Stratification of enhancers by signal strength in ChIP-seq assays has resulted in the establishment of super-enhancers as a widespread and useful tool for identifying cell type-specific, highly expressed genes and associated pathways. We examine a distinct method of stratification that focuses on peak breadth, termed hyperacetylated chromatin domains (HCDs), which classifies broad regions exhibiting histone modifications associated with gene activation. We find that this analysis serves to identify genes that are both more highly expressed and more closely aligned to cell identity than super-enhancer analysis does using multiple data sets. Moreover, genetic manipulations of selected gene loci suggest that some enhancers located within HCDs work at least in part via a distinct mechanism involving the modulation of histone modifications across domains and that this activity can be imported into a heterologous gene locus. In addition, such genetic dissection reveals that the super-enhancer concept can obscure important functions of constituent elements. Super-enhancer are usually defined by high levels of chromatin modification and associate with cell-specific gene expression. Here, the authors define hyperacetylated chromatin domains (HCDs) by using histone hyperacetylation peak breadth information and show that HCDs associated more closely with cell identity than super-enhancers. They are also historically characterized by their ability to function over distances of anywhere from 100 bp to more than 1 Mb. Whole-genome methods of identifying enhancers rely on associated histone modifications, common transcriptional cofactors, and/or cell type-specific transcription factors (TFs). These methods suggest that mammalian genomes harbor a large number of enhancer sequences — perhaps 1–2 million — and that they represent the most numerous and significant cis-acting sequence determinants of cell-type-specific gene expression.Enhancers are 5.Fundamental issues regarding enhancer function, however, remain unclear. For example, the dominant model for how enhancers communicate with their cognate gene promoters, termed looping, involves direct interactions between factors bound to enhancers and factors bound near promoters. Evidence for such interactions, however, has provided little insight into how a distal enhancer finds a gene promoter, or how it distinguishes among potential promoters in gene-dense regions. Moreover, some evidence suggests that mechanisms of enhancer-promoter communication may be more varied8. Obvious functional differences have not emerged from studies of super-enhancers, however, with the only reported distinction being a higher diversity and/or number of TF binding sites mapping to super-enhancers10. Mechanistically, there is as yet no indication that, aside from the strength of activation, these sequences are intrinsically different from other enhancers that exhibit weaker signals for enhancer-defining marks.Additional insight into enhancer function can be obtained by discerning functional differences between them. One attempt to classify enhancers according to strength, as determined by the signal intensity in ChIP-seq assays for associated enhancer marks, has led to the classification of super-enhancers, which have been shown to be associated with highly expressed genes that define cell identity12. Our results suggested a distinction between enhancers that mediate broadly distributed changes in chromatin structure vs enhancers that work via other mechanisms.We have previously investigated enhancer-associated histone modifications from the perspective of peak breadth, as opposed to signal strength. We characterized specific regions as hyperacetylated chromatin domains (HCDs), defined as continuous genomic regions exhibiting significant enrichment for histone hyperacetylation and other marks associated with active transcription, such as H3K4 dimethylation (H3K4Me2). From this analysis, we identified a novel enhancer within the murine β-globin locus, termed HS-E1, which is required for the formation of such a broad region of histone hyperacetylation encompassing the two genes within the cluster that are expressed during primitive erythropoiesisTo further investigate this, we perform ChIP-seq analyses of specific histone modifications in primary murine erythroid, retinal, and intestinal epithelial cells and rank peaks according to breadth. We find that an HCD ranking that utilizes a combination of two histone marks suffices to identify a subpopulation of genes that are both more highly expressed and more cell type-specific than those associated with super-enhancers. Moreover, the deletion of enhancers found in loci with or without an HCD identifies functional differences between enhancers that have the ability to modulate long-range chromatin structure and those that do not. Insertion of an HCD-associated enhancer into another locus results in the formation of an HCD, further suggesting a distinct function for this class of enhancer.12. In an effort to define the genome-wide distribution of such domains, we analyzed (1) ChIP-seqs we performed using e14.5 murine fetal liver, which is comprised of 70–80% erythroid cells, using antibodies specific for H3K27 acetylation (H3K27Ac), and for H3K4 mono-, di- and trimethylation ; (2) ATAC-seq13 we performed using sorted proerythroblasts from e14.5 murine fetal liver; and (3) publicly available ChIP-seq data sets from e14.5 murine fetal liver for the erythroid TFs GATA-1 and SCL/Tal1 (ref. 14)  peaks by breadth. The establishment of a formal definition for a domain requires the application of a subjective cutoff value; for our purposes, rather than an absolute peak breadth we chose the top 2% of MACS2 peaks ranked by breadth. We did this because we found that a cutoff based on ranking, as opposed to absolute peak breadth, translated more consistently between different data sets. Our other criterion was that this produced a list of HCDs that was comparable in size to the list of super-enhancers called from the same data sets (see below), and thus facilitated a comparison of the two methods.We identified HCDs using our H3K27Ac and H3K4Me2 ChIP-seq data sets derived from e14.5 murine fetal liver by ranking MACS2 , and then applied the 2% cutoff to each intersection, resulting in 420 H3K27Ac and 760 H3K4Me2 peaks. We then identified the H3K27Ac and H3K4Me2 peaks that overlapped within the genome and merged them by taking their union. This resulted in a final tally of 216 regions we term hyperacetylated chromatin domains (HCDs) in primary murine fetal liver  and a ±500 bp TSS exclusion zone, which resulted in 307 super-enhancers ranked on H3K27Ac and 214 on H3K4Me2. To identify super-enhancers for both H3K27Ac and H3K4Me2 we took the union of the 307 and 214 super-enhancers, only where these regions overlapped, for a final tally of 173 murine erythroid super-enhancers.  peaks to identify a set of enhancers in fetal liver17, we then compared expression levels for genes associated with HCDs and genes associated with super-enhancers , and so we used this method to associate super-enhancers with genes. We used the same approximation for HCDs, although for 205 out of the 216 regions we have classified as HCDs in murine fetal liver, the nearest active genes are actually located within them. The remaining 11 HCDs harbor peaks of H3K4Me3  cells26. These are transformed cells that exhibit a phenotype similar to proerythroblasts, which can be induced to mature (but not enucleate) by incubation in 2% DMSO. Maturation is associated with cessation of cell division in a majority (>90%) of cells and dramatic upregulation of erythroid-specific genes.Our previous characterization of HS-E1 within the β-globin locus as an enhancer required for the formation of an HCD implied that classification of enhancers according to peak breadth could distinguish between enhancers with different functionsGypa), a late-stage erythroid cell surface marker that is highly upregulated upon MEL cell maturation. The region exhibits a domain that encompasses the gene promoter and a pair of putative enhancers within the third and fourth introns of the gene  MEL cells, which more closely resemble e14.5 fetal liver than undifferentiated MEL cells, and saw that Gypa expression decreased >10-fold in cell lines harboring the deletion ; the latter is located near the 3′ end of the annotated Bloodlinc gene.Another HCD occurs within the SLC4A1 is not expressed at measurable levels in proliferating MEL cells, so we confined our analyses to differentiating MEL. Interestingly, individual deletions of either enhancer result in reductions in SLC4A1 mRNA levels  the +1.1 kb enhancer deletion has no significant effect on these modifications in the vicinity (±1 kb) of the −10.4 enhancer. Thus, at this locus, the HCD requires the activity of two enhancers, neither of which is sufficient for domain formation in the absence of the other. As with the Gypa locus, however, the HCD appears to be a consequence of enhancer activity and is associated with high-level gene expression.We evaluated the effects of separate CRISPR/Cas9-mediated deletions on these enhancers. Tspan32, a member of the tetraspanin superfamily. The super-enhancer at this locus includes three putative elements, marked by GATA1 binding, located at −17, −10, and +0.7 kb relative to the TSS. We created deletions of each enhancer individually by CRISPR/Cas9-mediated genome editing in MEL cells. Notably, measurement of Tspan32 gene expression in differentiating MEL cells indicated that only deletion of the +0.7 enhancer had a significant effect  in the region encompassing the Tspan32 gene promoter  that contains two other genes expressed in erythroid cells, Cd81 (encoding another tetraspanin superfamily member) and Tssc4 (encoding a protein of unknown function), located 48 and 64 kb from the Tspan32 TSS, respectively. Surprisingly, the deletion of the +0.7 enhancer had significant effects on the expression of both genes cts Fig. . This sulls Fig. . This suGypa and SLC4A1 gene loci requires the activity of specific enhancers within them. We, therefore, asked if the Gypa enhancer was also sufficient to form an HCD. To do this, we employed a CRISPR-in strategy, in which a portion of the Gypa enhancer was inserted into the location of the −10 kb element at the Tspan32 locus. This was accomplished using a repair template consisting of the Gypa enhancer sequence flanked by homology arms, targeted to the site of the deleted Tspan32 −10 kb element. We find that in subclones homozygous for this insertion, expression of the Tspan32 gene increases in differentiating MEL cells There are several potential explanations for the differences in genes and pathways identified by HCD (peak breadth) vs super-enhancer analysis. Assignment of genes to super-enhancers relies on the assumption that a given enhancer regulates the nearest active gene. This is, at best, an approximation, the accuracy of which is impossible to evaluate in the absence of genetic analysis or other data, and difficult even then. In contrast, genes assigned to HCDs are nearly always (95%) located within them, and there is a logically greater certainty that