Escherichia coli bacteriophage T5 has three temporal classes of genes . All three classes are transcribed by host RNA polymerase (RNAP) containing the σ70 promoter specificity subunit. Molecular mechanisms responsible for the switching of viral transcription from one class to another remain unknown. Here, we find the product of T5 gene 026 (gpT5.026) in RNAP preparations purified from T5-infected cells and demonstrate in vitro its tight binding to E. coli RNAP. While proteins homologous to gpT5.026 are encoded by all T5-related phages, no similarities to proteins with known functions can be detected. GpT5.026 binds to two regions of the RNAP β subunit and moderately inhibits RNAP interaction with the discriminator region of σ70-dependent promoters. A T5 mutant with disrupted gene 026 is viable, but the host cell lysis phase is prolongated and fewer virus particles are produced. During the mutant phage infection, the number of early transcripts increases, whereas the number of late transcripts decreases. We propose that gpT5.026 is part of the regulatory cascade that orchestrates a switch from early to late bacteriophage T5 transcription.The  Escherichia coli bacteriophage T5 injects its genome into the host in two steps ) ultracompetent cells (Stratagene) were used for molecular cloning  ATP at 30 °C for 1 h, followed by a clean-up on Ni-NTA (Qiagen). 1 μg of RNAP core enzymes or individual RNAP subunits  were applied as small drops on a Hybond ECL membrane and annealed for 3 min at 50 °C. Then, membranes were blocked in PROB buffer  (pH 7.4), 200 mM KCl, 1 mM DTT, 2 mM MgCl2, 10% glycerol, 0.5% Tween-20, 1% nonfat dried milk) for 2 h at room temperature. Afterwards, each piece of membrane was enveloped in a parafilm sack containing 150 μL of radiolabeled proteins solutions with/without added non-labeled proteins (depending on the design of experiment) in PROB buffer and incubated for 2 h at room temperature. Next, membranes were washed three times with 1 mL of PROB buffer and dried at room temperature for 15 min. Results were revealed using a PhosphorImager (Molecular Dynamics).The recombinant gpT5.026 protein was labeled with an isotope escribed ,21,22, fescribed . BrieflyE. coli RNAP , under the control of a promoter lacUV5. The plasmid pBRαLN encodes the N-terminal domain and the linker of E. coli RNAP α subunit (aa 1–248) under the control of tandem promoters lpp and lacUV5. A PCR fragment containing gene T5.026 was cloned into the pBRαLN plasmid as a C-terminal fusion with the α subunit coding sequence. The resulting plasmid was named pBRαLN-gpT5.026. E. coli strain BN469 contains the F’ episome, which carries the lacZ gene under the control of lac promoter and phage λ operator OL2. This strain was transformed with pBRαLN-gpT5.026 together with one of the plasmids A1–E8. All further procedures were performed as previously described  σ70), was prepared as previously described [2) containing 0.02% Tween 20 at 25 °C. Final assay mixtures (800 μL) contained 1 nM labeled RNAP holoenzyme and DNA probes at various concentrations. The TMR fluorescence intensities were recorded with an excitation wavelength of 550 nm and an emission wavelength of 578 nm. Time-dependent fluorescence changes were monitored after manual mixing of RNAP beacon (800 μL) and a DNA probe (<20 μL) in a cuvette; the mixing dead time was 15 s.RNAP holoenzyme containing σescribed . FluoresDouble-stranded and fork junction DNA probes were prepared by the annealing of DNA oligonucleotides synthesized by Integrated DNA Technologies, as described previously ,29.To obtain equilibrium dissociation constants (Kd) of the RNAP beacon with oligonucleotides and fork junctions, the experimental dependence of the fluorescent signal amplitude on DNA probe concentration was fitted to a chemical equilibrium equation , as previously described . The Kd 70 subunit ; and two major additional bands  was detected in RNAP preparations in early studies of bacteriophage T5 infection [D20-21 is a major head protein (pb8) with a calculated molecular weight of ~50 kDa [026, coding for gpT5.026, is an early gene, located close to the boundary of FST- and SST-DNA , while the abundance of late transcripts (but not the kinetics of their accumulation) was