It is now clearly established that complex interactions between genes and environment are involved in multiple aspects of neuropsychiatric disorders, from determining an individual’s vulnerability to onset, to influencing its response to therapeutic intervention. In this perspective, it appears crucial to better understand how the organism reacts to environmental stimuli and provide a coordinated and adapted response. In the central nervous system, neuronal plasticity and neurotransmission are among the major processes integrating such complex interactions between genes and environmental stimuli. In particular, immediate early genes (IEGs) are critical components of these interactions as they provide the molecular framework for a rapid and dynamic response to neuronal activity while opening the possibility for a lasting and sustained adaptation through regulation of the expression of a wide range of genes. As a result, IEGs have been tightly associated with neuronal activity as well as a variety of higher order processes within the central nervous system such as learning, memory and sensitivity to reward. The immediate early gene and transcription factor early growth response 1 (EGR1) has thus been revealed as a major mediator and regulator of synaptic plasticity and neuronal activity in both physiological and pathological conditions. In this review article, we will focus on the role of EGR1 in the central nervous system. First, we will summarize the different factors influencing its activity. Then, we will analyze the amount of data, including genome-wide, that has emerged in the recent years describing the wide variety of genes, pathways and biological functions regulated directly or indirectly by EGR1. We will thus be able to gain better insights into the mechanisms underlying EGR1’s functions in physiological neuronal activity. Finally, we will discuss and illustrate the role of EGR1 in pathological states with a particular interest in cognitive functions and neuropsychiatric disorders. Despite a high level of heritability observed in the most common neuropsychiatric disorders, a clear genetic basis in their etiology has proven difficult to identify  are critical mediators of gene × environment interactions and thus have been the focus of an extensive research interest in order to elucidate how environmental stimuli trigger a fast response with enduring neuroadaptations on neuronal activity and plasticity  in the rat PC12 cells in the presence of the protein synthesis inhibitor cyclohexamide  zinc fingers DNA-binding domains, suggesting similarities in the DNA sequences recognized by each EGR protein and thus the possibility of overlap in their respective targets and functions  also up-regulate Egr1 mRNA levels in an NMDA receptor-dependent manner  and cyclic AMP-response element binding protein (CREB) transcription factors, which can bind their respective response elements located in the Egr1 promoter , activator protein-1 (AP-1), nuclear factor kappa B (NFκB), or EGR1 itself  and transcription factor(s) to directly regulate Egr1 transcriptional regulation can be resolved, however, when accounting for kinetics and interactions between transcription factor binding, cofactors recruitment and chromatin dynamics including histone methylation, acetylation and phosphorylation, as well as nucleosome positioning. Indeed, by focusing on Egr1 gene transcription in MLP29 mouse progenitor cells, Riffo-Campos et al. . Indeed, in peripheral tissues and several cancer cell lines, several studies report direct targeting of EGR1 by miR-543  following by microarray profiling in monocytic differentiation of human monoblastoma cells or following UV-induced apoptosis in prostate carcinoma cells . Indeed, EGR1 binds to Interestingly, recent genome-wide investigations of EGR1 transcriptional targets point towards a widespread regulation of genes associated with similar dynamics critical in regulating synaptic plasticity. Indeed, a multitude of genes related to vesicular transport and neurotransmitter release, clathrin-dependent endocytosis , or actin cytoskeleton, are commonly observed as direct EGR1 targets  of the hypothalamus  axis—but not in the hippocampus or cortical regions for which EGR1 signal was more related to the exploration of the environment  up-regulates EGR1 expression in the rat or mouse hippocampus, which mediates stress-related fear memories  gene promoter and activates its expression  MHR01 MH87583, MH099085 and MH109450 to MK.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In the last decade, advanced functional genomics approaches and deep sequencing have allowed large-scale mapping of histone modifications and other epigenetic marks, highlighting functional relationships between chromatin organization and genome function. Here, we propose a novel approach to explore functional interactions between different epigenetic modifications and extract combinatorial profiles that can be used to annotate the chromatin in a finite number of functional classes. Our method is based on non-negative matrix factorization (NMF), an unsupervised learning technique originally employed to decompose high-dimensional data in a reduced number of meaningful patterns. We applied the NMF algorithm to a set of different epigenetic marks, consisting of ChIP-seq assays for multiple histone modifications, Pol II binding and chromatin accessibility assays from human H1 cells.We identified a number of chromatin profiles that contain functional information and are biologically interpretable. We also observe that epigenetic profiles are characterized by specific genomic contexts and show significant association with distinct genomic features. Moreover, analysis of RNA-seq data reveals that distinct chromatin signatures correlate with the level of gene expression.Overall, our study highlights the utility of NMF in studying functional relationships between different epigenetic modifications and may provide new biological insights for the interpretation of the chromatin dynamics.The online version of this article (doi:10.1186/s13072-017-0131-7) contains supplementary material, which is available to authorized users. In eukaryotes, DNA is wrapped and packaged in nucleosomes, which represent the fundamental unit of the chromatin. Each nucleosome consists of an octamer of four different histone proteins: H2A, H2B, H3 and H4. These subunits undergo several types of chemical modifications on their N-terminal chain, including phosphorylation, methylation and acetylation. It has been shown that posttranslational modifications of histone proteins can modulate the structural and functional properties of the chromatin and may be associated with transcriptional activation or repression , suggestIn the last decade, advanced functional genomic techniques and NGS sequencing  have allowed large-scale mapping of histone modifications and other epigenetic marks, highlighting functional relationships between chromatin states and transcriptional activity. As genome-wide approaches became popular, broad datasets of different type of epigenetic data started being collected in public databases. Nowadays, two major consortia, the ENCODE (The ENCyclopedia Of Dna Elements) project  and the We applied NMF on multiple genomic datasets collecting 13 different epigenetic marks, including ChIP-seq assays for multiple histone modifications , 16, 17,Each of the genomic dataset consisted of an experiment with two or more biological replicates for a given chromatin mark. In some cases, experiments from multiple laboratories for the same mark were provided. In order to generate a uniform epigenetic signal for each type of mark, we combined data from replicates in each experiment or multiple experiments (laboratories) when present.Read alignment files (BAM or BED files) were selected from the NIH Roadmap Epigenomics data portal and the ENCODE project database and downloaded from GEO (Gene Expression Omnibus) .ad hoc pipeline to generate a mark-specific normalized coverage track along the genomic coordinate.To maintain a uniform, standard treatment of the data across different experiments, we adopted a common protocol of data processing. For each genomic assay, read alignments were processed through an http://broadinstitute.github.io/picard)Remove all duplicate sequences using ‘MarkDuplicates’ function from Picard Tools The first part of the pipeline takes raw BAM files as input and extracts high-quality alignments generating a processed BED file for each separate sample. More specifically, we apply the following steps:For some datasets, processed BED files were already available and did not require any of the aforementioned steps for the final signal estimation.i in the dataset P for the epigenetic mark k and the bin j:R, the observed number of reads of k assigned to jE, the expected number of reads of k assigned to j assuming a uniform distribution of reads over all uniquely mappable sites on the genome.For each chromatin mark, processed BED files were then combined together generating a multivariate distribution of different epigenetic signals, which represent the input to the NMF. This procedure was carried out following a standard segmentation approach. First, we partitioned the genome (hg19/GRC37 assembly version) in 200-bp non-overlapping intervals (bins), which better approximates the average occupancy of a single nucleosome along the DNA. For each sample, uniquely mapped reads were re-distributed to intervals according to their alignment positions: each read overlapping with an interval was assigned to the interval. Following the approach of Hoffman et al. , raw coui of P and the different dataset sizes, we also estimated a scaling factor which normalizes R on the basis of the sample size and the average library size of the dataset. Hence, the expected read count E is given by:M(i) is the number of uniquely mappable positions in the genomic interval j, G is the uniquely mappable size of the human genome and Q is the normalization factor estimated for sample i, defined as Q = A(P)/C(i), where A(P) is the mean total count of mapped reads across all samples i in the dataset P and C(i) corresponds to the total number of mapped reads in i. The hg19 uniqueness mappability track was generated as part of the ENCODE project and downloaded from the UCSC Browser database . A general rule of thumb for the stochastic initialization approach is to perform several runs of the NMF  and keep the factorization that minimizes the reconstruction error across multiple runs.