a gene within an HCD is directly affected. Complexity arises in cases involving multiple active genes within an HCD, and so even the domain concept involves a degree of approximation, but based on gene expression and cell type-specificity Figs. , 3, thisTo a certain extent, however, HCDs may be advantaged compared to super-enhancers due to technical aspects of ChIP-seq analysis. Ranking by peak intensity  is limited because mapping algorithms routinely discard duplicate reads as a precaution against PCR artifacts arising from library construction. Reads therefore cannot stack on top of each other, and the maximum height of a given peak is artificially limited. Ranking by peak breadth avoids this limitation and therefore may present a more sensitive metric for determining the strongest and/or most cell type-specific enhancers.An important consideration is the potential mechanisms that underlie differences in peak intensity vs differences in peak breadth. In ChIP-seqs, which assay populations of cells, differences in peak intensity most likely reflect differences in binding affinity between enhancer regions; thus, higher peak intensity for a TF reflects a higher proportion of alleles crosslinked to that factor, and for histone modifications a higher proportion of alleles associated with the cognate enzyme(s). In either case, binding does not necessarily invoke transcriptional activation of a neighboring gene.Gypa and SLC4A1 gene loci in this study. This requirement suggests that the histone modification pattern associated with an HCD spreads in some fashion from the regulatory element(s). Regardless of the mechanism that underlies such spreading — whether a specific mechanism akin to heterochromatic domain formation, or simple spill-over from an excess of chromatin modifiers — a broad domain implies a high degree of binding and activity, and insofar as the vast majority of HCDs subsume gene promoters, of activity at transcription initiation sites. Rather than implying that a greater proportion of alleles in the cell population harbor modified nucleosomes, an HCD implies a greater quantity of modified histones at each allele, which could contribute to stability of the active transcriptional state in the nuclear environment.In contrast, the largest peak breadths most likely arise from a mechanism distinct from higher binding affinity. We previously demonstrated that within the murine β-globin locus association of specific histone modifications was not intrinsic to the entire sequence underlying the HCD, but was a function of a smaller enhancer region within it. We demonstrate similar effects for enhancers within the 31. Among the antibodies we have used, this study indicated that the H3K4Me1 Ab was highly specific, while the H3K4Me2 Ab exhibited significant cross-reactivity with H3K4Me1 and the H3K4Me3 Ab with H3K4Me2. All of these reagents were polyclonal antisera, however, and so differences in the behavior of different lots of these antibodies introduce an additional level of uncertainty. We are therefore cautious in interpreting the biological significance of the H3K4Me2 enrichments we show here, outside of the apparent role of specific enhancers in contributing to them. Notably, of the other ChIP-seq data sets we have analyzed, one used the same H3K4Me2 Ab as here (see Methods), while the other two used a distinct reagent. Based on a search of available databases, these two reagents account for more than 80% of H3K4Me2 ChIP-seqs. Our ability to consistently identify HCDs from ChIP-seqs using these antibodies suggests that differences in specificity alone do not account for the utility of the H3K4Me2 antibody, and the HCD approach produces similar results with other antibody combinations.A recent study examined the specificity of antibodies directed against different methylated states of H3K4, and determined that many of them exhibited cross-reactivityGypa, SLC4A1, and Tspan32 gene loci, which are all classified as super-enhancers, but only the Gypa and SLC4A1 loci as HCDs. At all loci, enhancer deletions result in substantial decreases in gene expression. At the Gypa and SLC4A1 loci, deletion of the enhancers results in loss of the broad peak of histone hyperacetylation, including modification levels associated with the region proximal to the TSS. At the Tspan32 locus, however, the deletion of neither the −10 kb element in undifferentiated MEL cells nor the +0.7 element in differentiating MEL cells results in any effect on chromatin structure at gene promoters or the regions surrounding them. Moreover, the introduction of the Gypa enhancer into the Tspan32 locus, at the site of the deleted −10 kb element, results in the formation of an HCD over a region of the locus that normally does not exhibit one. The data suggest a functional distinction between enhancers that work, at least in part, via modulation of long-range chromatin structure and enhancers that do not.Notably, ranking of H3K27Ac/H3K4Me2 peaks by breadth is potentially useful in identifying enhancers that function by different mechanisms. In this study, we contrast the effects of the deletion of enhancers from within the endogenous SLC4A1 locus are required for HCD formation, despite a separation of nearly 12 kb between them. This collaborative function raises the question of whether HCD formation by the Gypa enhancer inserted within the Tspan32 locus is fully enhancer-autonomous, or if instead it requires additional elements, both at this locus and at its native location. Additional genetic manipulations will be required to address this question.The relationship between enhancer function and HCD formation, however, does not appear to follow the simplest possible model. Both of the putative enhancers within the 8. Some studies have questioned the general validity of this assumption32, and our own analysis of the Tspan32 locus presents a clear example of how the super-enhancer concept tends to oversimplify more complex patterns of gene regulation. We found that the Tspan32 super-enhancer, comprised of three distinct candidate enhancer regions, is in fact involved in the regulation of two genes in addition to Tspan32. Moreover, individual enhancers within the super-enhancer exhibit different roles, with the −10 kb enhancer required solely in undifferentiated MEL cells, the +0.7 kb enhancer required solely in differentiating MEL cells, and the −17 kb enhancer showing no requirement at all. Based on such behavior, the classification of these three elements as a single super-enhancer appears to obscure the functions of these regions more than it illuminates them.An additional concern is the stitching step of the super-enhancer analysis, in which putative enhancer regions located within a specific distance of each other  are considered as a single element, under the assumption that closely spaced enhancers work together in activation of the same gene or genesWhile super-enhancers can identify highly expressed genes that characterize cell identity, we find that HCDs define a set of genes that exhibit higher expression, and better specify cell lineage. Furthermore, as an analytical tool, HCDs present additional advantages over super-enhancers, including a significantly simpler workflow. Our results suggest that super-enhancers may not have a function distinct from that of typical enhancers, and that clustering of enhancer elements does not necessarily imply cooperative function, whereas there appears to be a functional difference between at least some of the enhancers located within HCDs, involving modulation of long-range chromatin structure, compared to other enhancers.33, but also including Cd117 staining using a FACS Aria-II. In brief, larger single cells that were Terlo CD117+CD44hi were collected. These cells are also positive for CD71.Mice (C57BL/6J) were mated overnight and the vaginal plug verified the next morning, indicating embryonic day 0.5 (e0.5). At e14.5 pregnant mice were killed by cervical dislocation for collection of fetal liver. The Institutional Animal Care and Use Committee affiliated UCAR at the University of Rochester Medical Center has reviewed and approved all protocols involved in this project for the use of mice. (UCAR 2006-119). For ATAC-seq (see below), E14.5 liver-derived proerythroblasts were sorted as previously described34. In brief, 2 × 107 cells were washed with PBS once, then cross-linked with 1% formaldehyde for 10 min at room temperature. Cross-linking was quenched with 5 M glycine for 1 min at room temperature, and cells were washed with PBS once. Cells were incubated in swelling buffer for 20 min on ice, followed by dounce homogenization to isolate cross-linked nuclei. Nuclei were placed in lysis buffer for 30 min and then sonicated into ~200 bp fragments using a Diagenode Bioruptor. Samples were diluted and immunoprecipitated with 200 μg antibody to either H3K4Me1 (Abcam #ab8895), H3K4Me3 (Active Motif #39916), H3K27Ac (Active Motif #39134), H3K4Me2 (Abcam #7766), or nonspecific rabbit immunoglobulin G (Millipore #12-370) and incubated on a rotator for 18 h. at 4 °C. DNA-protein complexes were recovered with protein G magnetic beads (Invitrogen). Library preparation was performed as previously described35. Library quality was evaluated on a Bioanalyzer and sequencing was performed on a HiSeq 2500 Rapid Run to obtain 1 × 50 bp reads.Chromatin immunoprecipitation was performed as previously described36) using the following parameters SLIDINGWINDOW:4:20 TRAILING:13 LEADING:13 ILLUMINACLIP:adapters.fasta:2:30:10 MINLEN:15. All quality reads were aligned to the mm9 reference genome using Bowtie-1.0.1 (ref. 37), suppressing multi-mapping reads using the ‘-m 1’ parameters. All alignments were written in the SAM format (-S), converted to BAM and sorted for all subsequent analyses using samtools38. Peaks were called for each replicate of each mark using MACS2 along with additional settings including (–broad –broad-cutoff 0.1 -B) using the total input control as the mock data file16. The intersection of each replicate was identified using bedtools intersect and was used for subsequent analyses39.Illumina reads were converted to the fastq format using bcl2fastq-1.8.4 with default parameters. Quality control and adapter removal was performed using Trimmomatic-0.32  and incubated at 37 °C for 30 min. Reactions were purified using AmpureXP beads (Beckman Coulter) following the manufacturer’s protocol with minor changes. Beads were used at a 1:1.1 ratio and reactions were washed twice. After purification samples were amplified using 1 × NEBnext PCR master mix and 1.25 μM of custom Nextera PCR primers 1 and 2 (ref. 41). Libraries were amplified again for an additional 17–19 cycles and left side size selected with SPRIselect beads (Beckman Coulter) at a 1:1 ratio following manufacturer’s protocol, then right side size selected with SPRIselect beads (Beckman Coulter) at a 1:0.5 ratio following manufacturer’s protocol. Library quality was evaluated on a Bioanalyzer and sequencing was performed on a Hiseq2500v2 platform in rapid mode to generate 50 million reads per sample.ATAC-seq was performed as previously described36) using the following parameters SLIDINGWINDOW:4:20 