours after transfection and luminescence was evaluated. Each treatment was carried out in duplicate, and each assay was repeated at least three times.Reporter assays were performed in HeLa cells using the Dual-Luciferase Reporter Assay System (Promega). Cells were seeded in a 24-well plate and 24 hours later were transfected using jetPEI DNA transfection reagent (Polyplus-transfection). Each well was co-transfected with three types of vectors in a total amount of 1210 ng of DNA: 1) 400 ng of a luciferase reporter vector (pGL3 basic) under the regulation of the examined promoter; 2) A total of 800 ng of expression vector (p3XFlag-CMV-10), either carrying no insert or containing an insert encoding the ORF of DpnI restriction enzyme (Fermentas) for 1 hour at 37°C, and 5 µl of the DNA was transformed into E. coli XL-1Blue strain, followed by colony-picking mini-prep and midi-prep plasmid purification (Qiagen). All plasmids were verified by sequencing.End-point PCR of 17 cycles was performed using oligonucleotides containing the desired mutated sites  containing protease inhibitor  and incubated on ice for 30 minutes. Extracts were clarified by centrifugation at 10,000 g for 15 minutes at 4°C. To avoid nonspecific binding of proteins to the beads, extracts were subjected to pre-clearing using 15 µl of protein A agarose beads  for 2 hours at 4°C, followed by centrifugation at 10,000 g for 1 minute at 4°C. Input samples (50 µl of the supernatant) were kept at −20°C for input analysis and the cleared extracts were incubated with 5 µl of rabbit anti-MITF , scraped in 1 ml lysis buffer . Antibodies used for IP were either rabbit anti-PAX6  or rabbit anti-MIT . Mouse anti-PAX6  and mouse anti-MITF  or non-immune rabbit IgG (Rockland) as a negative control. The primers used for ChIP analysis are listed in ChIP was performed as previously described Pax6. Nuclear extracts were obtained as previously described 2, 0.4 mM DTT, 2 mg polydI/dC (Sigma). Binding with 1 ml double-stranded 59-c-ATP-labeled probe  was performed at room temperature for 20 minutes and 200 ng of ‘‘cold’’ PAX6 consensus site (PAX6CON) was used for competition HEK-293T cells were transfected with p3XFlag-CMV-10 encoding the ORF of full-length Figure S1loxP/loxP;DctCrePax6 mutant mice. Paraffin sections of (A-D) control, loxP/loxPPax6, and (E-H) mutant, loxP/loxP;DctCrePax6, eyes stained for the N-terminus  and C-terminus  of PAX6. (A'-H' insets) Higher magnifications of indicated regions and nuclear staining with DAPI. (E-H') PAX6PD is not detected in the RPE of loxP/loxP;DctCrePax6 mutants at E12.5, E15.5 or E19.5 (red). Nevertheless, a PAX6ΔPD polypeptide is detected in the loxP/loxP;DctCrePax6 RPE (green). The spatiotemporal expression pattern of PAX6 isoforms is similar in wild-type and loxP/loxP;DctCrePax6 RPE:  at E12.5, PAX6 isoforms are expressed in the entire RPE;  at E15.5, PAX6 isoforms are highly expressed in the distal RPE;  and at E19.5 PAX6 isoforms are expressed only in the distal most cells of the RPE.  The boundary region along the RPE, where the expression of PAX6 isoform is gradually reduced, is shown in higher magnifications. Scale bar is 50 µm.A PAX6ΔPD protein is expressed in the RPE of (TIF)Click here for additional data file.Figure S2Pax6 gene structure and transcripts expressed in the RPE of loxP/loxP;DctCrePax6 mutant and control mice. (A) A scheme of Pax6ΔPD transcript expressed under the regulation of the P4 promoter. Striped rectangle indicates the location of the ISH probe used to identify the expression pattern of the Pax6ΔPD transcribed from promoter P4. (B) A scheme of the abnormal Pax6ΔPD transcript that is expressed in the RPE of loxP/loxP;DctCrePax6 mice. Coding exons are marked with large rectangles and non-coding exons are marked with small rectangles. The PD and HD coding exons are marked in light blue and blue, respectively. Locations of primers that were