δ is the difference between the real and the model output values of the epigenetic mark levels.The weights and the coefficient matrices need to be initialized with a seed . As with most unsupervised learning algorithms, the choice of the optimal r represents a critical step in an NMF analysis and a clear consensus strategy to determine the best value of r is still lacking. In general, large factorization ranks results in sparse signal profiles  and few combinatorial interactions. Conversely, too small values of r compress the data in a scanty number of patterns where spurious interactions between epigenetic marks are more likely to arise.The most important parameter in NMF is the factorization rank r values, estimate a quality measure of the results, and select the best value of r according to this quality criteria  is the maximum weight for that bin over all the contributions. We then examined the frequency by which two given chromatin profiles co-occurred in the same bin considering all possible pairs among the profiles for decreasing RWC thresholds  weight observed in the We then asked how the assignment of additional profiles influences the amount of genomic information captured in each combinatorial pattern. For this purpose, we examined how the mean overlap rate with genomic annotations varies in function of the mean number of profiles assigned per bin . To this aim, we computed the frequency of each chromatin profile over a region of 12 kb (2-kilobases upstream and 10-kilobases downstream) spanning the transcription start site, binned them in increasing ranges of expression levels and identified the most frequent profile in a given bin for a given expression interval. We observed that most of the epigenetic profiles are partitioned in a number of well-delimited regions along the genomic and the expression coordinate, generating diverse bi-dimensional patterns that reflect multiple levels of information about the gene position and the promoter activity . Next, we computed the frequency of each chromatin profile in each expression interval in each bin over a window of ±2 kb around the transcription start site. We next estimated the logarithmic fold-change of the occurrence observed in the real data with respect to the frequency in the random dataset. The heatmap in Fig. p value = 10−10; Creb1: f.e. = 6.45, p value = 10−10; Nanog: f.e. = 6.1, p value = 10−10; Jund: f.e. = 8.6, p value = 10−10; Pol2: f.e. = 10.7, p value = 10−10; histone acetylase p300: f.e. = 10.64, p value = 10−10; Sp1: f.e. = 9.24, p value = 10−10; Taf1: f.e = 10.3, p value = 10−10; Usf: f.e. = 3, p value = 10−10; Pou5f1: f.e. = 5.5, p value = 10−10). Notably, this profile shows significant overlap with the human histone deacetylase 1 protein , suggesting a possible additional role in the control of the chromatin organization for this factor. The ‘Transcription Initiation’ profile shows the highest enrichment in the cAMP-responsive element binding protein  and for members of the basal transcription machinery  but with a less pronounced overlap compared to that of profiles ActProm and RegEl. Interestingly, the TxInit profile has relatively stronger enrichment in the E2f6 transcription factor  compared to profile ActProm. E2f6 is a well-known inhibitor of the E2f-dependent transcription and may negatively regulate RNA polymerase II-dependent promoters via the recruitment of a chromatin remodeling complexes. This result seems to be in agreement with the fact that profile TxInit is found as predominant in the TSS-spanning regions of lowly expressed genes  but with lower levels of overlap than found in promoter-associated profiles (ActProm and TxInit). In contrast, the RegEl profile is found to be enriched in the CCCTC binding insulator protein  and the double-strand break repair Rad21 homolog protein , another chromatin binding protein known to cooperate with the CTCF-mediated insulator complex in modulating enhancer–promoter interactions [The enrichment of the chromatin profiles relative to ChIP-seq data for a subset of transcription factors allowed us to further investigate the regulatory role of each combination of marks in the control of gene transcription see “a, b. Unsractions .Fig. 8OvWe compared our chromatin profile distribution with that of ChromHMM (9), which is a common chromatin segmentation technique. We did not compare our method with the Segway algorithm  because Both ChromHMM and EpicSeg have been shown to be effective when used close to 13 chromatin states , 44. TheWe assessed the ability of the methods to recover functional information about biologically relevant regions and to correctly predict the presence of distinct functional elements in terms of their sensitivity (true positive rate), specificity (true negative rate), precision  and accuracy .7), NMF with r = 13 (NMF13), the ChromHMM 7-states model (ChromHMM7) and the ChromHMM 13-states model (ChromHMM13). The enrichment patterns of NMF7 are very similar to those of ChromHMM7 and ChromHMM13, while the NMF13 model is much less effective, confirming that a level of factorization of 7 is sufficient for the NMF model to capture the combinatorial information contained in the chromatin profiles.The heatmaps in Additional file 13 model shows very weak overlap in all the features examined . Remarkably, the NMF7 approach and the ChromHMM7 tend to perform better than the ChromHMM13 model in terms of feature coverage. In 9 out of 12 features (75%), our NMF model shows the best mean coverage, confirming the ability of the NMF profiles to retrieve a larger fraction of biological information from distinct types of functional regions in the genome. In both analyses, the most striking differences are observed for Refseq transcription start sites , upstream regions , smallRNAs  and DNase hypersensitive sites .Interestingly, we noted that our NMFred Fig. a, b. Ind7 approach gives similar or better overlap compared to both the ChromHMM7 and the ChromHMM13 model, suggesting that this trend is unlikely to depend on the selection of a specific combination of enriched profiles  is reported in Fig. Next, we tested the performance of the different approach in the prediction of some important functional genomic features  using, for each model, the full set of enriched profiles/states. The true positive rate was estimated considering all the bins assigned to any of the enriched profile/state and having (or not) a minimum overlap (1 bp) with the analyzed feature. A number of intragenic (or intergenic) intervals lacking any annotation for the specific feature were assumed as negative controls for the test. We found that, when all the enriched profiles/states were used, our approach has in general the same predictive power of the ChromHMM models , another cell line well characterized by a large number of functional chromatin assays publicly available on the ENCODE catalog . We compIn this work, we demonstrated the usefulness of the non-negative matrix factorization for the systematic characterization of the chromatin functions. This is an unsupervised classification technique that uses a signal decomposition algorithm to reduce the dimensionality of multivariate datasets to a restricted number of combinatorial components , 45, 46.As discussed in previous studies, the most critical parameter in common chromatin segmentation techniques is represented by the expected number of chromatin profiles (states) chosen to capture meaningful combinations of marks. One of the main advantages of the NMF approach is that the number of chromatin profiles is not arbitrarily set. Indeed, here we statistically assessed the stability of the data for an increasing number of profiles and selected the value at which the clustered data started being significantly stable compared to the random profile distribution. This approach allowed us to identify seven different chromatin profiles that better represents the most recurrent combination of marks.We tested NMF on a combined epigenetic dataset of 13 chromatin marks, encompassing 9 histone modifications, one histone variant, two transcription factors binding data Pol2, CTCF) and one chromatin accessibility mark (DNase hypersensitive site assay), previously mapped in hESC and IMR90 lines. A complete list of the analyzed datasets is reported in the Additional file , CTCF anWe also found that chromatin profiles are more effective than single marks in recovering known functional elements and observed that most profiles are distributed following specific bi-dimensional patterns along the genomic and the expression coordinates. This strongly suggests that chromatin profiles are not only related to the genomic localization of distinct functional elements but can also be correlated to the level of transcriptional activity. Specifically, we showed that, around the promoter region, chromatin profiles are organized in signatures that preserve positional information and are able to mirror well the different ranges of expression. Our data suggest the presence of an almost symmetric mechanism of chromatin activation, which could propagates progressively from the gene TSS toward distal upstream and downstream regions through a defined number functional chromatin states Fig. b–f. WhenWe also used the enriched profiles from both methods to verify how well they can predict annotated regions in the genome and showed that the NMF model has very similar predictive power, but has a slightly higher precision and specificity in the prediction of Refseq TSSs, with a very small drop in terms of sensitivity.Furthermore, our results are not specific for the selected cell line (hESC-H1) since the profiles obtained using another one (IMR90) provides very similar results.Despite these observations, there are still a number of limiting factors that have to be taken into account and that will require additional efforts to further improve the accuracy of the approach. First of all, we analyzed how the chromatin profile assignment varies as function of the single profile contribution and we found that specific profile combinations are more recurrent than others, suggesting that there is still a fraction of uncertainty in determining the correct chromatin status for specific groups