TRAILING:13 LEADING:13 ILLUMINACLIP:adapters.fasta:2:30:10 MINLEN:15. All quality reads were aligned to the mm9 reference genome using Bowtie-1.0.142 (ref. 37), suppressing multi-mapping reads using the ‘-m 1’ parameters. All alignments were written in the SAM format (-S), converted to BAM and sorted for all subsequent analyses using samtools38. Reads aligning to organism-specific blacklist regions and the mitochondrial genome are discarded. Accessible regions are identified using MACS2 (ref. 15) and ATAC specific parameters (–nomodel –shift -100 –extsize 200).Illumina reads were converted to the fastq format using bcl2fastq-1.8.4 with default parameters. Quality control and adapter removal was performed using Trimmomatic-0.32 (ref. 23. All reads were processed using trimmomatic36 (v0.36) to remove low quality bases and any residual adapter sequence (TRAILING:13 LEADING:13 ILLUMINACLIPtrimmomatic_adapters.fasta:2:30:10 SLIDINGWINDOW:4:20 MINLEN:15). Quality reads were aligned to either mm9/hg19 (depending on organism of origin) using STAR42 (v2.5.2b) . Read alignments were quantified using featureCounts (v1.5.0-p3) (-s 2 -T -t exon -g gene_name -a genes.gtf –o) and normalization was performed within the R (3.4.1) framework using DeSeq2 (v1.16.1)43.Raw fastq files for publicly available data sets  were downloaded using fastqDump available in SRAtoolkit37. The code to call HCDs is included in Supplementary Software 17. Enrichr was used to perform comprehensive enrichment analysis on associated genes19.The top 2% broadest peaks were filtered from the H3K4Me2 and H3K27Ac intersected replicates. Bedtools intersect was used to identify the intersection of these two tracks, which is what we define as a hyperacetylated domainhttp://younglab.wi.mit.edu/super_enhancer_code.html), created by the Young lab6. Input enhancers were defined as a merged peak set of all Gata1 and Tal1 replicates44 and a TSS_EXCLUSION_ZONE_SIZE of 500 bp. Super-enhancers were associated with nearby genes using bedtools closest, default settings. Enrichr was used to perform comprehensive enrichment analysis on associated genes19.For the identification of super-enhancers within our H3K4Me2 and H3K27Ac data sets we used ROSE  for fetal H3K4Me2, H3K27Ac, Gata1, Tal1 and the total input control samples. MACS2 was used to identify histone marks (-B –broad –broad-cutoff 0.1) and TF peaks (-B –q 0.01) relative to the total input control15. Domains were defined as the intersect of the largest 2% H3K4Me2  and H3K27ac , for a total of 302 regions. Two super-enhancer populations were called using ROSE and identified based on a Gata1/Tal1 union enhancer population which was ranked on H3K4Me2 or H3K27Ac6. The resulting sets of super-enhancers were intersected to identify a final population of super-enhancers (326). Domains and super-enhancers were associated with hg18 RefSeq Genes (UCSC) based on a nearest neighbor analysis using bedtools closest (default settings)33. Enriched cell types were evaluated using Enrichr and the Human Gene Atlas. RNA expression log2(RMA) were downloaded from ArrayExpression (G_GEOD-36984)20.Domains and super-enhancers were identified and evaluated during human fetal erythropoiesis using publicly available data (GSE36985)45. In brief, sgRNAs were designed following guidelines to minimize off-target effects (http://crispr.mit.edu/) -2A-GFP(px458) (Addgene ID 48138) using 100 ng px458 cut with BbsI (New England Biolabs), 0.2 μM annealed oligos, 1× quick ligase reaction buffer (New England Biolabs), and 2000U quick ligase and incubated at room temperature for 10 min. For deletions, MEL745a cells were transfected with one plasmid targeting upstream and one plasmid targeting downstream sequence surrounding the enhancer using Lipofectamine 3000 (Invitrogen) according to manufacturer instructions. For insertions, MEL745a cells were transfected with one plasmid targeting the sequence surrounding the previously deleted enhancer and a double-stranded repair template using Lipofectamine 3000 (Invitrogen) according to manufacturer instructions. At 48 h. the cells were resuspended in D-PBS (Gibco) +0.5% FBS(Gemini), stained with DAPI (ThermoFisher), and the viable, GFP positive population was sorted in bulk  was added to the culture medium.MEL745a cells were obtained from the laboratory of Mark Groudine. Cell lines were maintained at 37 °C in a CO7 cells for each experiment and then sonicated to ~500 bp fragments of genomic DNA using a Diagenode Bioruptor. Samples were diluted and immunoprecipitated with 5 μg of antibodies specific for H3K27Ac (Active Motif # 39134), H3K4Me2 (Millipore # 07-030), or nonspecific rabbit immunogloblulin G (Milipore # 12-370). The analysis was performed using qPCR and detected using the CFX Connect Real-Time PCR System and CFX Manager (Bio-Rad). Primers were designed to amplify regions within the Glycophorin A locus or Tspan32 locus, and as a negative control the Amylase gene promoter (a region that is inactive in erythroid cells)  according to manufacturer instructions. One microgram of RNA was used to synthesize cDNA using the iScript cDNA synthesis kit (Bio-Rad). cDNA was amplified using iTaq Universal SYBR Green Supermix (Bio-Rad) and detected using the CFX Connect Real-Time PCR System and CFX Manager (Bio-Rad). Primers were designed to amplify Gypa promoter deletion as well as the enhancer replacement experiment where only two biological replicates of the homozygous insertion of the new enhancer were found (standard error of the mean (S.E.M.) is shown). Individual data points represent the mean of three technical replicates. Biological replicates are defined as experiments performed on an independent single-cell-derived homozygous knockout clone. Technical replicates are defined as experiments performed on the same sample and analyzed multiple times. Statistical significance for gene expression was calculated using a two-tailed Student’s t-test.Gene expression and ChIP-qPCR experiments were performed on three biological replicates with the exception of the Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Software 1


Bmal1 knockout mice. In WT, we observed 24h oscillations in promoter-enhancer loops at multiple loci including the core-clock genes Period1, Period2 and Bmal1. In addition, we detected rhythmic PolII activity, chromatin modifications and transcription involving stable chromatin loops at clock-output gene promoters representing key liver function such as glucose metabolism and detoxification. Intriguingly, these contacts persisted in clock-impaired mice in which both PolII activity and chromatin marks no longer oscillated. Finally, we observed chromatin interaction hubs connecting neighbouring genes showing coherent transcription regulation across genotypes. Thus, both clock-controlled and clock-independent chromatin topology underlie rhythmic regulation of liver physiology.The circadian clock drives extensive temporal gene expression programs controlling daily changes in behavior and physiology. In mouse liver, transcription factors dynamics, chromatin modifications, and RNA Polymerase II (PolII) activity oscillate throughout the 24-hour (24h) day, regulating the rhythmic synthesis of thousands of transcripts. Also, 24h rhythms in gene promoter-enhancer chromatin looping accompany rhythmic mRNA synthesis. However, how chromatin organization impinges on temporal transcription and liver physiology remains unclear. Here, we applied time-resolved chromosome conformation capture (4C-seq) in livers of WT and arrhythmic  In the eukaryotic cell nucleus, DNA loops bring together gene promoters and distal regulatory elements to tune gene expression. To adapt to the 24h periodic changes in the environment, such as light-dark cycles, many organisms on the planet adopted an internal circadian clock encoded genetically. Thus, animal physiology resonates with external cycles. In mouse liver, thousands of genes, including ones belonging to the central clock, lipid and glucose metabolism, as well as detoxification are expressed with a 24h period. Here, we monitored DNA contacts across the circadian cycle involving the promoters of genes playing key roles in the daily physiology of the mouse liver. While the dynamics of some loops oscillates in sync with gene expression along the 24h day, other loops connected daily active regulatory elements to target gene promoters stably over time. Using arrhythmic mutant animals, we demonstrated that the maintenance of such stable loops goes beyond the circadian clock function. Also, we discovered a set of frequently interacting genes having coherent temporal expression regulation in wild type and clock impaired animals. Thus, both dynamic and static DNA contacts underlie the circadian transcription of mouse liver physiology. Period  and Cryptochrome (Cryptochrome1 and Cryprochrome2) genes [Human behaviour and physiology have adapted to daily recurring inputs from the environment. Most animals including mammals have integrated a time monitoring device, known as the circadian clock, allowing them to resonate with these 24h external cues. The coupling of our internal clock with environmental light-dark cycles controls our wake-sleep rhythm but also, as illustrated here, the 3-dimensional (3D) shape of chromosomes in cells of the intact liver in mice. In mammals, the suprachiasmatic nucleus (SCN) receives light input from the retina and synchronizes peripheral organs through direct and indirect signalling . The cir2) genes . For exa2) genes –8. Furth2) genes –12.cis within ~0.1 to few megabases (Mb) large topologically associating domains (TADs) [trans chromosomes [Bmal1 KO animals, showing that the circadian clock sustained daily changes in genomic interactions [Cryptochrome1 (Cry1) intronic enhancer element abolished the dynamics of the loop and perturbed the Cry1 transcription cycle , and eventually led to a short period phenotype of mutant animals [In this context, changes in chromatin topology along the 24h day emerge as a regulatory layer for temporal gene expression ,13. In ts (TADs) –18. In comosomes  or with omosomes . Howeveromosomes . At a smomosomes . In factomosomes . In addiomosomes –24. Remaractions . Further animals . These dBmal1 KO animals using 4C-seq. We identified 24h rhythms in promoter-enhancer looping synchronized with the expression of the core-clock genes Bmal1, Period1 and Period2. Furthermore, we showed that promoters of clock output genes, representing key physiological properties of hepatocytes such as metabolite synthesis, detoxification and glucose metabolism, recruited surrounding elements resembling enhancers. Although PolII activity and chromatin marks oscillated at interacting DNA sites in WT livers, promoter-enhancer contact frequency was maintained at similar levels during day-time and night-time, corresponding to active and inactive transcription, respectively. This suggested that rhythmic transcription took place over a static and closed conformation of chromatin loops. Remarkably, in Bmal1 KO animals, PolII activity and chromatin modifications no longer oscillated at these sites, while their interaction frequency remained stable over time and at a comparable level to WT, showing a