used to sequence the two Pax6ΔPD variants are marked with red arrows. Locations of possible start codons for the Pax6ΔPD transcript variants are indicated by ATG codons. (C) PCR products generated using primers designed to ampliy a Pax6 intron 7 to exon 9 fragment, suggestive of the product shown in panel A. (D) PCR products generated using primers designed to ampliy the a Pax6 exon 3 to exon 8 fragment, suggestive of the product shown in panel B. (E) A view of the distal OC of cryo-section subjected to in situ hybridization with a probe corresponding to Pax6 intron 7. Scale bar is 50 µm. (F) Transcript levels of Pax6 exons 7-8 in the RPE of control loxP/loxPPax6 and mutant loxP/loxP;DctCrePax6 mice at E15.5.(TIF)Click here for additional data file.Figure S3Otx2 and Sox9 was similar in wild-type and loxP/loxP;DctCrePax6 mice. (A) Relative transcript levels of Otx2 and Sox9 in RPE fractions from wild-type and mutant mice determined using QRT-PCR (n = 5). (B-E) Distal OC view of paraffin sections labeled with antibodies against  OTX2 and  SOX9. Scale bar is 25 µm. Only few cells in the distal most region of the RPE of loxP/loxP;DctCrePax6 do not express OTX2 . The expression of SOX9 in a proximal to distal gradient is maintained in the RPE of loxP/loxP;DctCrePax6 mutants.  The boundary region along the RPE, where the expression of SOX9 gradually increases.Expression of the RPE transcription factors (TIF)Click here for additional data file.Figure S4Mitf gene locus, the deleted DNA segment in the ΔD/ΔDMitf transgenic mice and the different MITF isoforms expressed in the RPE and choroid. (A) Scheme of Mitf gene structure presenting only the exons that constitute the four main isoforms expressed in the RPE  and choroid (M-Mitf). The alternative transcription (TSS) and translation (ATG) start sites are indicated. White rectangles mark untranslated regions and colored rectangles mark coding sequences. A graphic scheme of all known Mitf isoforms and their unique alternative first exons is presented in Bharti et al. (2008) ΔDMitf transgenic allele, a 5.8 kb DNA fragment that includes exon D (25bp) and covers a region of ∼200bp downstream and ∼5.6 kb upstream to the D-Mitf TSS was replaced by a neomycin cassette Schemes of the mouse (TIF)Click here for additional data file.Figure S5mMlana in the presence of MITF. Activity of luciferase under the regulation of the mMlana promoter co-transfected into HeLa cells along with different combinations of expression vectors and/or their backbones lacking the ORF, as indicated (n = 3). The positions of binding sites for MITF  and potential binding sites for PAX6 (light blue rectangles) are indicated relative to the TSS.PAX6 trans-activates the promoters of (TIF)Click here for additional data file.Figure S6Tyrp1 promoter co-transfected into HeLa cells along with different combinations and amounts of expression vectors and/or their backbones lacking the ORF, as indicated. PAX6ΔPD had no effect on PAX6-MITF mediated transactivation of the mTyrp1 promoter.The PAX6ΔPD variant is capable of association with MITF but probably does not take part in the MITF-mediated transcriptional activation of melanogenic genes. (A) co-IP of MITF and PAX6 or PAX6ΔPD. HeLa cells were transfected with vectors expressing 3xFlagPax6, 3xFlagMitf, 3xFlagPax6ΔPD or their combinations, as indicated. Cell lysates were prepared (input) and immunoprecipitated with anti-MITF antibodies. Samples were subjected to SDS-PAGE and analyzed by immunoblotting with anti-FLAG antibody . Both full-length PAX6 and PAX6ΔPD were found to be in association with MITF (lanes 9-10). (B) Activity of luciferase under the regulation of wild-type m(TIF)Click here for additional data file.Figure S7Tfec transcript level in the RPE of loxP/loxP;DctCrePax6 mice may contribute to the reduction in pigmentation. (A) Activity of luciferase under the regulation of wild-type mTyrp1 promoter .The slight reduction in (TIF)Click here for additional data file.Table S1Pax6-deficient RPE relative to wild-type RPE according to the microarray results.Genes differentially expressed in the (DOCX)Click here for additional data file.Tables S2mD-Mitf promoter (from +6 to −1153 relative to the TSS).Putative MITF and PAX6 binding sites in (DOCX)Click here for additional data file.Tables S3mTyrp1 promoter (from +5 to −252 relative to the TSS).Putative MITF and PAX6 binding sites in (DOCX)Click here for additional data file.Tables S4hTyr promoter (from +80 to −115 relative to the TSS).Putative MITF and PAX6 binding sites in (DOCX)Click here for additional data file.Tables S5mMlana (from +6 to −1153 relative to the TSS).Putative MITF and PAX6 binding sites in promoter of (DOCX)Click here for additional data file.Table S6Primers used in this study.(DOCX)Click here for additional data file.Text S1Supporting references.(DOCX)Click here for additional data file.


CASP9 and CYP26A1 genes, 15 and 45 min following RA exposure, and we found that histone H3 lysines 4 and 9 are demethylated by the lysine-specific demethylase, LSD1 and by the JMJ-domain containing demethylase, D2A. The action of the oxidase (LSD1) and a dioxygenase (JMJD2A) in the presence of Fe++ elicits an oxidation wave that locally modifies the DNA and recruits the enzymes involved in base and nucleotide excision repair (BER and NER). These events are essential for the formation of chromatin loop(s) that juxtapose the RARE element with the 5′ transcription start site and the 3′ end of the genes. The RARE bound-receptor governs the 5′ and 3′ end selection and directs the productive transcription cycle of RNA polymerase. These data mechanistically link chromatin loops, histone methylation changes and localized DNA repair with transcription.Histone methylation changes and formation of chromatin loops involving enhancers, promoters and 3′ end regions of genes have been variously associated with active transcription in eukaryotes. We have studied the effect of activation of the retinoic A receptor, at the RARE–promoter chromatin of  Retinoic acid (RA), an active derivative of vitamin A, plays a role in the regulation of embryonic development, homeostasis and differentiation of adult tissues. RA metabolites, collectively known as retinoids, are well-characterized inhibitors of cancer cell proliferation or inducers of stem cell differentiation. The biological activity of RA is mediated by its binding to RA receptors  that function as hetero-dimers with retinoid X receptors (RXRs), targeting DNA at specific sites, known as RA responsive elements (RAREs). Following hormone binding, an active receptor complex induces covalent modifications at the N-terminal tails of nucleosomal histones and assembles an active transcription complex on chromatin .Despite extensive studies on RA-induced transcription, it is not known if there is a common set of histone modifications or how the initiation transcription complex is assembled on regulatory regions. The H3 methylation changes reported so far associated with activation of the receptor(s) by RA, may be secondary to repression of transcription  or inducCASP9) and Cyp26A1 (CYP26A1). CASP9 contains a functional RARE located 9.5 kb downstream of the transcription start site (TSS)  , whereas2 in Dulbecco's modified Eagle's medium (DMEM) supplemented with phenol red, L-glutamine (2 mM), insulin (10 μg/ml), hydrocortisone (3.75 ng/ml) and 10% fetal bovine serum (FBS) (Invitrogen). Non-tumorigenic breast MCF10-2A cells were grown in a 1:1 mixture of DMEM and Ham's F12 medium supplemented with 20-ng/ml epidermal growth factor, 100-ng/ml cholera toxin, 0.01-mg/ml insulin and 500-ng/ml hydrocortisone, 95%; horse serum, 5%. Cells were provided with fresh medium every 3 days. To evaluate the effect of RA, cells were grown in phenol red-free medium containing 10% dextran-charcoal-stripped