of bins. We also found that, in some cases, assigning more than one profile makes the chromatin segmentation procedure even more informative, suggesting that some genomic information could be missed in the bin assignment. In this context, more statistically rigorous approaches to redefine bin/profile relationships will likely be needed. Moreover, further methodological improvements will be required to optimize the signal decomposition algorithm in the integrative analysis of multiple epigenetic datasets. Although the technique has been shown to be effective in capturing most important functional mark interactions, the detection of combinatorial information for some histone mark distributions  still remains a challenging task. This consideration arises from the fact that such histone modifications tend to be compressed into a unique chromatin profile  rather than gradually fluctuate over different functional states, making the approach less sensitive to local patterns of mark interactions. An example of such patterns is particularly evident in undifferentiated cells, where distinct bivalent domains of chromatin modifications  were found at poised promoters of lineage-specific genes , 48. Thi®Xeon® 56 Gb RAM multi-core machine. It is, however, worth mentioning that the implementation of the NMF technique in the R environment allows easier integration of statistical function and appropriate libraries normally used in downstream analyses. We believe that this technique can improve the limited repertoire of tools and algorithms currently available in the analysis of high-dimensional epigenetic datasets, thus facilitating the development of novel frameworks for a more accurate characterization of the chromatin activity . Figure S1: Selection of the best factorization rank. (a) The plot shows the variation of the cophenetic correlation coefficient (on the Y-axis) for both the real (blue) and the random (red) data for increasing values of r. Each point in the real data is obtained after 30 runs of a single NMF analysis using the factorization rank indicated on the X-axis. For the random data, each point indicates the mean cophenetic coefficient obtained by repeating the NMF analysis 30 times at a given factorization rank. The cophenetic coefficient in the real dataset becomes stable at r = 7 remaining at about 0.99 up to r = 11, whilst the stability of the random dataset dramatically drops in the same interval. Within this range, the cophenetic coefficient obtained in the real dataset is more than 4-fold the standard deviation of the coefficients in the random data. b The plot shows the trend of the sparseness in the W (basis) and H (coefficient) matrices over the same range of factorization ranks, in both the real and the random dataset. Figure S2: Significance of chromatin profile enrichment in distinct genomic features. The heatmap shows for each epigenetic profile, the significance of the enrichment compared to different types of genomic features and functional regions of the genome. The significance of the enrichment is assessed using a Fisher’s exact test with a p value of 10−3 as statistical threshold. The color-scale from blue to red indicates the significance of the test as follows: blue: p value > 0.01; purple: 0.01 > p value > 0.001; dark red: 0.001 > p value > 10−5; red: p value < 10−5. A specific biological label is assigned to each profile in order to facilitate its biological interpretation on the basis of the enrichment observed (top-bottom): ActProm = Active Promoter (profile 1); TxInit = Transcription Initiation (profile 3); RepReg = Repressed Regulatory Regions (profile 4); Ehn = Enhancer Regions (profile 6); RegEl = Regulatory Elements (profile 7); GenBd = Gene Body Transcription (profile 5); RepChr = Repressed Chromatin (profile 2). Genomic features are the same represented in Figure 3: CAGE = hESC-H1 CAGE clusters from ENCODE; RfTSS = Refseq Transcription Start Sites; RfTES = Refseq Transcription End Sites; 5UTR = Refseq 5’untranslated region; 3UTR = Refseq 3’unstranslated regions; H1 Enhancers = Superenhancer regions from hESC; CpG = CpG islands; Upstream = 1Kb upstream regions from Refseq TSSs; DNase1 = hESC DNase1 Hypersensitive sites from ENCODE; TFBS = Conserved transcription factor binding sites from the Transfac Matrix Database; 5C = Chromatin conformation capture carbon copy data from hESC; EnhancersDB = experimentally validated enhancer elements from the VistaEnhancer Dabatabse; Rf = Refseq genes; Int = intronic sequences from Refseq genes; Ex = exonic sequences from Refseq genes; PolyA = predicted poly-adenylation sites; sRNA = small RNAs; HMMhetero = predicted heterochromatin regions in hESC. Figure S3: Frequency of transition between epigenetic profiles. The grid shows the occurrence of each transition for all possible pair-wise combination of profiles. Each cell in the heatmap represents the enrichment of a transition A → B from the profile indicated in each row (A) to the profile reported in the corresponding column (B). Only transitions between consecutive profiles (i.e. regions of the profiles not separated by one or more unassigned bins) are considered. For each combination, the enrichment is calculated as the logarithm of fAB(s)/fAB(r) where fAB(s) is the fraction of the regions of A followed by any region of B over the total of A-regions observed in the real sample and fAB(r) the fraction of A-regions followed by any region of B over the total number of A-regions in the random dataset. Chromatin profiles are indicated using the same labels as in Figure 3 and Supplementary Figure 2. Figure S4: Comparison between the recovery of poly-adenilation sites in chromatin profiles and single epigenetic marks. The plots show the Receiver Operating Characteristic (ROC) curve generated to compare the performance of different chromatin profiles with those of single marks for the recovery of known poly-adenilation sites. The curve is generated by measuring the TPR (true positive rate) and FPR  at increasing prediction thresholds according to the fraction of annotated poly-A sites covered by all bins having a signal above that threshold. Each epigenetic mark is evaluated on the basis of the sigmoid-transformed normalized coverage track, as reported in the input matrix Vj,k of the NMF. Each chromatin profile is quantitatively evaluated using the weight distribution over all genomic intervals . To provide a better visualization of the results only the most representative set of chromatin profiles and marks are represented for each feature. Figure S5: Recovery of genomic information using ambiguous profile assignment. The plot gives a representation of how the genomic overlap changes in function of the number of different profiles assigned to a bin using their relative weights sorted in decreasing order . The amount of genomic information retrieved is reported on the Y-axis as the mean rate of overlap considering all genomic features significantly enriched in a given profile. Each chromatin profile is denoted with the same label and color scheme previously adopted in the main text. Figure S6: Chromatin profile assignment according to genomic position and gene expression. The color-code heatmap is used to represent chromatin profile assignment over a 12Kb region (2Kb upstream and a 10Kb downstream) around the TSS in a subset of 1000 genes from GENCODE (GRCh37)-database binned in 200bp consecutive genomic intervals. Genes are sorted in decreasing order according to the RPKM expression vaule and reported on the Y-axis. The X-axis indicates the genomic distance from the GENCODE Transcription Start Site, which is positioned at zero. Each profile is indicated using the same color legend previously adopted in this work: ActProm (Active Promoter) = light green, RepChr (Repressed Chromatin) = purple, TxInit (Transcription Initiation) = dark green, RepReg(Repressed Regulatory) = blue, GenBd (Gene Body Transcription) = red, Enh (Enhancer Regions) = yellow, RegEl (Regulatory DNA Elements) = grey. The white vertical line on the left side of the heatmap shows the exact TSS position. The fraction of genes with length corresponding to each interval from the TSS is reported in the upper panel. The yellow bar at the bottom of the graph represents genes with length greater than 10 Kb . Figure S7: Frequency of chromatin profiles according to expression and distance from the gene TSS. Each plot shows the distribution of a specific epigenetic profile in a bi-dimensional space defined by TSS-surrounding region and the level of gene expression (RPKM). A 12Kb region (2Kb upstream the TSS and 10Kb downstream) is represented on the X-axis as a sequence of 200 bp genomic intervals. Each column along the axis is labeled with its distance from the TSS (x = 0). On the Y-axis, RPKM expression estimates are reported as percentiles in decreasing order from the top (highest expression) to the bottom (lowest range of expression). Each cell in the matrix shows the frequency by which a given profile occurs in a given interval from the TSS and a given range of expression. We compute this frequency as ci/citot , where ci is the number of bins of profile i observed in the genomic interval x and the expression range y and citot is the total number of genomic bins belonging to the profile. The ‘Repressed chromatin’ (RepChr) profile is omitted in this figure since no distinct patterns were observed on the basis of the TSS-distance and the expression. Figure S8: Distribution of gene expression in different subclusters. Each box-plot in the graph shows the expression distribution from all genes contained in each of the subclusters represented in Figure 7. Sub-clusters are numbered from 1 to 5 according to their median expression value. Gene expression levels are reported in RPKM (reads per Kilobase per Million of Mapped Reads) on the Y-axis. Figure S9: Feature enrichment distribution across profiles/states using different methods. The enrichment distribution (reported as Fisher-test Odd-ratio) of different genomic features for each profile identified in our study is displayed as color-scale heatmap (a top-left) and compared with that obtained from different chromatin segmentation approaches: (b top-right) NMF with factorization rank = 13; (c bottom-left) ChromHMM with 7 states; (d bottom-right) ChromHMM with 13 states. In heatmaps from b to d, profiles/states are numbered