clock-independent mechanism of DNA looping. Finally, we found a cluster of stable interactions linking a set of genes that were co-regulated across time and genotypes. Overall, these findings further our understanding on the role of chromatin architecture in circadian gene regulation in animals.Here, we investigated temporal changes in chromatin conformation in livers of WT and Bmal1 [Bmal1 gene is rhythmically transcribed in WT mouse liver with pre-mRNA abundance peaking around ZT22  to ~75 kb downstream of the transcription start site (TSS), with the contact frequency peaking around ZT18-20 at multiple 4C-seq peaks . Another ZT20 4C-seq peak located near exon5 ~73 kb downstream of the bait coincided with a ZT20 DNase1 signal   . These dPeriod1 (Per1) and Period2 (Per2) genes that belong to the negative limb of the circadian molecular oscillator [Per2 and Per1 4C-seq signals were largely enriched within a 2Mb window on cis chromosome  peaking around ZT16, as well as the Hes6 gene in which PolII, H3K27ac and DHS peaked at ZT16  or other genes (the Por interacting hub) in cis. Although the contacting regions displayed rhythmic chromatin modifications (in the case of regulatory elements) and rhythmic transcription (in the case of gene-gene contacts), their relative contact frequencies in the liver did not change across time, at least not between the two probed maximum and minimum transcription time-points  and were comparatively very low at ZT18-ZT22 .Bmal1 KO animals were maintained at the EPFL animal house facility in 12hour/12hour light-dark cycle with 4 animals per cage.C57/BL6J (WT) and Bmal1 KO. 3 biological replicates were used in the 4h time-resolved 4C-seq experiments. Sample preparation and analyses were performed as in [5 g for 45 minutes at 4°C. Nuclei were washed twice in 1× PBS and resuspended in 1 mL of 10 mM Tris-HCL (pH 8.0), 10 mM NaCl, 0.2% NP-40, and protease inhibitor cocktail ; kept for 15 minutes on ice; and washed twice with DpnII buffer (New England Biolabs). Thirty million nuclei were incubated for 10 minutes at 65°C in DpnII buffer and triton X-100 was added to 1% final concentration. Chromatin was digested overnight with 400 U of DpnII (New England Biolabs) at 37°C with shaking. Digested chromatin was then diluted in 8-mL of ligation buffer containing 3000 U of T4 DNA ligase for 4 h at 16°C plus 1 h at room temperature. 50 μL of 10 mg/mL proteinase K was added and samples were incubated overnight at 65°C. DNA was precipitated and resuspended in TE buffer (pH 8.0) containing RNase A, and incubated for 30 min at 37°C. Libraries were digested with NlaIII using 1U/μg of DNA template (New England Biolabs) overnight at 37°C. Digested products were ligated with 2000 U of T4 DNA ligase (New England Biolabs) for 4 h at 16°C in a 14-mL final volume. Circularized products were precipitated and resuspended in TE buffer (pH 8.0). Inverse PCRs were performed on 600 ng of circular DNA template per sample as described in (23). Inverse PCR primers are mentioned in the 4C-seq was performed in livers of 8 to 12 weeks old male with 4 biological replicates when comparing ZT08 versus ZT20 in WT animals and 3 biological replicates in ed as in . In briecis-chromosome (excluding the five fragments upstream and downstream of the bait). To stabilize variance, the fragment counts c in each sample were log-transformed:Y) were modeled with fragment effects ai and condition effects bj . We estimated these effects by minimizing the weighted sum S of squared residuals across replicates r:4C-seq data were analyzed as in . BrieflyWi,j are defined as Wi,j = wg,i×ws,j, where wg,i is the Gaussian smoothing kernel (sigma=2500bp) at position i, and ws,j a condition weight based on the number of samples with non-zero counts on fragment i. To compare between two conditions, we calculated p-values and condition effects at each genomic fragment using t-statistics. To detect rhythmic signal, we calculated the 24-hour Fourier coefficients of the condition effect  from the six equally-spaced time points and used the chi-square test to test deviations from the null model that the real and imaginary parts have both a mean of zero.GSE73554) and rhythms were analyzed as in [Processed RNA-seq data were downloaded from  GSE73555GSE73554GSE60430) and analyzed as in [Data were downloaded from   of the harmonic regression fit.To obtain smooth color transitions, the value GS26602). REVERB-alpha and ROR-gamma ChIP-seq data were downloaded from [GSE67973). CTCF ChIP-seq data were downloaded from [BMAL1 ChIP-seq data were downloaded from   and log2 fold change (middle panel) and −log10(p)  for rhythmicity analyses [Per2 TSS bait in WT mouse liver (top panel) and log2 fold change (middle panel) and −log10(p)  for rhythmicity analyses [Per1 TSS bait in WT mouse liver (top panel) and log2 fold change (middle panel) and −log10(p)  for rhythmicity analyses [(A) 2Mb genomic window view of 4C-seq signal over time from the analyses . Fragmenanalyses . Fragmenanalyses . Fragmen(TIF)Click here for additional data file.S2 FigPeriod1 pre-mRNA expression over time in WT mouse liver [Period1 TSS bait in WT mouse liver (top panel) and log2 fold-change (middle panel) and −log10(p)  from rhythmicity analyses [Period1 bait, two localized genomic regions at ~6 kb upstream and ~17 kb downstream of the bait position were recruited preferentially at ZT12 to the Period1 promoter. Fragments with p< 0.01 are colored according to peak time in contact frequency (color-coding as in following top left circle panel D). * = local maximum in differential genomic contact. n=2. Dashed rectangle: region of rhythmic interaction. (C) 4C-seq signal over time adjacent to * (B). (D) Period1 4C-seq signal at ZT04 (purple) and ZT12 (yellow) and time-resolved ChIP-seq signals for PolII, H3K27ac and DNase1 hypersensitivity in WT mouse liver [(A) se liver . (B) 4C-analyses  (n=2). Ase liver . Colored(TIFF)Click here for additional data file.S3 FigPfkfb3 pre-mRNA expression over time in WT mouse liver [Pfkfb3 bait position (upper panel) and the corresponding Z-scores (middle track) and p-values (lower track) revealing multiple prominent 4C peaks. Dashed rectangle: region of highest interaction frequency. (C and D) Genome browser viewing with Pfkfb3 4C-seq signal at ZT08 (red) and ZT20 (blue) and time-resolved ChIP-seq signal for PolII, H3K27ac and DNase1 hypersensitivity in WT mouse liver (D) [Pfkfb3 TSS are marked by DHSs and rhythmic H3K27ac signals peaking around ZT10, in sync with Pfkfb3 transcription. See (A) se liver . (B) 4C-(TIFF)Click here for additional data file.S4 FigMfsd2a pre-mRNA expression over time in WT mouse liver [Mfsd2a bait position (upper panel) and the corresponding Z-scores (middle track) and p-values (lower track) revealing a localized prominent 4C-seq peak  ~15 kb upstream of the bait position (arrow). (C and D) Genome browser viewing with Mfsd2a 4C-seq signal at ZT08 (red) and ZT20 (blue) and time-resolved ChIP-seq signal for PolII, H3K27ac and DNase1 hypersensitivity in WT mouse liver (D) [Mfsd2a transcription. Colored tracks represent peak time in chromatin marks following the color code as in (C) . See (A) se liver . (B) 4C-(TIFF)Click here for additional data file.S5 FigNampt pre-mRNA accumulation over time in WT (solid line) and Bmal1 KO (dashed line) livers [Nampt 4C-seq signal at ZT08  and ZT20  in a genomic window of 2Mb surrounding the bait in the liver of Bmal1 KO animals and the corresponding Z-scores and p-values. (C and D) Genome browser view of the Nampt 4C-seq signal and the corresponding Z-scores in livers of WT and Bmal1 KO animals at ZT08 (red line) and ZT20 (blue line) (D). Mean and peak time of PolII and H3K27ac ChIP-seq signals are shown for both WT and Bmal1 KO conditions (D) [Bmal1_KO animals. H3K27ac rhythms are observed at connected regions in WT livers. In the arrhythmic Bmal1 KO livers, the chromatin marks no longer oscillate while chromatin contacts are maintained at levels comparable to WT conditions.(A) ) livers . (B) Nam(TIFF)Click here for additional data file.S6 FigNampt 4C-seq signal at ZT08 (red) and ZT20 (green) in a genomic window of 2Mb surrounding the bait in the liver of WT  and Bmal1 KO animals  and the corresponding Z-scores and p-values. Nampt 4C-seq signals are very similar across all conditions. (B) 4C-seq signal from Por TSS bait at ZT08 (red) and ZT20 (green) in a genomic window of 1Mb surrounding the bait in livers of WT  and Bmal1_KO animals  and the corresponding Z-scores and p-values. Por 4C-seq signals are very similar across all conditions.(A) (TIFF)Click here for additional data file.S7 FigBmal1 locus as in Bmal1 promoter preferentially between ZT18 to ZT00 are marked by synchronous rhythms in H3K27ac and DNase1 hypersensitivity as well as binding of REVERB-alpha, ROR-gamma and CTCF. (B) 4C-seq signal and ChIP-seq signal for PolII, H3K27ac and DNase1 hypersensitivity in WT mouse liver at Per2 locus as in Per2 promoter preferentially at ZT16 are marked by synchronous rhythms in H3K27ac and DNase1 hypersensitivity as well as binding of REVERB-alpha, ROR-gamma and CTCF, as well as low binding of BMAL1.(A) 4C-seq signal and ChIP-seq signal for PolII, H3K27ac and DNase1 hypersensitivity in WT mouse liver at (TIFF)Click here for additional data file.S8 FigPer1 locus as in Per1 promoter are marked by peaks of H3K27ac and DNase1 hypersensitivity as well as binding of BMAL1, REVERB-alpha, ROR-gamma and CTCF. (B) 4C-seq signal and ChIP-seq signal for PolII, H3K27ac and DNase1 hypersensitivity in WT mouse liver at Mreg locus as in Mreg promoter preferentially at ZT20 are marked by synchronous rhythms in H3K27ac, DHS as well as binding of REVERB-alpha, ROR-gamma and CTCF.(A) 4C-seq signal and ChIP-seq signal for PolII, H3K27ac and DNase1 hypersensitivity in WT mouse liver at (TIFF)Click here for additional data file.S9 FigNampt locus as in Nampt promoter are marked by rhythms in PolII, H3K27ac and DNase1 hypersensitivity as well as binding of REVERB-alpha and ROR-gamma. (B) 4C-seq signal and ChIP-seq signal for PolII, H3K27ac and DNase1 hypersensitivity in WT mouse liver at Por locus as in Por promoter are marked rhythms in PolII, H3K27ac and DNase1 hypersensitivity as well as binding of REVERB-alpha, ROR-gamma and CTCF.(B) 4C-seq signal and ChIP-seq signal for PolII, H3K27ac and DNase1 hypersensitivity in WT mouse liver at (TIFF)Click here for additional data file.S10 FigPfkfb3 locus as in Pfkfb3 promoter are marked by rhythms in H3K27ac and DNase1 hypersensitivity as well as binding of BMAL1, REVERB-alpha, ROR-gamma and CTCF. (B) 4C-seq signal and ChIP-seq signal for PolII, H3K27ac and DNase1 hypersensitivity in WT mouse liver at Mfsd2a locus as in Mfsd2a promoter are marked rhythms in PolII, H3K27ac and DNase1 hypersensitivity as well as binding of REVERB-alpha and ROR-gamma, and low binding of CTCF.(A) 4C-seq signal and ChIP-seq signal for PolII, H3K27ac and DNase1 hypersensitivity in WT mouse liver at (TIFF)Click here for additional data file.S11 FigData from .(TIFF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