FBS for 1–3 days, before being challenged with 300-nM RA for different times according to the experimental protocol.Human breast cancer MCF-7 cells were grown at 37°C in 5% COLSD1 knock down with siRNA, cells were transiently transfected, using a Neon® Transfection System, with siRNA in medium without serum to a final concentration of 10 nM and incubation was continued for 48 h. Scrambled RNA, at the same concentration, was used as negative control. The same procedure was used to obtain JMJD2A, OGG1 and APE1 knock down with the specific siRNAs . To rescue LSD1 activity in knock down experiments with siRNAs, LSD1 full-length cDNA was inserted into the CMV 3xFLAG expression vector (Sigma-Aldrich). LSD1ALA mutant has been described elsewhere .To obtain lsewhere ,8. To reTotal RNA was extracted using TRIzol (Gibco/Invitrogen). cDNA was synthesized in a 20-μl reaction containing 1 μg of total RNA, 100 U of Superscript III Reverse Transcriptase (Invitrogen) and 2 μl random hexamer (20 ng/μl) (Invitrogen). mRNA was reverse-transcribed for 1 h at 50°C, and the reaction was heat inactivated for 15 min at 70°C. The products were stored at −20°C. Quantitative reverse transcription polymerase chain reactions (PCRs) and quantitative PCRs (qPCRs) were performed on a 7500 Real Time PCR System (Applied Biosystems) using the SYBR Green-detection system . The complete list of oligonucleotides used is reported in Supplementary Table S1.6 for each antibody) were fixed for 10 min at room temperature by adding 1 volume of 2% formaldehyde to a final concentration of 1%; the reaction was quenched by addition of glycine to a final concentration of 125 mM. Fixed cells were harvested and the pellet was resuspended in 1 ml of Lysis Buffer  containing 1X protease inhibitor cocktail (Roche Applied Science). The lysates were sonicated to have DNA fragments 300–600 bp. Sonicated samples were centrifuged and supernatants diluted 2-fold in the chromatin immunoprecipitation (ChIP) buffer . An aliquot (1/10) of sheared chromatin was further treated with proteinase K, extracted with phenol/chloroform and precipitated to determine DNA concentration and shearing efficiency (input DNA). The ChIP reaction was set up according to the manufacturer's instructions. Briefly, the sheared chromatin was precleared for 2 h with 1 μg of non-immune IgG (Santa Cruz Biotechnology) and 20 μl of Protein A/G PLUS-Agarose (Santa Cruz Biotechnology) saturated with salmon sperm (1 mg/ml). Precleared chromatin was divided in aliquots and incubated at 4°C for 16 h with 1 μg of the specific antibody  and non-immune IgG, respectively. The immunocomplexes were recovered by incubation for 3 h at 4°C with 20 μl of protein-A/G agarose, beads were washed with wash buffers according to the manufacturer's instructions and immunoprecipitated DNA was recovered and subjected to qPCR using the primers indicated in the legend of the specific figures, primers sequences and qPCR protocols are described in Supplemental Information and in Supplementary Table S1.Cells were transfected and/or treated as indicated in the legends of the figures. The cells . The slides were then washed three times with Tris-buffered saline (TBS)/Tween-20 and permeabilized by serial washes in methanol solutions, prior to be washed with TBS/Tween-20, blocked for 1 h at 37°C and incubated with fluorescein isothiocyanate-labeled protein, that binds 8-oxo-dG, for 15 h at 4°C . Cover slips were mounted in Moviol and viewed by fluorescence. To obtain LSD1 knock down, cells were transfected with specific or control siRNAs. After 48 h, cells were subjected to different treatments, according to experimental needs, and processed for fluorescence microscopy. For single-cell transfection assays, cells were co-transfected with BLOCK-iT Alexa Fluor® Red Fluorescent Control. The efficiency of transfection was 65%+10. All images were captured with Axiocam