in a completely independent manner since there are no relationships between numerical orders in the different plots. Genomic features indicated in the columns are those reported in Fig. 3. CAGE = hESC-H1 CAGE clusters from ENCODE; RfTSS = Refseq Transcription Start Sites; RfTES = Refseq Transcription End Sites; 5UTR = Refseq 5’untranslated region; 3UTR = Refseq 3’unstranslated regions; H1 Enhancers = Superenhancer regions from hESC; CpG = CpG islands; Upstream = 1Kb upstream regions from Refseq TSSs; DNase1 = hESC DNase1 Hypersensitive sites from ENCODE; TFBS = Conserved transcription factor binding sites from the Transfac Matrix Database; 5C = Chromatin conformation capture carbon copy data from hESC; EnhancersDB = experimentally validated enhancer elements from the VistaEnhancer Dabatabse; Rf = Refseq genes; Int = intronic sequences from Refseq genes; Ex = exonic sequences from Refseq genes; PolyA = predicted poly-adenylation sites; sRNA = small RNAs; HMMhetero = predicted heterochromatin regions in hESC. Figure S10: Overlap and coverage of functional genomic regions with different combinations of enriched profiles/states. Each boxplot panel shows the distribution of the mean overlap over all possible combinations of profiles/states in each feature (a) and the mean coverage of a given feature across profile/state combinations (b) using different approaches. Data are represented such that any point in a boxplot indicates the mean overlap (or coverage) of the represented feature observed for a single combination of profiles/states. The overlap/coverage distributions are highlighted with different colors according to the tested method: ChromHMM/13states = green; ChromHMM/7states = darkblue; NMF/13profiles = yellow; NMF/7profiles = orange. Table S1: Combinations of enriched profiles/states used to test the overlap/coverage in a set of genomic features. The table shows for each genomic feature (first column) the list of profiles (states) enriched in that feature and the number of all their possible combinations with size L ≤ N (where N is the number of enriched profiles/states for that feature) obtained by each method. Distributions of the mean overlap/coverage for such profiles/states combinations are reported in Supplementary Fig. 10. Feature labels reported in the most-left column are (top-bottom): 1 Kb-upstream = 1 Kb upstream regions from Refseq TSS; CpG Islands; 100 bp window surrounding Refseq Transcription Start Site; 100 bp windows surrounding Refseq Transcription End Site; Refseq genes over the 75th percentile of the RPKM expression distribution; Refseq genes under the 25th percentile of their RPKM expression distribution; PolyAdenylation sites; Conserved transcription factor binding sites; Superenhancer regions from hESC; Experimentally validated enhancer elements from the VistaEnhancer Dabatabse; hESC DNase1 Hypersensitive sites from ENCODE; small RNAs; Predicted hESC heterochromatin regions from ChromHMM. Figure S11. NMF and ChromHMM performance in the prediction of distinct functional elements in the genome. Each graph compares the performance of the NMF-based approach with that of ChromHMM (with 7 and 13 states) for the prediction of functionally relevant regions such as Refseq transcription start sites (a), hESCH1-super enhancers (b) and Refseq 1Kb-upstream regions (c). For a given feature, only significantly enriched profiles/states identified by each method are used as predictors. True positives are bins assigned to an enriched profile/state and having a minimum overlap (1 bp) with the considered feature. Negative controls are provided as a list of intragenic (or intergenic) intervals without any evidence of annotation for that feature. Figure S12. Comparison of the NMF chromatin segmentation approach in different cell lines. The two heatmaps show Pearson correlation index distribution between chromatin profiles from hESCH1 and IMR90 cell lines using all possible pairwise comparisons. The two cell lines were correlated using either single chromatin mark coefficients from the H-matrix (a) or genomic enrichment distribution (b) of single profiles in a number of genomic features . Epigenetic profiles detected IMR90 (columns) are labeled with an ‘s’ followed by numeric assignment (1–7). hESC chromatin profiles are reported in rows using the same label system adopted in the previous figures.Additional file 2. List of samples collected and analyzed in this study (Excel format). For each sample, the table reports: (1) the cell type, (2) the genetic information analyzed through the genomic assay, (3) the epigenetic mark, (4) the sample identifier from GEO, (5) the name of the BAM/BED mapping file, (6) the type of the alignment data (processed or not), the number of the biological replicate from the same lab/experiment, (8) the name of the structure which generated the experiment, (9) the link to the sample file. All ChIP-seq/DNase-seq alignment files were downloaded from the Gene Expression Omnibus database and were provided as part of the NIH Roadmap Epigenomics project and the ENCODE project database.Additional file 3. Archive file (ZIP format) containing all the scripts of the computational pipeline implemented in our work (FGandolfi_pipeline.zip).


Here using fission yeast, we identify Fun30Fft3 as a chromatin remodeller, which localizes at transcribing regions to promote RNAPII transcription. Fun30Fft3 associates with RNAPII and collaborates with the histone chaperone, FACT, which facilitates RNAPII elongation through chromatin, to induce nucleosome disassembly at transcribing regions during RNAPII transcription. Mutants, resulting in reduced nucleosome-barrier, such as deletion mutants of histones H3/H4 themselves and the genes encoding components of histone deacetylase Clr6 complex II suppress the defects in growth and RNAPII occupancy of cells lacking Fun30Fft3. These data suggest that RNAPII utilizes the chromatin remodeller, Fun30Fft3, to overcome the nucleosome barrier to transcription elongation.Previous studies have revealed that nucleosomes impede elongation of RNA polymerase II (RNAPII). Recent observations suggest a role for ATP-dependent chromatin remodellers in modulating this process, but direct  Fft3 localizes to transcribing regions to promote transcription by nucleosome disassembly in vivo.Nucleosomes have been shown to impede the elongation of RNA polymerase II during transcription. Here, the authors provide evidence that in fission yeast chromatin remodeller Fun30 The best characterized factor which modulates the nucleosome barrier during RNAPII elongation is the histone chaperone, FACT (FAcilitates Chromatin Transcription). FACT is known to promote RNAPII transcription through chromatin templates in vitroin vivo464101112Nucleosomes impose a strong barrier against RNAPII-mediated transcription elongation Saccharomyces cerevisiae, the histone deacetylase complex, Rpd3S, is localized to transcribing regions by Set2-mediated H3 K36 methylation and by elongating RNAPII, and is responsible for removing histone acetylation at transcribing regions141510Histone acetylation also contributes to modulating the nucleosome barrier during RNAPII elongation. The acetylation of histones H3 and H4 ahead of an elongating RNAPII are thought to destabilize nucleosomes. Notably, however, this acetylation must be removed to restore the normal chromatin state at transcribing regions. In in vitro2627in vivo.ATP-dependent chromatin remodellers can displace histone dimers from nucleosomes or evict whole histone octamers from chromatin1819212223Fft3 promotes RNAPII occupancy at coding regions in collaboration with the additional Fun30 paralogue, Fft2, and the FACT complex. Furthermore, we show that Fun30Fft3 interacts and co-localizes with RNAPII and transcription elongation factors. Importantly, we demonstrate that Fun30Fft3 cooperates with FACT to induce nucleosome disassembly at transcribing regions, which accounts for a large fraction of RNAPII-mediated nucleosome disassembly. The slow growth of cells with impaired Fun30Fft3 can be rescued by mutations that reduce the nucleosome barrier at transcribing regions, such as mutations in the genes encoding components of Clr6 complex II  and histones themselves. We also show that a histone gene knockdown rescues the defects of RNAPII occupancy at transcribing regions in cells lacking Fun30Fft3. Collectively, these results suggest that Fun30Fft3 plays a major role in nucleosome disassembly during RNAPII elongation, which facilitates the efficient passage of RNAPII through chromatin templates during transcription elongation.In this study, we used fission yeast to explore the involvement of chromatin remodellers in regulating the nucleosome barrier at transcribing regions. We report that Fun30S5P) during transcription initiationS2P) on the transition to elongationS2P), as this represents the actively elongating form of RNAPII. However, a recent mass-spectrometric analysis of budding yeast revealed that CTD phosphorylation occurs in less than half of all CTD repeats, and that Ser2 phosphorylation is observed in fewer than 10% of all CTDs32un) in parallel with RNAPII-CTDS2P.To gain a comprehensive understanding of the roles of ATP-dependent chromatin remodellers in the regulation of RNAPII transcription, we performed a genome-wide analysis of RNAPII distribution among cells harbouring mutations of fission yeast chromatin remodellers. RNAPII is differentially phosphorylated at its C-terminal repeat domain (CTD) during different stages of transcription: it is phosphorylated at Ser5 (CTDhrp1Δ and hrp3Δ), INO80 (ies6Δ), RSC (snf21-36), SWI/SNF (snf22Δ), and SWR1 (swr1Δ). We also analysed a mutant defective in the most recently identified chromatin remodeller Fun30 which has three paralogues  in fission yeast. Consistent with a previous study, we found that fft3Δ cells show a cold-sensitive growth defectfft2Δ but not with fft1Δ  in the analysis. Metagene analysis was performed on RNAPII-CTDS2P and RNAPII-CTDun occupancy profiles obtained by chromatin immunoprecipitation combined with sequencing (ChIP-Seq). Our results revealed that RNAPII occupancy was severely reduced at genes with high RNAPII (RNAPII-CTDS2P and RNAPII-CTDun) occupancy in spt16-18, fft2Δ fft3Δ, ies6Δ, snf21-36 and snf22Δ cells, but not in hrp1Δ, hrp3Δ, or swr1Δ cells (r≥0.4) as a meaningful correlationies6Δ, fft2Δ fft3Δ, and snf21-36 