In-depth analysis of global RNA sequencing has enabled a comprehensive overview of cellular transcriptomes and revealed the pervasive transcription of divergent RNAs from promoter regions across eukaryotic genomes. These studies disclosed that genomes encode a vast repertoire of RNAs beyond the well-known protein-coding messenger RNAs. Furthermore, they have provided novel insights into the regulation of eukaryotic epigenomes, and transcriptomes, including the identification of novel classes of noncoding transcripts, such as the promoter-associated noncoding RNAs (pancRNAs).trans-acting ncRNAs that regulate expression of target genes located in different chromosomal domains and displaying their function both in the nucleus and in the cytoplasm, the pancRNAs operate as cis-acting elements in the transcriptional regulation of neighboring genes. PancRNAs are very recently emerging as key players in the epigenetic regulation of gene expression programs in development and diseases.PancRNAs are defined as transcripts transcribed within few hundred bases from the transcription start sites (TSSs) of protein-coding or non-coding genes. Unlike the long Herein, we review the complex epigenetic network driven by pancRNAs in eukaryotic cells, their impact on physiological and pathological states, which render them promising targets for novel therapeutic strategies. Recent advances based on RNA sequencing efforts documented several classes of long and short noncoding RNAs transcribed in the promoter regions proximal the transcription start sites (TSS) of most annotated genes, bona fide involved in the regulation of transcription initiation . Among tng genes . Notablyng genes .cis-acting ncRNAs transcribed from promoter regions of protein-coding genes and their impact on chromatin signature and gene expression programs, in both physiological and pathological conditions.Here we are reviewing current knowledge about hRrp40, a core component of the human 3′ to 5′ exoribonucleolytic exosome and one of the major RNA degradation complexes, was depleted [hRrp40 depleted- or control cells demonstrated that both sense and antisense transcripts were detectable upstream the TSS, and the transcribed region was characterized by markers of active transcription, such as RNA polymerase II (RNAPII) and acetylated histone 3 (H3K9ac) peaks [The first evidence documenting the presence of noncoding transcripts arising from regions located upstream the TSS came from high throughput experiments where depleted . In thesc) peaks . Genome-c) peaks , 3, 12. c) peaks . Bidirecc) peaks . Howeverc) peaks  and direc) peaks , whereasc) peaks . A fascic) peaks , 17.Sphk1 gene and its Khps1 pancRNA variants [trans-acting factors, such as TET  proteins, to achieve a sequence-specific DNA demethylation  methylation, as described for the variants . A similvariants , potentiAn alternative, but not mutually exclusive possibility, is that pancRNAs display a more general function, facilitating rapid activation or repression of the downstream gene by altering chromatin structure or by recruiting transcription regulators. Short RNAs originating within 700 base pairs (bp) upstream the TSS of genes targeted by Polycomb were identified in primary T cells and embryonic stem cells . These RS. pombe [Another functional model derives from initial work performed in S. pombe  and confS. pombe , where tIn a further model, the RNAPII reading through the promoter allows transcription of low copy pancRNAs targeted by antisense RNAs. The pancRNAs and antisense RNA form a complex that associate with the local chromatin architecture through a chromatin remodeling complex presumably containing histone methyltransferase DNMT3A. Remarkably, the antisense RNAs-pancRNAs form RNA:RNA hybrids that create docking sites for the recruitment of gene silencing complexes , 25. In In addition to RNAPII, pancRNAs transcribed by RNAPI in the nucleolus have been shown to take part to regulatory networks controlling the epigenetic state of chromatin of ribosomal genes. These pancRNAs form a DNA:RNA triplex recognized by the DNA methyltransferase DNMT3B to achieve de novo CpG methylation of rRNA genes (rDNA) . The forCCND1) [CCND1 gene. The authors showed that the pancCCND1_D was able to recruit the RNA binding protein FUS and CBP/ p300, thus inhibiting the histone acetyl transferase (HAT) activity, resulting in CCND1 repression  , 29. WanAlthough initial models focused on pancRNAs involved in the epigenetic silencing of nearby genes, later on genome-wide efforts demonstrated a positive correlation between the expression of pancRNAs and the neighboring protein-coding genes , 31. TheIn the past few years, pancRNAs have been shown to cover crucial roles in the guidance of cell differentiation , 32, 33.cis the transcription of the nearby coding genes, by contributing to their epigenetic setting [Sphk1) gene, in primary cells from rat fetuses [Sphk1 CpG island contains a regulatory element, indicated as tissue-dependent differentially methylated region (T-DMR), involved in the regulation of Sphk1 expression. Transcription of the antisense pancRNA Khps1 from the promoter induces DNA demethylation at CG sites, specifically in the T-DMR of Sphk1, thus inducing de-repression of the gene [pancIl17d turned out to be responsible for triggering the expression of the interleukin 17d, indispensable for successful formation of the blastocyst. Although the expression of this pancRNA was associated with an increased DNA demethylation at the promoter, the molecular mechanism underlying this regulation was not further characterized. The epigenetic modifications associated to the zygotic gene activation was assumed to be due to the enrollment of PARP and TET3 demethylation factors on chromatin by pancRNAs or to be possibly only an indirect upshot [It is well known that promoters with a high CpG content can undergo bidirectional transcription and that divergent lncRNAs are capable to positively regulate in  setting . As ment fetuses . Sphingothe gene . A furththe gene . This futhe gene , 36. Init upshot , 37.In addition, it has been proposed that pancRNAs might bind epigenetic factors, promoting the fold of the chromatin into a transcriptionally competent ternary structure, named R-Loop and composed by the nascent RNA annealed with its complementary ssDNA and the non-template ssDNA. Another possibility was that pancRNAs may perhaps stabilize the active state of chromatin through the association with small dsRNAs, which in turn, could cover the catalytic site of methyl transferases .Evx1as lncRNA for the transcriptional activation of the neighboring protein-coding gene EVX1, essential for mesoderm differentiation [locus and enables the recruitment of the transcription co-activator Mediator on the promoter [A subsequent investigation carried out in murine ESC, underlined the requirement of ntiation . In thispromoter . Mediatopromoter .pancNusap1 positively regulates the expression of the spindle-associate gene Nusap1, and this effect is accompanied by an increased acetylation of H3 lysine 9 and 27 at the promoter [pancNusap1 and Nusap1, resulting in the inhibition of the M-phase and the irreversible cell cycle withdrawal [Uph1 pancRNA regulates in cis the transcription of its paired coding gene Hand2 by enhancing the histone modifications H3K4me1 and H3K27ac in the enhancer [Uhp is critical for heart development in mice embryos, as demonstrated by the severe cardiac defects of Uph−/− mice, leading to embryonic lethality at stage E10.5 [Myoparr binds the myogenin shared promoter and physically interacts with the transcriptional co-activator DDX17 and the histone acetylase PCAF. This association leads to increased H3K27 acetylation of the promoter and the subsequent recruitment of the transcription factor MYOD1 [Myogenin locus, contributing to the exit of myoblasts from cell cycle and promoting differentiation [Coupled expression of pancRNAs and the relative mRNAs in differentiating cells is often supported by the enrichment of histone acetylation and methylation in the promoter . The terpromoter . The stithdrawal . Similarenhancer . The funge E10.5 . A possior MYOD1 . This alntiation .VX1 promoter is associated to the H3K27ac and H3K4me1 histone modifications [hTAPAS represses the gene encoding human telomerase reverse transcriptase (hTERT). In the germline and human embryonic stem cell line hTAPAS is fundamental for keeping low the concentration of the TERT protein, guarantying telomere length homeostasis and proliferative potential in the cells [locus, but can modulate in trans the transcription of other genes, by functionally interacting with the protein encoded from their cognate sense transcript, as for the pancRNA Six3OS, that modulates the activity of the transcriptional factor SIX3, by linking chromatin-modifying enzyme complexes on the SIX3 target genes [Daphnia magna it has been recently characterized the sense capped but non-polyadenylated pancRNA DAPALR, that can activate in trans the transcription of the downstream locus dsx1, relevant for sex determination during embryogenesis [DAPALR is still unclear, but this discovery denotes that the developmental role of pancRNAs is conserved in all eukaryotes although displaying diversification for directionality of transcription, post-transcriptional modifications and mechanism of action. Interestingly, antisense lncRNAs and sense transcripts tend to have a concerted expression in a tissue-specific fashion in fully developed organs, including cerebral cortex, cerebellum, heart, kidney and liver [Remarkably, recruitment of the Mediator complex on the Eications , where the cells . It is iet genes . This meet genes . Interesogenesis . The molnd liver , 34. Thind liver , 34. To nd liver . In partnd liver .All these evidences support the idea that pancRNAs display evolutionary conserved functions emblematic both for differentiation and homeostasis in all eukaryotes.Alterations of pancRNAs have been extensively linked to the changes of gene expression programs occurring in human cancer. PancRNAs affect epigenetic modification of promoters through scaffolding function. It has been shown that non-protein coding transcripts display key roles in cancer biology by impacting the gene expression of important regulatory proteins . In partCCND1 pancRNAs are transcribed and negatively regulate cyclin D1 expression [CCND1 gene associate with and recruit the RNA binding protein FUS/TLS . The localization of the pancRNA_CCND1-FUS complex to the CCND1 promoter results in the inhibition of the CBP/p300 histone acetyltransferases activity thereby preventing CCND1 transcription [CCND1. Perturbation of the EWS-FLI1/DHX9 interaction by the small molecule YK-4-279 causes the formation of an alternative inhibitory complex composed by the RNA binding protein Sam68, DHX9, and the pancRNA_CCND1_B determining a repressive chromatin environment responsible of cyclin D1 transcriptional inhibition. The ability of pancRNAs_CCDN1 to affect cell proliferation through transcriptional regulation of CCND1 reveals the contribution of these molecules in the control of cell cycle progression and in the tumorigenic process [It is well established the oncogene-like role of pancRNAs in tumorigenesis through the control of critical cell cycle regulators like cyclins , 29. In pression . In