microscopy (Zeiss) with a 63x objective in the same conditions of brightness and contrast.For 8-Oxo-7, 8-dihydro-2′-deoxyguanosine (8-oxo-dG) detection, 106) were crosslinked in 12 ml of PBS with 1% formaldehyde for 10 min at room temperature. The reaction was quenched by addition of glycine to a final concentration of 125 mM. Fixed cells were harvested and the pellet resuspended in 1 ml of ice-cold lysis buffer (the same used for ChIP experiments). Nuclei were washed with 0.5 ml of restriction enzyme buffer, centrifuged and resuspended in 100 μl of restriction enzyme buffer. Sodium dodecyl sulphate (SDS) was added to a final concentration of 0.1%, and nuclei were incubated at 37°C for 15 min. Triton X-100 was added to a final concentration of 1% to sequester SDS. Digestion was performed with 100 U of restriction enzyme at 37°C for 16 h. The restriction enzyme was inactivated by addition of SDS to 2% and incubation at 65°C for 30 min. The reaction was diluted into 1-ml ligation reaction buffer  and incubated at 16°C for 18 h with 50 U of T4 DNA Ligase (Roche Applied Science). Ethylenediaminetetraacetic acid (10 mM) was added to stop the reactions. Samples were treated with Proteinase K (200 μg/ml) and incubated for 5 h at 55°C and then overnight at 65°C to reverse the formaldehyde crosslinks. The following day, the DNA was purified by phenol/chloroform extraction and ethanol precipitation. Samples were redissolved in 20 μl of Tris-EDTA (TE) buffer. To prepare a control template, we used a pool of plasmids containing an equimolar amount of the CASP9 or CYP26A1 inserts spanning the genomic regions of interest. Five micrograms of plasmid DNA were digested with NcoI in 50 μl of 1x buffer for 8 h at 37°C and then ligated in 20 μl with 5 U of T4 Ligase at 16°C for 4 h. The efficiency of digestion at the end of 3C treatment was quantified by real-time PCR, amplifying a fragment spanning two NcoI (uncut) in different 3C DNA preparations. Primer sequences and positions and details of PCR and qPCR protocols are described in Supplemental Information and Supplementary Table S1. PCR products were separated on 1.2% agarose gels, stained with ethidium bromide and quantified with the imageJ program . The amplified fragments at the end of the procedure were verified by DNA sequence analysis.The chromosome conformation capture (3C) assay was performed as described  with minn ≥ 9). Statistical significance between groups was determined using Student's t-test (matched pairs test or unmatched test was used as indicated in figure legends). All tests were performed using the JMP Statistical Discovery™ software by SAS.All data are presented as mean ± standard deviation in at least three experiments in triplicate , their levels reached a maximum 4 h after RA exposure. In CASP9, a RARE element is localized in intron II, 9.5 kb downstream of the TSS  and another site at the 3′ end close to the polyA  , which function as hetero-dimers with RXRs, targeting specific sites (RAREs). We have used breast cancer cells MCF-7, because they express RARs and respond to RA . We studS Figure . CYP26A1e and f) .CASP9 and CYP26A1 by ChIP. We included in our analysis very early times after RA induction (min) to detect the earliest chromatin changes induced by the hormone. Figure CASP9. We noticed that the levels of RARα recruited to the promoter and RARE chromatin were not stable, but oscillated between 15 and 60 min after RA exposure. RARα and RXRα were also recruited to the RARE/promoter of CYP26A1 . RARα did not accumulate to the RARE or promoter of CASP9 gene in unresponsive MCF10 cells . Recruitment of RARα and RXRα was associated with accumulation of total and serine 5-phosphorylated RNA polymerase II  at the RARE and promoter regions of CASP9 and CYP26A1 . mRNA levels of four other genes induced by RA display a similar pattern with a small peak at 30–60 min and a large peak at 240 min