strains) and the FACT mutant (spt16-18) all exhibited transcription-dependent decreases in RNAPII occupancy (for both RNAPII-CTDS2P and RNAPII-CTDun). Thus, it appears that these chromatin remodellers function during active transcription.We profiled RNAPII occupancy changes in mutants defective in major chromatin-remodelling complexes, such as CHD  occupancy  elementsFft2 and Fun30Fft3. Scatterplot analysis was performed to calculate the correlation between the occupancy of RNAPII and those of FACT or the chromatin remodellers  that were only slightly weaker than those obtained for FACT  was weaker than that of Fun30Fft3 but stronger than those of Ino80 and Snf21. Our results indicate that there are strong similarities in the localizations and functions of FACT and the Fun30 paralogues in fission yeast. We also found that direct comparison of RNAPII occupancy changes of fft2Δ fft3Δ cells positively correlate with those of spt16-18 cells  mutation of spt16+ (spt16-1 or spt16-2), and found that fft3Δ causes synthetic growth defects when combined with spt16-1 or spt16-2 (fft3Δ was combined with mutations in other transcription elongation-related factors such as Paf1 and Spt6 (paf1Δ and spt6-1). This revealed an essential function that is shared by Fun30Fft3 and FACT but not apparently by Fun30Fft3 and other transcription elongation-related factors. The synthetic growth defects of fft3Δ spt16-1 and fft3Δ spt16-2 cells were observed at a temperature (30 °C) that only partially disrupted the function of FACT. Thus, we could not determine whether or not the shared function of Fun30Fft3 and FACT resides in the same pathway by the genetic analysis alone. To address this question, we compared the RNAPII occupancy changes in fft3Δ spt16-1 cells with those in fft3Δ cells and in spt16-1 cells grown at their restrictive temperature (37 °C). Scatterplot analyses revealed that fft3Δ did not have any additive effect on RNAPII occupancy when the function of Spt16 was severely impaired  occupancies were reduced in fft3Δ cells (log2 fold-change≤−0.5) versus spt16-1 cells (log2 fold-change≤−0.5). Analysis of the overlapping genes by boxplots showed that the reduction of RNAPII occupancy in spt16-1 cells was more severe than that in fft3Δ cells, but was similar to that in fft3Δ spt16-1 cells  method demonstrated that Fun30Fft3 interacted not only with RNAPII subunits but also with various transcription elongation-related proteins, including Spt16 (Fft3 at transcribing regions strongly correlated with the occupancies of Set2 and the levels of Set2-mediated histone H3K36 trimethylation (H3K36me3)Fft3 may regulate RNAPII transcription by interacting with both RNAPII and transcription-elongation factors.The correlation between RNAPII occupancy and the localization or function of Fun30I-CTDS2P , revealing Spt16 . ConsistFft3 localized to transcribing regions might perform this function during RNAPII elongation. To test this possibility, we first examined how nucleosome occupancy is regulated by Fun30Fft3 at transcribing regions during transcription. Comparison of Fun30Fft3-mediated nucleosome loss (histone H3 occupancy changes of fft3Δ cells relative to wild-type) at transcribing regions with RNAPII occupancies revealed that histone H3 occupancy is increased at a number of transcribing regions in fft3Δ cells especially among highly transcribed genes . In this model, we hypothesized that the level of RNAPII-mediated nucleosome disassembly at transcribing regions, denoted by D , is equivalent to the sum of the level of nucleosome loss during transcription, denoted by L , and the level of nucleosome reassembly during transcription, denoted by R . By fitting the available experimental data to the model, we could estimate the relative contributions of Fun30Fft3 in RNAPII-mediated nucleosome disassembly at transcribing regions to be 84% and 54% for RNAPII-CTDS2P and RNAPII-CTDun, respectively (see Methods section for more detail). The levels of Fun30Fft3-mediated nucleosome disassembly at transcribing regions estimated in this way strongly correlated with RNAPII occupancies  rather than the levels of Fun30Fft3–mediated nucleosome loss at transcribing regions  significantly reduced the amounts of soluble and chromatin-bound histone H3 and H4  recovered the RNAPII occupancy of genes with increased histone H3 occupancy in fft3Δ cells. Metagene analysis of the transcribing regions for all protein-coding genes revealed that the transcription-dependent decrease in the RNAPII occupancy of fft3Δ H3/H4ΔΔ cells fell between those of H3/H4ΔΔ and fft3Δ cells  or unstable nucleosomes at the promoter followed by well-positioned nucleosomes over the transcribing regions that are aligned with the transcription start site. Previously, we did not understand in detail how RNAPII overcomes these well-positioned nucleosomes at transcribing regions during transcription elongation. Here we show that Fun30t manner  and 4. E regions . Our res on FACT  and 4, s on FACT . The FAC regions , indicat3Δ cells . To deteΔ cells.  and 6. Ifun30Δ on RNAPII transcription in budding yeast was determined to be marginal. Given the extensive redundancy in the abilities of chromatin remodellers to regulate the nucleosome barrier and transcription initiation at PHO5 promoter of budding yeastin vitro26Drosophila SMARCAD1 facilitates RNAPII transcription both in vitro and in vivoFft3 cooperates with FACT complex to promote RNAPII transcription through chromatin, suggest that Fun30 may have a conserved role in promoting nucleosome disassembly during RNAPII elongation in higher eukaryotes.A role for Fun30 in transcription was also reported in budding yeastFission yeast cells were grown until mid-log phase in suitable media following standard protocol8 cells for H3, RNAPII-CTDun and RNAPII-CTDS2P ChIP and 1.6 × 109 cells for FLAG ChIP were used). The amounts of lysis buffers, antibodies, and agarose beads were increased accordingly. Equal amounts of wild-type cells lacking expression of FLAG-tagged proteins were used as tag-free controls. Cell extracts were prepared in lysis buffer  using the standard bead-beating method. After IP, the agarose bead-bound IP DNA was washed and eluted on an IP column (SigmaPrep spin column). For FLAG ChIP, we used a modified lysis buffer  and performed washes using TBS  and TBS with 0.05% Tween-20 to prevent the M2 antibody from being denatured by sodium deoxycholate. After de-crosslinking, the DNA samples were purified using a Qiagen PCR purification kit. ChIP-seq libraries were constructed from 10 ng of input DNA or 1–10 ng of IP DNA, using a commercial ChIP-seq kit  according to the manufacturer's protocol. For multiplexed libraries, 50-nt single-end reads were sequenced on an Illumina Hiseq 2500. After high-throughput sequencing, the output reads were processed by Trim Galore-driven adapter removal and quality trimming, and aligned to the S. pombe ASM294 genome assembly using the Novoalign tool. If a read was mapped to multiple locations, a single location was randomly selected. The reproducibility between biological duplicates was confirmed, and the aligned duplicates were merged using the bamCorrelate tool of deepToolsS2P ChIP) or mock (for FLAG ChIP) using bamCompare, and average metagene analyses were performed using the computeMatrix and plotProfile tools of deepTools. The normalized data were visualized and extracted using the integrative genome viewer (IGV). Further bioinformatic analyses of the ChIP-seq data were performed using the R software package. Spike-in controls were not included in our ChIP-seq samples. Thus, it should be noted that the relative ChIP-seq signals in our analysis may not be sensitive to a global change.ChIP cells were fixed with 1% formaldehyde for 15 min (for cells grown at 30 °C), 10 min (for cells grown at 37 °C) or 40 min (for cells grown at 22 °C), and then collected. Since high-throughput sequencing requires a sufficient quantity of immunoprecipitated (IPed) DNA to yield an optimal signal, we cultured many more cells for the ChIP-seq experiments than for the conventional ChIP experiments  become,The levels of histone H3 occupancy changes at transcribing regions in x cannot be 1 as the observed values of ΔH3fft3Δ are non-zero. We postulated Rspt16-18 approximately zero because spt16-18 cells lack an essential function to reassemble nucleosomes during RNAPII transcription. Given this,The constant spt16-18 cells relative to wild-type cells become,Then the levels of histone H3 occupancy changes at transcribing regions in This led to the formula,x indicates the decrease in the levels of nucleosome disassembly in fft3Δ cells relative to wild-type cells; thus, we defined this as the relative contribution of Fun30Fft3 in RNAPII-mediated nucleosome disassembly at transcribing regions. Rewriting the formula according to this definition becomes,The value 1–x′ is the relative contribution of Fun30Fft3 in RNAPII-mediated nucleosome disassembly at transcribing regions.where Dspt16-18 should be coupled to transcription, the most likely value of x′ could be determined by identifying x′ (0<x′≤1) which results in the maximum correlation between Dspt16-18 and RNAPII occupancies (Since upancies .x′ to be 84% (r=0.71) and 54% (r=0.67) for RNAPII-CTDS2P and RNAPII-CTDun, respectively. We could estimate Dspt16-18 accordingly. By Assumption 1, Dwt, Dfft3Δ and Dspt16-18 are proportional to each other. Thus, the levels of Fun30Fft3-mediated nucleosome disassembly at transcribing regions (x′· Dwt) is proportional to Dspt16-18, which leads to,Based on this plot, we could estimate the most likely values of Fft3-mediated nucleosome disassembly at transcribing regions (x′· Dwt) estimated on an arbitrary scale and RNAPII occupancies as well as RNAPII occupancy changes in fft3Δ cells , the α-RNA polymerase II CTD repeat YSPTSPS with phosphorylation at Ser2 , α-FLAG M2 , and α-beta actin  were used for ChIP-seq, co-immunoprecipitation and western blot. In addition, the α-H3 antibody , which was produced in-house against recombinant yeast histone H3Antibodies against α-RNA polymerase II CTD repeat YSPTSPS without phosphorylation  under accession number GSE83600. Mass spectrometry data can be accessed from the Stowers Original Data Repository at http://www.stowers.org/research/publications/libpb-1085. The authors declare that all the data supporting the findings of this study are available within the article and its Supplementary Information files and from the corresponding author upon reasonable request.The raw and processed sequencing data from this publication have been submitted to the NCBI Gene Expression Omnibus .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures and Supplementary Tables