HeLacription . More repancRNA_Ets-1, displaying a role in the promotion of in vivo/in vitro growth and aggressiveness of gastric cancer [PancRNA_Ets-1 facilitates the physical interaction between the RNA binding protein NONO and the transcriptional factor ERG, resulting in transactivation of ERG and increased cancer-related gene transcription [pancRNA_Ets-1 displays a crucial role in neuroblastoma tumorigenesis, by promoting neuroblastoma cell growth, invasion and metastasis. PancRNA_Ets-1 binds to the heterogeneous nuclear ribonucleoprotein K (hnRNPK) to facilitate its physical interaction with β-catenin, resulting in reduced β-catenin proteasome-dependent degradation [pancRNA_Ets-1 upregulation in clinical tissues and cell lines of both gastric cancer and neuroblastoma could be exploited as promising markers and potential therapeutic targets.A similar mechanism has been documented for the c cancer . PancRNAcription . Moreoveradation . As a copancRNA_FOXCUT is transcribed upstream of the Forkhead box C1 (FOXC1) gene and its expression is positively correlated with FOXC1 mRNA [ESCC) [FOXCUT expression was found enhanced in Basal-Like Breast Cancers (BLBCs) but not in other non-basal like breast cancer subtypes, suggesting that FOXCUT may function as specific biomarker in BLBCs [FOXCUT reduced the levels of FOXC1 mRNA, concurring with the observed inhibition of migration, invasion and the metastatic potential of cells.The XC1 mRNA . The traXC1 mRNA , 49, 50.A [ESCC)  and nasoA [ESCC) . FOXCUT in BLBCs . NotablypancRNA_HIF2PUT (HIF2PUT) was found correlated with the expression of the hypoxia-inducible factor-2α (HIF-2α), in osteosarcoma patients [HIF2PUT knockdown is able to induce enhanced proliferation, migration and self-renewal of MG63 cells, suggesting that HIF2PUT may suppress the properties of cancer stem cells in osteosarcoma by regulating the expression of HIF-2α [HIF2PUT positively correlates with HIF-2α expression and HIF2PUT knockdown impairs the stem cell like properties of colorectal cancer stem cells by regulating HIF-2α expression [Similarly, the expression of the patients , 55. HIFpatients , 56, 57.f HIF-2α . In colopression .CDH1), known as an epithelial cell differentiation marker and a tumor suppressor, is epigenetically silenced contributing to the acquisition of key essential properties in the tumor development and progression. They found that a specific miRNA (isomiR-4534) guides the Argonaute1 (Ago1) binding to a precise site on the pancRNA_CDH1. In turn, Ago1 recruits the histone methyltransferase SUV39H1 to repress the promoter activity with the consequent transcriptional suppression. Importantly, a cancer-associated single nucleotide polymorphism  facilitates the accessibility of the miRNA-Ago1 complex to the pancRNA_CDH1, increasing CDH1 gene silencing [In different prostate cancer cell lines and tumors, Pisignano and colleagues showed that the gene encoding E-cadherin , supports an open chromatin state by participating to the formation of R-loop. This structure facilitates the accessibility of Vimentin gene to the transcriptional machinery and enhances the NF-Kβ binding to Vimentin promoter, thus resulting in increased gene transcription [As mentioned above, pancRNAs can also be involved in the formation of a RNA:DNA hybrid (named R-loop) to affect gene expression. This kind of mechanism was found in colon cancer cells and in two different breast cancer cell lines, MCF7 and MCF10, where the cription .Khps1, transcribed in antisense orientation to SPHK1. Khps1 is bound to a homopurine stretch (TFR2), forming a DNA-RNA duplex able to interact with p300/CBP, thus establishing an open chromatin structure that facilitates the E2F1-mediated SPHK1 transcription [Beside the possibility to form a R-loop, pancRNAs may be involved in the formation of a DNA-RNA triplex. Recently, it has been reported that the expression of the proto-oncogene SPHK1, involved in the control of apoptosis, requires a pancRNA, termed cription .Collectively, these studies  in the promoter regions affect the function of these promoter-proximal transcripts, by impacting chromatin dynamics and transcription factor recruitment. To this regard, several disease-associated SNPs map to noncoding regions, including promoters , 64, butCollectively, the reported findings highlight pancRNAs as potential candidates for transcriptional reprogramming. To this regard, the epigenome profile could be re-written by a set of epigenetic modifiers, including pancRNAs. Manipulations of these molecules could be employed in medical purposes.


SVL expression. We further report the identification and characterization of the EBB3 gene. EBB3 is a temperature-responsive, epigenetically-regulated, positive regulator of bud-break that provides a direct link to activation of the cell cycle during bud-break. EBB3 is an AP2/ERF transcription factor that positively and directly regulates CYCLIND3.1 gene. Our results reveal the architecture of a putative regulatory module that links temperature-mediated control of bud-break with activation of cell cycle.Bud-break is an economically and environmentally important process in trees and shrubs from boreal and temperate latitudes, but its molecular mechanisms are poorly understood. Here, we show that two previously reported transcription factors, EARLY BUD BREAK 1 (EBB1) and SHORT VEGETATIVE PHASE-Like (SVL) directly interact to control bud-break. EBB1 is a positive regulator of bud-break, whereas SVL is a negative regulator of bud-break. EBB1 directly and negatively regulates  EBB1 and a MADS-box gene SVL encode two regulators of poplar bud break. Here, the authors report another AP2/ERF transcription factor EBB3, which functions together with EBB1, SVL, and cell cycle progression promoter CYCD3.1 to regulate poplar bud break.An AP2/ERF family gene  Populus, cessation of shoot growth and the induction of dormancy are either induced or accelerated by short days (SDs), and prevented or delayed by long days (LDs)2. The integration of photoperiod detection and growth inhibition involves the convergence of the regulatory activities of the circadian clock machinery (involved in photoperiod sensing) on the FT (FLOWERING LOCUS T) regulatory hub6. In poplar, the clock daylength/nightlength sensing components LHY1 (LATE ELONGATED HYPOCOTYL 1), LHY2, GI (GIGANTEA), and CO1/2 (CONSTANS) regulate FT2  expression in accordance with the day length9. High FT2 expression promotes active growth, while FT2 repression leads to early growth cessation and bud set10. The signaling downstream of FT2 involves Like-APETALA1 (LAP1), which directly and positively regulates AINTEGUMENTA-Like 1 (AIL1) gene11, while AIL1 directly regulates D-type cyclins, an important cell cycle progression check point12. Thus, growth cessation and bud-set prior to dormancy establishment have co-opted genes and signaling cascades that regulate the photoperiodic floral initiation pathway.The alteration of periods of active growth and dormancy is a widespread adaptive strategy in plants from seasonal climates that enable them to survive unfavorable conditions associated with prolonged periods of low temperature and/or moisture stress. In boreal and temperate woody perennials, winter bud dormancy is developed in the fall and involves, in chronological order: cessation of shoot elongation, the formation of buds (bud-set), and establishment of dormancy. In most woody plants, including 13. It was recently shown that abscisic acid (ABA) plays a major role in the establishment of bud dormancy14. Specifically, under SDs, ABA concentration and signaling increase and promote the biosynthesis and deposition of callose at the plasmodesmata (PD) to develop obstructions known as PD sphincters18. These symplastic blockages isolate shoot apical meristem (SAM) from growth-promoting signals19.Following growth cessation and bud set, continual exposure to SD results in the establishment of a dormant state during which buds are insensitive to growth-promoting signals22. This phase, known as dormancy release, ensures that plants resume growth only after the stable return of favorable growth conditions; in essence, it represents a clock measuring the length of winter23. Although both LDs and warm temperatures are required for a return to active growth, the dominant triggering signal is warm temperatures23. Therefore, in contrast to growth cessation and dormancy establishment phases, which are photoperiod-regulated processes, dormancy release and reactivation of growth are primarily thermo-regulated processes25. How temperature controls dormancy release and bud-break is poorly understood at the molecular level.Once dormancy is established, resumption of active growth requires prolonged exposure of the bud to low temperaturesDormancy Associated MADS-box (DAM) genes from the evergreen peach mutant has led to speculation that dormancy release and bud-break may share similarities with vernalization26. Indeed, DAM genes and MADS-box genes like FLOWERING LOCUS C (FLC) are repressed during vernalization, which by low temperatures, and expression changes are correlated with changes in activating/repressive histone modifications28. Furthermore, over-expression of DAM genes causes delayed bud-break similar to how FLC overexpression leads to delayed flowering31. However, since evergreen mutant does not cease growth in response to SDs, its relevance to dormancy and bud-break remains unclear. Similarly, co-suppression of an apple DAM1 (MdDAM1) led to an inability of the transgenic plants to cease growth and establish dormancy32. Recently, another MADS-box gene with homology to SVP in Arabidopsis, named SVP-like (SVL), was shown to negatively regulate bud-break29. SVL not only regulates bud-break but is also a major hub in the signaling cascade leading to dormancy, downstream of ABA signaling29. Expression of SVL is regulated by both SD photoperiod during dormancy initiation and low temperature during dormancy release33. Thus, SVL provides the regulatory link between the photoperiodic and thermo-signaling pathways during the onset of dormancy and its release.The discovery of the EARLY BUD-BREAK 1 (EBB1) gene encoding a transcription factor of the AP2/ERF family with high homology to Arabidopsis SAM activity regulator DORNROSCHEN (DRN), as a conserved positive regulator of bud-break35. However, the regulatory context of EBB1 and its link with the SVL pathway remained unclear. As a result, there are significant gaps in our knowledge of how bud-break is regulated. Here, we demonstrate that EBB1 acts as a direct upstream repressor of SVL. Importantly, we identify EBB3, a transcription factor of the AP2/ERF family as a component of the EBB1-SVL bud-break pathway. Our results demonstrate that EBB3 is epigenetically regulated by low temperature and promotes bud-break by positively regulating cell proliferation-related genes. The elucidation of the role of EBB3 thus provides a missing link of low-temperature signals, cell proliferation, and the control of bud-break.Earlier we identified the poplar Populus tremula X Populus alba) was screened in a field trial and a mutant with early bud-break was identified  and the corresponding gene EBB3 (EARLY