after RA (not shown). In CASP9 normalization of p-Pol II with total Pol II shows that this ratio increases dramatically 30 min after RA at the promoter, while it is stable at the RARE . MCF10 cells, which do not respond to RA . We conclude that the polyA1 and polyA2 sites undergo methylation changes similar to those seen at the promoter and RARE, raising the possibility that these regions are functionally and physically associated in a unique chromatin domain.Methylation of lysine 4 in histone H3 (H3K4) marks transcribed loci, whereas dimethyl-lysine 9 in the same histone H3K9me2) is associated with transcription silencing . To find is assocI Figure . A similCASP9 and CYP26A1 genes. Supplementary Figure S3 shows that the recruitment of these proteins to these sites following RA induction mirrors the demethylation–methylation cycles described above. SUV39H1 accumulates at the promoter, RARE and polyA addition sites with a sharp peak 30 min following RA exposure . The SUV39H1 recruitment overlaps with the peak at 30 min of bi- and tri-methylated H3K9 in Figure CASP9 and CYP26A1 genes. The earliest temporal event we are able to detect following RA exposure is the massive demethylation of H3K4 and H3K9 induced by RA at 15 min. This suggests that K4 and K9 demethylation enzymes are also recruited to CASP9 and CYP26A1. H3K9me3 can be demethylated by enzymes of the Jumonji C (JMJC) family  ,17 Supp,19 (Supp) family ,21, wherin vitro  and H3K9. Knock down of either demethylase, as shown by retention of di- and tri-methylated H3K4 and H3K9, significantly reduced RA-induced expression of CASP9 . Re-expression of the wild-type LSD1 or JMJD2A and not a mutant form of these enzymes restored RA-induced CASP9 RNA levels  of LSD1 (LSD1ALA). This mutant is still enzymatically active, but is unable to target transcription factors ,8. The Le Figure  because CASP9 and CYP26A1 and demethylation of H3K4me2 and H3K9 me2/me3 at the RARE, promoter and polyA2 sites. These localized demethylation events by both demethylases are essential for the induction of transcription of CASP9 and CYP26A1 by RA  and a FAD oxidase (LSD1) at the same chromatin sites (ERE or Ebox-promoters) triggers local oxidation. Oxidized guanine (8-oxo-dG) is recognized by OGG1 . RA induces the binding of OGG1 to the ERE and promoter sites with the same timing of nuclear 8-oxo-dG accumulation  indicating that 8-oxo-dG accumulates at specific segments of RARE, promoter and polyA addition sites. In fact, OGG1 recruitment was not seen in non RA-induced genes or in segments distant (1–2 kb) from the TSSs of CASP9 or CYP26A1 .It was recently reported that nucleotide excision repair (NER) enzymes are recruited to promoter and RARE elements. NER is essential for the formation of discrete chromatin loops and induction of transcription by RA . Similarescribed ,29. Acti guanine -oxo-dG i by OGG1 . That ox9 Figure  15 min fAPurinic site Endonuclease1, APE1, which recognizes the apurinic site generated by OGG1 and cleaves the phosphodiester backbone, immediately 5′ to the site; (ii) thymine DNA glycosylase (TDG), which is required for BER of deaminated methylcytosine, a frequent product of base oxidation; and (iii) uracil glycosylase (UNG), which removes uracil or oxidized cytosine. Figure CYP26A1) show that all these enzymes are recruited to the promoter and to RARE chromatin 15 min following RA induction similar to the recruitment of RARα and Pol II  and for RPA, a single strand binding protein (Replication Protein A) involved in NER, on RARE by ChIP. XPG and RPA selectively accumulated at RARE chromatin following 15 min of RA stimulation Figure . As expeCASP9 and CYP26A1 promoter, RARE and polyA addition sites undergo similar changes in histone H3K4 and H3K9 methylation and accumulate BER and NER enzymes after RA treatment. This coordination is consistent with the idea that these regions are physically associated