MYC promoter to modulate transcription through their downstream effectors. Critically, even small increases in MYC abundance (<2 fold) are sufficient to drive overproliferation; however, the details of how oncogenic/growth signaling networks regulate MYC at the level of transcription remain nebulous even during normal development. It is therefore essential to first decipher mechanisms of growth signal-stimulated MYC transcription using in vivo models, with intact signaling environments, to determine exactly how these networks are dysregulated in human cancer. This in turn will provide new modalities and approaches to treat MYC-driven malignancy. Drosophila genetic studies have shed much light on how complex networks signal to transcription factors and enhancers to orchestrate Drosophila MYC (dMYC) transcription, and thus growth and patterning of complex multicellular tissue and organs. This review will discuss the many pathways implicated in patterning MYC transcription during development and the molecular events at the MYC promoter that link signaling to expression. Attention will also be drawn to parallels between mammalian and fly regulation of MYC at the level of transcription.The transcription factor and cell growth regulator MYC is potently oncogenic and estimated to contribute to most cancers. Decades of attempts to therapeutically target MYC directly have not resulted in feasible clinical applications, and efforts have moved toward indirectly targeting MYC expression, function and/or activity to treat MYC-driven cancer. A multitude of developmental and growth signaling pathways converge on the  MYC expression [MYC upregulation is a key driver of tumorigenesis ,2,3. Funpression ,13.Drosophila, prior to the advent of genome-wide deep sequencing, implicated dMYC in controlling expression of 10%–15% of all genes [Myc expression, revealing a global positive correlation between level of gene expression, active chromatin marks and MYC binding across the genome [MYC heterodimerizes with its partner bHLH protein MAX in order to bind DNA and activate transcription ,15. MYC-ll genes ,19. The ll genes ,5,6,7) wll genes ,20, enrill genes ,22. Nevell genes ,24,25,26e genome . This obMYC overexpression alone does not induce aggressive tumours, in the context of dysregulated growth signaling, the ability of MYC to amplify all active transcription could lead to tumourigenesis. For instance, elevation of MYC will not only enhance cellular overgrowth and tissue hyperplasia (by binding promoters that are active in proliferating tumour cells), but will also upregulate expression of weakly expressed cancer-promoting targets to accelerate tumour progression. The expanse of MYC’s transcriptional capacity is reviewed comprehensively elsewhere [MYC must be tightly controlled.So how does the transcriptional signature associated with increased MYC arise in both normal and tumour cells? At the broadest level, global amplification as a result of elevated MYC levels will increase the output of previously active transcriptional programs, potentiating the growth and proliferation programs already operational in developing organs, proliferating cell culture systems or tumours. The observation that MYC binding may not always result in productive transcriptional outcomes  could allsewhere , but theMYC family benefits somewhat from the redundancy of three family members, MYC, MYCN and MYCL  in the context of wild type extraembryonic structures (trophectoderm and primitive endoderm), generates embryonic organs without gross developmental abnormalities [Myc depletion; thus, placental rescue pups are severely anaemic due to loss of hematopoietic stem cells (HSCs) [Myc with Mycn results in viability, demonstrating that MYCN can provide functional compensation when MYC is absent [In mammals, the lsewhere ,30). Mycferation . Some MYferation . Interesmalities . The excs (HSCs) . Strikins absent .Myc depletion, and inability of Mycn expressed from its endogenous locus to compensate in the placenta and hematopoietic lineage, likely reflects large differences in expression levels of the paralogs between tissues. Myc is expressed broadly in developing mice, with particularly high levels of mRNA in thymus, spleen, and liver, while Mycn is highly expressed in the brain and kidney [Mycn, which impairs neural stem and progenitor cell proliferation, while deletion of Myc does not alter these lineages [The sensitivity of certain lineages to d kidney . The cerlineages . In the lineages . Mycn and Myc are only coexpressed in immature HSCs, and Mycn transcripts decrease following HSC differentiation [Mycn and Myc double knockout hematopoietic phenotype is significantly more severe than individual knockout of either gene [Myc prevents HSCs from differentiating into progenitors due to inability to exit the niche [Myc, deletion of Mycn alone does not impair HSC number or steady-state hematopoiesis [In the hematopoietic lineage, ntiation . In accoher gene . Althoughe niche , the remopoiesis . TherefoMyc depletion can lead to increased Mycn abundance [Mycn into the endogenous Myc locus rescue the Myc null? Expression of the Mycn gene from the Myc locus reflects the endogenous Myc patterning, suggesting that transcriptional control of the Myc locus is critical for normal development. Thus, despite the ability of MYCN to functionally replace MYC during development , and thebundance , MYC andDrosophila, the single orthologue (dMYC) has enabled MYC function to be discerned without concern for redundancy. The functional conservation of MYC between invertebrates and vertebrates is stunning. Not only can expression of human MYC rescue lethal dMYC mutations in flies [dMYC can provide competitive advantage to Myc null mouse fibroblasts [V1Ras2 alleles to rescue proliferation defects and induce oncogenic transformation in Myc null rat fibroblasts [Drosophila dMYC regulates cell growth and progression through the cell cycle [Drosophila was the first organism where cell growth impairment phenotypes  were associated with dMYC reduction and diminished organ and tissue growth [dMYC mutant flies are smaller than wildtype, with normal organ proportions, albeit composed of cells reduced in size [dMYC overexpression increases ribosome biogenesis and tissue growth to result in overgrown flies [In in flies , but exproblasts  and cooproblasts . Thus, lll cycle ,43,44,45e growth . Specifi in size . In line in size ,46,47,48wn flies .MYC levels. Even small changes in MYC levels are sufficient for cell and tissue overgrowth [Ras or EGFR where mutations constitutively activate the protein [MYC expression must not only be tightly restricted, but also maintain capacity to rapidly respond to developmental growth signals according to the cell’s requirements. Since transcriptional upregulation of MYC expression can trigger and propagate tumour pathogenesis, we focused the next part of this review on the mechanisms orchestrating expression of MYC under normal circumstances and reflect on their disarray in the context of cancer.The potent oncogenic capacity of MYC is the downside of its efficiency to normally drive growth and proliferation in developing organisms, and once again highlights the need for precise regulation of ergrowth ,51, in c protein . To enabMYC transcription must be integrated with those altering mRNA and protein abundance. mRNA stability and translation of both mammalian MYC and Drosophila dMYC is modulated by numerous microRNAs (miRNAs) [MYC coding sequence in the context of cancer [For MYC to drive the major cellular growth and proliferation events orchestrating development, abundance is predominantly regulated at the levels of transcription and mRNA/protein stability. To attain the level of MYC appropriate to cell fate, signaling inputs to (miRNAs) ,58,59,60(miRNAs)  can be i(miRNAs) ,63,64,65(miRNAs) ,67,68,69f cancer ,71 tend f cancer ,73, althf cancer , but migMYC patterning; MYC must be appropriately expressed in a subset of tissues and repressed in others. Early human cell culture studies demonstrated that MYC expression is rapidly activated by growth factors in serum, and maintained in proliferating stem cells/progenitor cells, but becomes downregulated in response to differentiation signals [MYC promoter to pattern cell and tissue growth is outlined briefly below, but is more comprehensively reviewed elsewhere [Ultimately, increased MYC mRNA/protein relies on production of mRNA via transcriptional changes in response to extracellular stimuli and developmental growth signals. The orchestration of cell growth and cell cycle for tissue and organ development of multicellular organisms requires  signals ,76,77. Alsewhere .dMYC expression have been particularly well studied in the Drosophila wing imaginal disc, where dMYC drives cell growth and G1 to S phase progression [dMYC expression [MYC promoter in association with tissue overgrowth and tumour predisposition [Myc in the crypt and CyclinD1 in the villi distinct cell populations [Links between upstream pathways controlling gression ,78,79. Tpression . On the pression , highligposition ,83,84,85position , inducinulations .Drosophila studies have enabled the dissection of interplay between Wg/Wnt and the Notch pathway, another major architect of development frequently linked with tumour progression [dMYC indirectly via the Wg/Wnt pathway to control dMYC expression, and thus promote cell cycle arrest and differentiation in the fly wing [dMYC gene, increasing dMYC expression and inducing wing overgrowth independently of Wg/Wnt [Drosophila muscle cell precursors, Notch activation also increases dMYC expression [dMYC expression via induction of the E(spl)m8 transcriptional repressor, which negatively regulates dMYC in the wing [Myc by β-catenin [Myc transcription by interacting with the Su(H) orthologue CBF1 in murine mammary cells [MYC is an essential NOTCH1 target for tumour progression in mammalian T cell lymphoblastic leukaemia (T-ALL) cells [Myc promoter is directly activated as a result of binding by intracellular domain of