BUD-BREAK 3). We positioned the tag in the genome sequence on chromosome XII and found two genes (Potri.012G108400 and Potri.012G108500) flanking the insertion within 10Kb up and downstream of the insertion site. Potri.012G108400 was 7.8Kb upstream and Potri.012G108500 2.4Kb downstream of the activation tag insertion , and transgenic lines overexpressing this gene as EBB3-OE. To further confirm the function of EBB3 in bud-break, we downregulated the expression of EBB3 (EBB3-RNAi). Three independent transgenic lines (EBB3-RNAi) with significantly reduced EBB3 expression were generated. Contrary to EBB3 overexpressers, all three EBB3-RNAi lines  showed significantly delayed bud-break compared to WT-T89  and highly upregulated during winter/spring months (Nov-March)  to a regime of SDs (10WSD: 10 weeks under SD photoperiod) to induce growth cessation and dormancy, followed by exposure to 5 weeks of cold temperatures (4 °C) (5WC) (dormancy release) and 2 weeks of a LD and warm temperature (2WLD) to induce bud-break. EBB3 expression was low in actively growing apices (0 W: time point before inductive treatments) and under SD photoperiod (10WSD) , remained high after 5 weeks of cold treatment (5WC), and further increased following bud exposure to warm temperature and LD (bud-break) (2WLD)  Fig. . This sueak Fig. . To moreSD) Fig. . EBB3 waLD) Fig. . This inEBB3 expression change. Low temperatures have been found to trigger histone modifications that promote or repress transcription of regulatory genes36. It has been previously shown that repressive H3 lysine 27 trimethylation (H3K27me3) histone marks are responsive to cold temperature in peach and pear37. Therefore, we investigated whether EBB3 activation after cold exposure involves changes in H3K27me3. Indeed, the repressive mark H3K27me3 was significantly decreased at the EBB3 locus after exposure to low temperature that promotes dormancy release   and under-expressing (EBB1-ami) plants. SVL expression was significantly repressed in EBB1 overexpressers and upregulated in EBB1 downregulated plants  to determine if EBB1 binds to the SVL promoter. Since EBB1 is an AP2/ERF transcription factor, binds to a consensus motif (GCCGCCA) known as a GCC-box38, we scanned the −2000 bp putative SVL promoter region and found a GCC-box −150 bp from the translation start site . Chromatin was isolated from EBB1-GFP DNA transfected poplar protoplasts using an anti-GFP antibody, whereas the IgG antibody was used as control. Consistent with the in vitro EMSA binding assay, the ChIP-qPCR results showed strong binding of EBB1 with the regulatory region of the SVL containing the GCC-box  are integrated, we first investigated potential interactions between EBB1 and SVL. We studied the SD) Fig. , stronglSD) Fig. . The patLDs Fig. . Since tnts Fig. . These rite Fig. . EBB1 bobox Fig. . Thus, tEBB3 in EBB1 transgenic plants. EBB3 was significantly upregulated in EBB1-OE plants and downregulated in EBB1-ami plants  and downregulated (EBB3-RNAi) transgenic plants. There was no significant change in the expression of SVL in EBB3-OE or EBB3-RNAi plants  plants whereas conversely it was upregulated in SVL suppressed (SVL-RNAi) plants  was significantly higher than in WT-T89 plants , 110 after 5-week cold treatment (5WC), and 1162 at the time of bud-break (2WLD)  gene was the only gene among all differentially regulated genes, that was statistically significantly (P < 0.05) downregulated in the EBB3-RNAi plants at all time points studied using RNA-seq  motif in the promoter region of CYCD3.1. To confirm that CYCD3.1 is a direct target of EBB3, we performed EMSA using 6xHIS-tagged EBB3-purified protein and the 27 bp GCC-box. The EBB3 protein specifically bound the GCC-box fragment of the CYCD3.1 promoter, whereas no binding was observed with the mutated version of the GCC-Box (TCCTTTCCA) , a key promoter of the G1/S progression of the cell cycle whose expression correlates with bud reactivation of growth at bud-break39. The identification of these regulatory interactions would benefit further from in planta analysis of their spatiotemporal specificity during dormancy-activity cycle. Furthermore, genetic evidence derived from double and triple mutants is needed to confirm the functionality and hierarchy of the proposed model.We report the identification of a putative regulatory pathway comprised of three transcription factors  that mediate bud-break in poplar Fig. . Our gen34, here we elucidated the interaction between these two components of the bud-break pathway by showing that EBB1 directly and negatively regulates SVL expression. This regulation is likely most relevant at the activity-dormancy transitions as suggested by the dramatic and opposing changes in the expression of these two genes that occur at these transition periods. EBB1 is downregulated, while SVL is upregulated during dormancy induction and establishment , while ABA upregulates expression of SVL29. Rising EBB1 levels during dormancy release likely break this feedforward loop by downregulating the expression of SVL and consequently ABA levels. Thus, EBB1 not only affects SVL expression but also likely indirectly plays a part in reducing the concentration of ABA during chilling to release apices from dormancy.While EBB1 and SVL were previously discovered as regulators of bud-breakEBB3 expression highly correlates with dormancy release and bud-break. EBB3 is almost undetectable during dormancy establishment and significantly increases with the progression of the cold treatment peaking right around the bud-break time.We also report the identification and characterization of EBB3, a component of the EBB1-SVL-mediated regulatory mechanism, that controls bud-break. We present several lines of evidence that clearly implicate EBB3 in the regulation of bud-break. First, transgenic up and downregulation of the gene leads to accelerated and delayed bud-break of the transgenic plants compared to WT controls Fig. . EBB3 ex40. In Arabidopsis, EBE is a positive regulator of axillary bud outgrowth40. Apical dominance plays a central role in axillary bud dormancy in Arabidopsis41. In contrast, winter bud dormancy is imposed by environmental cues and requires resetting through a cold treatment23. Thus, it is likely that dormancy regulation in the axillary and apical bud may be different, particularly upstream of EBB3/EBE. Nevertheless, the downstream processes controlled by the two genes appear to share significant similarities. For example, in Arabidopsis, one of the most prominent transcriptomic changes in response to inducible EBE upregulation involves cell cycle genes, including CYCDs40. Thus, both EBE and EBB3 appear to activate cell cycle. In Arabidopsis, the genes differentially regulated by EBE have an overrepresentation of TCP18/BRC1 binding sites40. It was previously found that in poplar, SVL positively regulates TCP18/BRC1 during dormancy. In Arabidopsis, Class II TCPs, to which BRC1 belongs, repress organ growth by inhibiting cell proliferation at the G1-to-S transition43. Here we find that SVL negatively regulates EBB3. Thus, the increasing and decreasing abundance of SVL during dormancy can act in balancing cell proliferation and bud-break competence by regulating TCP18/BRC1 and EBB3. Higher TCP18/BRC1 levels may inhibit cell proliferation and negatively regulate bud-break44, while high EBB3 levels post cold treatment would activate cell cycle and bud-break.EBB3 encodes a transcription factor of the AP2/ERF family with highest homology to AtERF114, known as ERF BUD ENHANCER (EBE)EBB3 locus correlate with gene expression and exposure to dormancy releasing low-temperature signal. Our data show that following exposure to low temperature, there is a reduction in the repressive H3 lysine 27 trimethylation (H3K27me3) marks across the whole sequence of the EBB3 gene with simultaneous upregulation of EBB3  histone marks in pear EBB1 and peach DAM genes are responsive to cold temperature37. This correlation between repressive marks and expression is not perfect and indicates that EBB3 is under complex regulatory mechanism including but not limited to epigenetic modifications. For example, transcription activators may upregulate the gene prior and at bud-break but repressors downregulate the gene past bud-break. Thus, the summary of all data strongly implicate EBB3 in the regulation of bud-break in poplar and this may involve at least in part low temperature-responsive changes in EBB3 expression via epigenetic changes. The importance of epigenetic changes in the regulation of low-temperature response and bud-break was also recently demonstrated by the role of a poplar DEMETER-LIKE 10 (DML), a DNA demethylase, plays in the regulation of bud-break45. To find potential commonalities in the mechanisms governed by EBB3 and DML during bud-break, we compared the differentially expressed genes (DEGs) in EBB3 and DML knocked-down transgenics. A total of 106 common genes were discovered , a MADS-box gene that regulates bud-break, is not regulated by histone marks29, while both EBB1 and EBB3 seem to be regulated by histone modifications48 . In s48 Fig. . Thus, a49. Poor synchronization of bud phenology with local climates can lead to significant damage from early and late frosts, poor crop yields, and the outbreak of pest and disease problems. A better understanding of the molecular mechanisms of the processes involved will allow for knowledge-based approaches to mitigate existing challenges.The discovery of the three genes and their interactions opens the door for a more comprehensive characterization of the genetic mechanisms regulating bud-break and enables approaches to modify dormancy-associated traits in shrubs and forest and fruit trees from temperate and boreal latitudes more rapidly and strongly. Such breeding and biotechnological approaches may become increasingly important in the face of rapid climate change, whose pace is likely to exceed the capacity of the forests to adapt through natural selection alone50. The EARLY BUD-BREAK3D (ebb3D) mutant was discovered in a field trial with 2-year-old plants in which all 4 ramets (arranged in 2 randomly distributed blocks with 2 ramets each) of the line flushed earlier than wild type. The early bud-break phenotype of the ebb3D mutant line was validated in a controlled growth chamber experiment, as described below.A poplar activation tagging population was generated and screened50. The recovered sequence was positioned in the poplar genome by BLAST searches and proximal genes to the insertion site were studied for activation by comparing their expression in the ebb3D mutant and wild type plants .The sequence flanking the insertion of the activation tag was recovered using TAIL-PCRPopulus tremula x alba; wildtype/WT-717) and T89  were used in the experiments. The EBB1-OE line generated in the WT-717 background, and the SVL-OE and SVL-RNAi lines were generated in the WT-T89 background34. Plants were first cultivated in vitro in half-strength MS medium (Duchefa) under sterile conditions for 5 weeks and then transferred to small pots with soil for 2 weeks for acclimation to greenhouse conditions. Plants were then transferred to 2 L pots and grown for 