after RA induction. Note that the CASP9 RARE and 5′ start site are 9.5 kb apart and the polyA site is 22 kb to the 3′ end of RARE  chromatin by the 3C technique . Briefly, fixed chromatin DNA was cleaved with a restriction enzyme (NcoI) and ligated after dilution. Real-time qPCR was then used to detect the ligated DNA segments. Figure CASP9 . Assembly of this loop was almost entirely dependent on RA treatment  a 5′ end loop connecting the RARE to the promoter  was induced by RA Figure ; (ii) a t Figure ; (iii) at Figure . We wishCASP9 mRNA synthesis, promoter and RARE occupancy by protein factors and histone modifications  with the 3′ end of the gene (polyA addition sites). This loop peaked 15 min after RA and slowly disappeared  similar to the early loop induced by RA on CASP9 chromatin  of the two polyA addition sites (1 and 2) does not discriminate which polyA1 or 2 is included in the RA-induced loop of CASP9 gene.Strikingly, formation of all of these loops is cyclical. They first appear 15 min following RA exposure, disappear at 30 min and reform by 60 min. This oscillation resembles that displayed in previous figures showing n Figure . The phyCASP9, we investigated the localization on CASP9 chromatin of Ssu72, a protein which marks the 3′ end of genes and interacts with the general transcription initiation factor, TFIIB .To find a 3′-end-specific RA-dependent marker of r, TFIIB . Supplemr, TFIIB . At 15 mHow relevant are these loops to RA-induced transcription and how they are linked to the demethylation cycles induced by RA? To address this question we measured the loops involving RARE in cells expressing the LSD1ALA mutant. Expression of LSD1ALA inhibited RA-induced demethylation Figure . SupplemWe conclude that the demethylation cycles triggered by RA govern the ordered formation of the loops essential for RA-induced transcription.The data reported here show that the methylation changes of K4 and K9 of histone H3 are linked to the recruitment of repair enzymes and, most importantly, to the formation of chromatin-DNA loops that juxtapose the 5′ end TSS, the enhancer (RARE) and the 3′ end of the transcribed gene Figure  and the Further complicating comparative analysis of the data published thus far are the different temporal frames used in various studies to describe the molecular events induced by transcriptional activators. These studies have been carried out at 1 h  to severCASP9 and CYP26A1 genes  oscillate with a period of 60 min from the initial induction with RA. With the time (2–4 h) this periodic oscillation is lost  with CYP26A1 (RARE contiguous with the promoter) genes, the timing of methylation–demethylation, the oscillation of RARα and LSD1-JMJD2A recruitment to the RARE promoter appear very similarWe wish to note that although the structural organization of the RARE is different comparing Why should the definition of the physical borders of a transcription unit be important? Each gene in eukaryotes is indeed the target of many stimuli, which can independently induce transcription. The protein Ssu72 has been identified in yeast as an element required for marking the 3′ end of the chromatin loops and transcription directionality ,42. The CASP9 gene, before and after RA induction, may indicate the changes of chromatin engaged in RA-induced and RA-independent transcription. This protein is present at 3′ end of CASP9 3′ end before RA induction, disappears from this site at 15 min and reappears 30 min after RA treatment . Fifteen minutes after RA stimulation, Ssu72 moves from the 3′ end to the CASP9 TSS and RARE , where its concentration oscillates synchronously with methylation–demethylation cycles and recruitment of BER and NER enzymes . Conversely, a rise in levels of the inducer rapidly reactivates transcription by stabilizing the loops (transcription memory) (see the Supplementary Movie).Supplementary Data are available at NAR Online.