NOTCH and CBF1. In mouse models for pancreatic neoplasia, specific NOTCH2 activation of Myc is also required for tumour development [gression ,89. Notcfly wing ,92,93,94f Wg/Wnt . In Drospression . Notch fthe wing . In the -catenin . Mammaliry cells . MoreoveL) cells  and mousL) cells ,101, wheelopment .MYC promoter to the master developmental regulators WNT and NOTCH, a myriad of cellular signalling cascades possess capacity to influence MYC transcription. In the wing disc, the Decapentaplegic  morphogen controls dMYC expression, albeit indirectly by downregulating the transcriptional repressor Brinker (Brk), which normally acts to suppress dMYC [MYC via direct activity of the downstream target SMAD3/SMAD4 on the MYC promoter [Drosophila midgut, JAK/Stat and EGFR activity increase dMYC promoter activity [MYC promoter is activated in response to JAK signaling, but independently of STAT3 [MYC expression via direct binding of the downstream transcription factor FOXO [Drosophila dMYC transcription is also activated directly by the downstream effector of Hippo pathway, Yorkie (Yki) [Drosophila genetics has enabled dissection of complex networks controlling MYC abundance in multicellular organisms under conditions of normal development, thus enhancing our understanding of the mechanisms of MYC dysregulation in disease.Further to the responsiveness of the ess dMYC . Testamepromoter . In Drosactivity , while tof STAT3 . The instor FOXO . Drosophie (Yki) , and oveie (Yki) . The funMYC promoter architecture came from the observation that MYC-driven malignancy was often associated with rearrangements of the MYC locus and, thus, dysregulation of transcription. In particular, the B-cell malignancy Burkitt’s Lymphoma is associated with reciprocal chromosomal translocations between MYC and one of 3 immunoglobulin loci [MYC and the immunoglobulin loci vary considerably, being either internal to the MYC transcriptional unit or up to several hundred kilobases away. Interestingly, the translocated MYC alleles contain either truncated or mutated exon 1, which alleviates the tightly regulated attenuation of RNA Polymerase II (Pol II) transcriptional elongation [MYC expression [MYC promoter induces transcriptional down-regulation of MYC and differentiation in human leukaemia cell lines [MYC promoter [MYC locus identified Pol II accumulation downstream of the MYC transcription start site, engaged but unable to proceed to productive elongation [MYC receives multiple activation signals, post initiation control via Pol II pausing provides opportunity for rapid transcriptional activation in response to developmental signaling, which could not be achieved by Pol II recruitment alone.Early interest in lin loci ,111. Theongation , enablinpression ,113,114.ll lines . This blpromoter ,116. In ongation . As MYC MYC promoter correlates with activity, being more sensitive in cells actively transcribing MYC compared with MYC-silent cells [MYC promoter. The disruption to DNase hypersensitive elements in Burkitt’s lymphoma cells [MYC promoter as a means of understanding mechanisms of this MYC-driven cancer. Of the many sequence-specific binding sites predicted for the MYC promoter based on the nuclease sensitivity assays, induction of differentiation and downregulation of MYC expression is only associated with lost binding activity of the far upstream sequence element (FUSE) 1.5kb upstream of the P1 promoter [At promoters, melting of duplex DNA is obligatory for Pol II entry and transcriptional initiation. Indeed, DNase hypersensitivity of the human nt cells ,115,118.promoter . MYC [MYC gene [MYC [MYC coding strand reveals hyperreactive thymidine residues in FUSE, consistent with an open single strand extending from FUSE toward P1 [MYC promoter structure is essential for tight regulation of MYC transcription.Oligonucleotide affinity chromatography using the FUSE retrieve the FUSE binding protein  from proliferating cell extracts, with binding dramatically decreasing following induction of differentiation and down regulation of MYC . PrefereMYC gene ; howeverMYC gene . Indeed,ene [MYC . Permangoward P1 . In contoward P1 . Interesoward P1 . Indeed,MYC expression [MYC promoter activity. Under the circumstances where MYC transcription initiates  FUBP1 binding to this pre-activated MYC promoter would enable release of the paused Pol II to drive promoter escape and enhance transcriptional elongation. The observation that FUBP1 binds the single-stranded FUSE, in preference to the double-stranded sequence, suggests that formation of the FUBP1-DNA complex requires prior unwinding of the DNA helix . In accopression . FactorsMYC transcription. Moreover, energy generated from promoter unwinding can be harnessed as a productive force in MYC transcription [MYC expression will be associated with region-specific destabilisation of B-DNA in torsionally strained regions of the active MYC promoter, and forward movement of Pol II will generate and transmit negative supercoils to the FUSE to enable FUBP1 binding. Thus, MYC promoter activity associated with transcription initiation enables FUBP1 binding to FUSE to maximize Pol II release and activation of MYC transcription [Hence, in addition to conventional dsDNA-binding transcriptional regulators, activity of single-strand nucleic acid binding proteins can drive cription . Specificription . MYC promoter; however, the physiological outcomes of single-strand-specific FUBP1 binding during development is still relatively obscure. Early studies demonstrated that reduced FUBP1 levels and/or activity diminishes MYC expression and cell proliferation in ex vivo cell culture systems [Fubp1 gene trap mice, with embryonic lethality (around E15.5) associated with anaemia [Fubp1 knockdown. Fubp1-deficient adult HSCs exhibited increased expression of key cell cycle  and pro-apoptotic (Noxa) genes based on mRNA expression profile data [p21 expression [Fubp1 knockout phenotype suggests that MYC could be a key downstream target that mediates these effects. Further studies are therefore required to determine if these transcriptional changes are direct, or occur indirectly, via altered MYC expression.Interaction between FUBP1 and the single-stranded FUSE is essential for maximal activation of the  systems . FUBP1 k systems . Essenti anaemia . Secondaile data . Given tile data , and thepression , the FubFubp1 knockouts display variable penetrance; although mice start dying at embryonic day 10.5 they can survive until birth [Fubp1 knockout had normal numbers of HSCs, but transplantation of Fubp1 loss-of-function HSCs into irradiated mice failed to reconstitute haematopoiesis [Fubp1 gene trap, which reduces but does not eliminate reconstitution capacity [il birth . Broaderopoiesis . The discapacity , suggestMyc mRNA levels varied dramatically between individual ex vivo cultures of Fubp1 knockout mouse embryo fibroblasts (MEFs) harvested from different Fubp1 embryos [Myc impairs recruitment of FIR  and results in a failure to shut Myc down, thus generating transcriptional noise. Another possibility is redundancy and/or compensatory activity of the other FUBP family members, FUBP2 and FUBP3. Like FUBP1, FUBP2 and FUBP3 can bind sequence specifically to the non-coding strand of FUSE, and possess potent transcriptional activation domains in vitro [MYC, it will be of great interest to determine whether FUBP1 loss-of-function leads to heightened FUBP2 activity and the phenotypic outcome of double knockout. In short, these mouse studies leave the major function of FUBP1 during development requiring clarification. Interestingly,  embryos . The reain vitro . FUBP2 win vitro . Given FMYC transcription. A co-immunoprecipitation (co-IP) screen in HeLa cells using purified components of the basal transcription apparatus available at the time  detected association of FIR with TFIIH, but not with GTF complexes necessary at earlier stages of promoter recognition and preinitiation complex (PIC) formation [Yeast two-hybrid screens, using FUBP1 as bait, identified the FUBP1 interacting repressor (FIR) as a potent binding partner . FIR behormation  ormation  requiredormation ,138,139,ormation ,141,142.MYC activation by decreasing XPB helicase activity, to permit only basal transcription [MYC promoter integrates multiple signaling inputs, but regulation by TFIIH at the level of promoter clearance would provide an opportunity to control transcription post-initiation. Repression of TFIIH by FIR, late in the transcription cycle, would provide an additional regulatory mechanism to safeguard against inappropriate or excessive signal-induced MYC activation. In context with the post-initiation block controlling MYC transcription [MYC repressor, as FIR knockdown is associated with MYC dysregulation ex vivo [FIR therefore blocks FUBP1-dependent cription . The MYCcription ,112,114, ex vivo  and loss ex vivo . MYC promoter and a pulse  of MYC transcription [MYC promoter following signal-stimulated MYC transcription, achieved by serum stimulation of previously starved human fibroblasts [MYC enhancers and activators are first to load, enrichment for FUBP1 is subsequently detected, prior to decreased Pol II loading  and increased abundance of MYC mRNA [MYC mRNA levels, FUBP1 and FIR initially co-localise; however, once MYC returns to basal levels FUBP1 exits and only FIR is detected on FUSE. Thus, maximal activation of MYC transcription correlates with dissociation of RNA Pol II and recruitment of FUBP1 [MYC mRNA levels, is consistent with FUBP1 promoting RNA Pol II release to hyperactivate MYC transcription. Serum-stimulation of mammalian tissue culture cells results in rapid activation of the endogenous cription . Elegantroblasts . MYC enhMYC mRNA . Followiof FUBP1 . Pol II MYC expression in response to signaling inputs.The time course ChIP data supported findings from electrophoretic mobility shift assays (EMSA), demonstrating that FUBP1 and FIR binding occurs simultaneously; FIR addition enhances FUBP1 binding with FUSE; i.e., FUBP1’s affinity within the ternary FUBP1-FIR-FUSE complex is stronger than the FUBP1-FUSE interaction alone . MoreoveMYC transcription by stimulating XPB helicase activity and decreasing the frequency of Pol II pausing normally associated with the MYC promoter. FIR interacts with XPB/TFIIH to delay escape of Pol II from the promoter post-initiation. Such fine-tuning of transcription