4 weeks in the greenhouse  before the manipulative dormancy experiments. For growth cessation and dormancy induction, plants were transferred from the greenhouse to growth chambers and grown for 10 weeks under short-day (SD) photoperiod . Height, numbers of leaves formed after initiation of SD treatment and bud-set at the apex were recorded weekly34 Dormant plants were then placed in a cold room at 4 °C for 5 weeks to meet the chilling requirement. To induce bud-break, plants were transferred to warm temperature and LD photoperiod . Bud-break of the apical bud was monitored and scored daily34.Hybrid clones 717-IB4  using Gateway BP and LR recombination reactions respectively (Invitrogen). The constructs were sequence-verified and transformed into Agrobacterium strain C58 using the freeze-thaw method51. To generate the EBB3-RNAi construct, a 200 bp fragment was amplified using full-length the EBB3 cDNA as a template and the following primers (Forward 5′-CACCTCTGGC GGGACCGAAT-3′ and Reverse 5′-GCAGGCGGCATGACATGGCT-3′). The amplified fragment was cloned into pENTR/D-TOPO (Invitrogen), sequence verified, and then transferred into the plant transformation vectors (CaMV 35S promoter) to generate the EBB3-pK7GWIWG2 (I) construct, which was then transformed into Agrobacterium strain GV3101pmp90RK52. The EBB3-RNAi construct was transformed into WT-T89 and whereas EBB3-pK7GWIWG2 (I) construct was into WT-717 plants54. For expression analysis during the dormancy cycle, we collected vegetative apical buds from small branches of three individual wild aspen  trees for each month of the dormancy period (September to April). Whole buds were immediately frozen in liquid nitrogen. For tissue-specific expression analysis, different tissue samples were collected from greenhouse grown four months old WT-717 plants and stored at −80 °C until processed. In control condition experiments, shoot apices for gene expression analyses were collected at the same time of the day , after 0, 10WSD, 2WC, 5WC, and 2WLD frozen in liquid nitrogen, and stored at −80 °C.To generate GFP-tagged version of EBB1 and EBB3 DNA, we amplified EBB1 and EBB3 CDSs using gene specific primers  containing B1 and B2 recombination site sequences, respectively. The amplified fragments were cloned into destination vector pMDC83 (CaMV 35S promoter) using Gateway LR recombination reaction (Invitrogen). The sequence-verified plasmids were then used for the poplar protoplast transfection assays.3 solution was applied directly to the shoot apex and sampled after 24 h for expression analysis55.For the ABA treatment, apices were cut and placed in MS solution with or without 50 µM ABA for 2 h and were then sampled for RNA extraction and expression analysis. For gibberellin (GA) treatments, 3 mM GAACT7 (WT-717 background) and ubiquitin (UBQ) (WT-T89 background) genes were selected and validated using geNorm Software56 as a reference gene. qRT-PCR analyses were carried out with StepOnePlus Real-Time PCR System  and Light Cycler 480 II (Roche) using Maxima SYBR Green qPCR Master Mix (Thermo Fisher Scientific Co.) and relative expression values were calculated using the ∆-Ct-method12. A complete list of the primers used for RT-PCR is presented in Supplementary Table Total RNA was extracted using an RNeasy® Plant Mini Kit (Qiagen), or an Aurum Total RNA kit (Bio-Rad), or an RNeasy® Plus Universal Kit (Qiagen). Total RNA 10 µg) was treated with RNase-Free DNase (Qiagen) and cleaned using an RNeasy® Mini Kit (Qiagen). One µg of the RNA was used to generate cDNA using an iScript cDNA synthesis kit (Bio-Rad) or SuperScriptII . 0 µg was 2) for 6–8 weeks and around 20 fully expanded leaves (1 g) were used to isolate protoplast58. Briefly, fully expanded leaves were cut into 0.5–1 mm fine strips and digested in 40 mL of enzyme solution  in dark for 4–5 h without shaking and later 30 min shaking at 50 rpm to release the protoplasts. The protoplasts were harvested and diluted to a concentration of 4 × 107 protoplast mL−1. The plasmids used in transfections were isolated using Qiagen Plasmid Midi Kit (Qiagen Inc.), and 40 µg of EBB1-GFP (EBB1-pMDC83) and 50 µg of EBB3-GFP (EBB3-pMDC83) plasmid DNA added to 500 µL (2 × 107 protoplasts) of protoplast for each transfection assays.58 After transfection, protoplasts were incubated in dark for 20 h and then 4 h on ice. Successful expression of the GFP-tagged proteins was monitored by examining GFP fluorescence of the transfected poplar protoplasts using Olympus SZX16   was used to crosslink the protein to DNA, and 125 mM glycine was used to stop the reaction. The crosslinked protoplasts were used to isolate the chromatin using EpiQuick ChIP Kit . Anti-GFP  and IgG  antibodies were used for chromatin immunoprecipitation. The enrichment of bound target sequences was quantified by qRT-PCR using the DNA recovered from anti-GFP and IgG ChIPs and normalized against 5% input DNA. All ChIP experiments were carried out in three biological replicates.After transfection and successful validation of GFP fluorescence, protoplasts were harvested by centrifugation at 200 × SVL promoter showed the presence of an ERF binding site  ~150 bp upstream (−150 bp) of the translation start site. Two sets of primers that can amplify 150 bp fragments, one with the binding site and the other without the binding site were amplified by using biotin-labeled primers (see Supplementary Table P. trichocarpa genomic DNA. The fragments were purified using an E.Z.N.A. Gel Purification Kit (Omega Bio-tek) prior to use in gel-shift assays. EMSAs were performed using biotin-labeled promoter fragments and cell extracts from Arabidopsis protoplasts expressing HA-EBB1/HA-EBB3 or control extracts from non-transformed protoplasts11. After confirming that HA-EBB1/HA-EBB3 bound to the SVL promoter fragment, smaller, biotin-labeled F1 fragments (27 bp) containing the GCC-box (GCCGCC) or a mutated version of the GCC-box were synthesized. For the EMSA reaction, 10 μL protoplast cell extract was mixed with 0.5 μL biotin-labeled DNA (10 fmol/mL), 0.4 μL nonspecific competitor , and 0.5 μL BSA (20 mg/mL) in EMSA buffer. To determine binding specificity, mutated GCC-box fragments were also used. The mixtures were incubated on ice for 10 min followed by 30 min at room temperature to allow binding. The samples were then separated on a non-denaturing polyacrylamide gel (5%) prepared with 0.5XTBE and transferred to a Hybond N + membrane . Finally, a Light Shift Chemiluminescent EMSA kit (Pierce) was used for cross-linking and detection.Sequence analysis of the PtaEBB3 was amplified using forward 5′-AAAAGAATTCATGGATGT GATGGTTT-3′ and reverse 5′-AAAAGCGGCCGCAAGCCCCTCGTCTTTG-3′ primers and cloned into the pET32a(+) vector at the EcoRI/NotI sites. The construct was sequenced validated and transformed into One Shot™ BL21(DE3) E. coli cells (Invitrogen). Colonies growing on selection media were validated using PCR and restriction digests. Successfully validated colonies were used to inoculate 50 mL LB medium grown for 2 h at 37 °C to obtain an OD600 between 0.6 and 0.8 and then induced for 20 h with 0.1 mM IPTG  at 16 °C. The PtaEBB3-His protein was purified on Ni2+‐NTA agarose resin (Qiagen) following the manufacturer’s protocol. Forward and reverse primers containing the ERF binding site  were synthesized (Eurofins Genomics). The double‐stranded probe was prepared by annealing the forward and reverse primers. The EMSA assay was carried out using the Electrophoretic Mobility-Shift Assay (EMSA) Kit, with SYBR™ Green and SYPRO™ Ruby EMSA stains (Invitrogen), according to the product instructions. Protein-DNA complex samples were loaded onto a 4–20% gradient polyacrylamide native gel (Bio-Rad) and run at 100 V for 1 h. The amount of DNA and protein used in the various experiments are indicated in Fig. The CDS of http://www.epigenesys.eu/en/protocols/bio-informatics/1283-guidelines-for-rna-seq-data-analysis. Reads quality was assessed using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/), v0.11.4. Ribosomal RNA (rRNA) reads were quantified and filtered using SortMeRNA59. Reads were filtered to remove adapters and trimmed for quality using Trimmomatic and then mapped to the Populus genome using STAR61. Then reads were remapped using BWA-MEM62 and peaks were called genome-wide using MACS263 with the non-default parameters29, on sequencing libraries down-sampled to 10 million PE reads. The depth of this down-sampled library was estimated by an ad hoc saturation/rarefaction analysis based on the number of peaks identified by MACS2 in varying subsets of the original dataset. These downstream analyses were solely used to estimate the fraction of the genome mapped under the different growing conditions. The data were normalized using ratios obtained for the analysis of the EBB3 locus histone methylation status.To perform ChIP-seq experiment, apical buds from WT plants of three biological replicates were collected from before dormancy inductive treatments (0 W), after 6 and 10 weeks under SD conditions (6WSD and 10WSD), and again after an additional 4 weeks of cold treatment (4WC). Anti-trimethyl-Histone H3 (LysK27)  and anti-Histone H3  antibodies were used for chromatin immunoprecipitation. Paired-end (PE) sequencing was done by BGI-Tech. Sequencing reads were processed following the guidelines described at EBB3 gene including 2 kb upstream and downstream region were extracted from the alignment. Coverage was calculated in the above region, transformed to log2, and corrected for the abundance differences between samples . Finally, the H3K27me3 abundance was normalized by H3 abundance, the mean of three biological replicates was calculated and curves were fitted to the data by local polynomial regression. R64 and Bioconductor65 were used to compare differences in histone methylation between the time points.Reads mapped to the sequence of the http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Residual ribosomal RNA (rRNA) contamination was assessed and filtered using SortMeRNA59  using the rRNA sequences provided with SortMeRNA . Data were then filtered to remove adapters and trimmed for quality using Trimmomatic60 . After both filtering steps, FastQC was run again to ensure that no technical artefacts were introduced. Filtered reads were pseudo-aligned to v1.1 of the P. tremula transcripts66 using kallisto67 . Statistical analysis of single-gene differential expression between conditions was performed in R68 (v3.6.0) using the Bioconductor69 (v3.9) DESeq2 package70 (v1.20.1). FDR  adjusted P-values were used to assess significance; a common threshold of 1% was used throughout. For the data quality assessment (QA) and visualisation, the read counts were normalised using a variance stabilising transformation as implemented in DESeq2.Illumina HiSeq 2500 was used to perform RNA-seq for 125 cycles in paired-end mode at BGI  using RNA isolated from apices of wild type and EBB3-RNAi at 10 weeks SD, 2 W, and 5 W cold and 2 W LDs. Briefly, the quality of the raw sequence data was assessed using FastQC (Further information on research design is available in the Supplementary informationPeer ReviewReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1-9