is essential for genes receiving multiple activation signals and requiring exquisite regulation like MYC, where recruitment alone would not provide sufficiently stringent control. Indeed, the early analysis of MYC dysregulation in Burkitt’s lymphoma revealed the importance of transcriptional regulation post-initiation, with precocious Pol II escape associated with MYC-driven pathology [We predict that FUBP1 normally increases athology ,112,114.MYC transcriptional activators, they are defective in both FUBP1 activation and FIR repression [MYC TSS in cell lines derived from XP patients with C-terminally truncated XPB, suggesting a failure to form the FUBP1-FIR-XPB/TFIIH bridge as a consequence of the defective interaction between XPB and FIR. Interestingly, three different XP patients with mutations causing loss of the wild type C-terminus of XPB all display UV sensitivity, developmental and aging disorders, but have very different cancer predispositions  [MYC oncogene and contribute to XP-related neoplasia.In addition to the essential function of TFIIH in basal transcription, the complex is essential for DNA excision repair . Mutatiopression . In addipression . The FUB cancer) . TogetheDrosophila ortholog of FIR, Half pint (Hfp), behaves as a tumour suppressor, with reduced abundance of Hfp resulting in tissue overproliferation and larval overgrowth [Drosophila XPB homolog [Hay mutants display phenotypes consistent with DNA repair and transcription defects [Hay in Hfp loss-of-function cells reduces dMYC expression and cell growth [Drosophila Hfp-Hay in terms of transcriptional regulation of MYC. The Drosophila models therefore provide the means to dissect molecular mechanism(s) of defective repression of dMYC and tissue overgrowth associated with Hfp loss-of-function. Not only did Hay/XPB mutants—C-terminally truncated like the human disease alleles—strongly enhance cellular overproliferation and tissue overgrowth normally associated with the Hfp depletion, but also further impaired Pol II pausing and exacerbated dMYC derepression [MYC transcription, Hfp interacts with XPB/TFIIH to maintain a pool of engaged Pol II on the dMYC promoter in vivo [MYC control by FUBP1/FIR is shown in MYC might contribute to hyperproliferation and cancer associated with XPB-related human diseases.The ergrowth ,150. Hay homolog ,152, and defects . Furtherpression . Thus, c in vivo . An overMYC promoter to integrate extracellular and developmental signals is fundamental to patterning of growth in multicellular animals. Although connections between signaling and patterning of dMYC transcription have been well delineated in Drosophila [dMYC transcription via P-element somatic inhibitor (Psi), the sole ortholog of the three mammalian FUBP proteins. Psi was originally ascribed the function of modulating splicing of transposable P-elements in Drosophila [Drosophila melanogaster, only entering the genome within the past 50 years [Drosophila development [The capacity of the osophila ,79,155, osophila . However50 years , this fu50 years . In accoelopment . The Psielopment ) was preelopment . Psi alselopment .Drosophila [kohtalo and skuld, which encode Drosophila homologs of the MED12 and MED13 subunits of the kinase module, are essential for the transcription of Wg/Wnt and Notch pathway targets and, thus, required to establish compartment boundaries of the wing imaginal disc [MED26 null mutant wing disc clones [Minute locus mutation, revealed a requirement for MED15 in expression of selected Dpp target genes, but not EGFR or Wg target genes [Of the Psi-interactors with designated functions in Pol II transcription (63% of the top 65 Psi-interactors), 32% were part of the chromatin-remodeling machinery, 12% gene specific transcriptional regulators, but by far most proteins (56%) comprised subunits of the transcriptional Mediator (MED) complex . The MEDosophila ,173,174.nal disc ,175,176.dMYC. In line with an activating role in dMYC transcription, Psi is required for maintaining dMYC mRNA at endogenous levels, with the latter being significantly decreased following Psi knockdown. In accordance with dMYC depletion being due to diminished transcriptional activity, ChIP revealed that depletion of Psi decreases enrichment of initiating Pol II (S5 phosphorylated) and elongating Pol II (S2 phosphorylated) across the dMYC gene. The observation that Psi activates from a single DNA binding site within a minimal promoter [dMYC transcription downstream of pre-initiation complex assembly.In general, the large (~30 subunit) MED complex can behave either as an activator or inhibitor of Pol II-dependent transcription. The “small” or core MED complex is required for activation of Pol II transcription. The “large” complex has been predominantly characterised as a transcriptional repressor and it contains an additional 4 proteins; the kinase module comprising the Cyclin dependent kinase complex (CDK8/CycC) and 2 additional MED subunits  . The imppromoter  (as obsepromoter ), compardMYC dysregulation is capable of such potent modification of the Psi knockdown phenotype due to MYC’s capacity to act as a transcriptional amplifier [dMYC transcription, cell and tissue growth. Further studies are required to determine whether human FUBP1 also interacts with MED to modulate expression of the mammalian MYC oncogene. As even subtle increases in MYC expression (>2 fold) can promote the cell and tissue overgrowth fundamental to cancer initiation and progression, these observations will have implications for human disease [Psi interacts with core Pol II machinery to maintain cell and tissue growth, which is of great interest given the proficiency of the MYC to drive cell growth ,42. We pmplifier ,24. In t disease . Our understanding of transcriptional regulation has been greatly extended by elucidation of the 3-dimensional structure of the mammalian genome ,178,179.Drosophila genome is similarly arranged into physical domains that form visible structures in polytene chromosomes of the salivary glands [Drosophila cohesin maintains genome structure by interacting with genes bound by paused Pol II to regulate gene expression within TADs [Drosophila CTCF also binds distinct sites to enable mitotic bookmarking , in proin marks . Highliga genome . In the MYC locus, early studies identified CTCF as a repressor of the chicken MYC gene [MYC transcription by cohesin is also conserved in zebrafish [Myc gene [MYC, located within the insulated neighbourhood associated with T-cell acute lymphoblastic leukemia (T-ALL) pathogenesis [MYC locus is rearranged as a consequence of infection with the lymphoma-associated Epstein-Barr virus (EBV) [MYC promoter interactions with several upstream long range enhancers (ranging from -556kb to -168kb upstream of TSS) to drive MYC activation [MYC transcriptional control could drive cancer initiation. There are emerging links between the 3D genomic architecture and developmental signaling inputs, with NOTCH-mediated MYC activation occurring through direct binding at long-distance elements capable of forming loops with the MYC promoter [MYC patterning, both specifically on the MYC locus and more globally, are awaited with great interest.In regard to the MYC gene . Regulatebrafish . StudiesMyc gene ,191. Theogenesis . Moreoveus (EBV) . Transactivation . Thus, bpromoter ,194. FutMYC transcriptional regulators would be informative for many current cancer therapies, where inhibition of signaling pathway components (particularly kinases) results in rapid development of drug resistance [Links to upstream pathways that input sistance ,196. Comsistance ,198. TheMYC transcription could be a promising treatment avenue.As an excellent proof of principle, MYC can be targeted in transgenic mouse models of disease by expression of the dominant negative Omomyc ,200. ThiMYC at the transcriptional level has been demonstrated using the bromodomain and extraterminal (BET) protein inhibitor JQ1. Although initially explored as a means of modulating MYC activity by interfering with MYC-associated acetylated histones, JQ1 regulates BRD4 to limit MYC expression in cancer cells [MYC transcription by binding a super-enhancer 1.7Mb downstream of the MYC promoter [MYC-specific activation by a factor originally characterized as playing general transcriptional co-activator roles [-secretase, an inhibitor of NOTCH-mediated MYC activation [The potential success of targeting er cells . BRD4 mopromoter ,208  and reestablishment of pausing (via FIR). The importance of these single-stranded nucleic acid binding proteins to MYC control in vivo is highlighted by their conservation between mammals and invertebrates, with Drosophila FIR/Hfp being essential for repression of dMYC [dMYC mRNA levels under signaling conditions conducive to growth in the fly wing [MYC transcription. Here we have brought together a snapshot of the vast web of signals integrated by the  of dMYC ,150,154,fly wing . The capDrosophila mass spectrometry screens [MYC promoter to elicit MYC-specific regulatory effects and/or modifies BRD4/Brm activity in order to promote release of Pol II. In addition, 4C analysis of the Drosophila dMYC promoter will undoubtedly identify long-distance elements interacting with the Brm complex, MED and Psi/FUBP1. Intriguingly, the bromodomain complex proteins were also detected in complex with Psi/FUBP1 in  screens , includi screens . InvestidMYC expression and promoting cell growth [Drosophila development [dMYC promoter activity measured by dMYC-lacZ reporter activity and enrichment of phosphorylated Pol II across the dMYC promoter [MYC promoter to modulate DNA topology and Pol II activity will be extremely important. Of great interest will be investigation of potential interactions with MED and the other single-stranded DNA-binding proteins implicated in MYC transcription, i.e., FUBP1/Psi and Hfp/FIR. We therefore invite future studies interrogating mechanisms by which FUBP1/Psi, FIR/Hfp and B52 sense the cellular signaling environment and how these factors modulate MYC promoter architecture to control cell and tissue growth during development, as these will shed light on MYC-dysregulation in cancer.Recently the RNA-recognition motif (RRM)-containing protein B52, orthologous to mammalian Serine/arginine-rich splicing factors (SRSF1-6), was implicated in regulating l growth . B52 is elopment , B52 ovepromoter . Determi