decreased. These effects were highly reproducible. Quantification of radioactivity in primer extension product bands . No effE. coli RNAP is known to specifically bind to certain model promoter fragments [70 derivative (beacon) with a fluorescent label site specifically incorporated in proximity to region 2.3, the part of σ70 involved in the recognition of the −10 promoter element. An RNAP holoenzyme containing labeled σ70 has low ground-level fluorescence, because region 2.3 aromatic amino acids quench the fluorescence of the label. After binding to a promoter and establishing specific contacts between region 2.3 aromatic amino acids and the −10 promoter element, the label is “unquenched”, leading to a strong increase in fluorescence. ragments ,40,41,42ragments ,29. The 70 RNAP holoenzyme, and suggest that gpT5.026 may affect formation of the open promoter complex by decreasing affinity of the enzyme to the −5/+2 segment of the non-template strand of the transcription bubble.We found that gpT5.026 improved the binding of a double-stranded −38/−12 probe by ~3-fold A. IntereThe inhibition of the −12/+2 oligonucleotide binding to RNAP was used to evaluate the Kd value of gpT50.26 binding to RNAP. To this end, we measured the signal generated by a 1 nM RNAP beacon in the presence of 300 nM −12/+2 without gpT5.026 and upon the addition of 100, 20, and 5 nM of gpT5.026. As can be seen from T5.026 in preparations of RNAP obtained from infected cells. GpT5.026 has no homologues among proteins with known function in public databases, although gene T5.026 was found in many T5-related phages and is highly conserved, not only within the Tequintavirus genus but also within the wider Demerecviridae family  region. It was demonstrated earlier that σ70 region 1.2 is involved in contacts with a discriminator [70 is remote from the gpT5.026 binding site. Therefore, gpT5.026 is likely to have an indirect effect on the interaction of RNAP σ70 holoenzymes with the discriminator region of a promoter.It has been shown in early studies that several proteins bind RNAP in T5-infected cells; however, only one of them (gpA1) has been positively identified based on genomic annotation ,14. Heree family . GpT5.02e family . These riminator . Figure A multiple alignment of gpT5.026 homologs from the indicated phages is presented. In vivo, the absence of gpT5.026 does not prevent the switch of transcription from early to late genes, but changes the number of transcripts synthesized. Compared to the wild-type phage infection, in the absence of gpT5.026, more early transcripts and fewer late transcripts are synthesized. The lower level of transcription of late genes encoding virion proteins is likely responsible for the observed five-fold decrease in the yield of the mutant phage. 70 holoenzyme and the discriminator region of promoters. These proteins and their mechanisms of action remain to be identified.Alignment of T5 early and late promoter sequences  shows th


Metabolic adaptation to nutritional state requires alterations in gene expression in key tissues. Here, we investigated chromatin interaction dynamics, as well as alterations in cis-regulatory loci and transcriptional network in a mouse model system. Chronic consumption of a diet high in saturated fat, when compared to a diet high in carbohydrate, led to dramatic reprogramming of the liver transcriptional network. Long-range interaction of promoters with distal regulatory loci, monitored by promoter capture Hi-C, was regulated by metabolic status in distinct fashion depending on diet. Adaptation to a lipid-rich diet, mediated largely by nuclear receptors including Hnf4α, relied on activation of preformed enhancer/promoter loops. Adaptation to carbohydrate-rich diet led to activation of preformed loops and to de novo formation of new promoter/enhancer interactions. These results suggest that adaptation to nutritional changes and metabolic stress occurs through both de novo and pre-existing chromatin interactions which respond differently to metabolic signals. Metabolic adaptation to different diets results in changes to gene expression. Here, the authors characterise the chromatin landscape and transcriptional network in mice on a diet of high saturated fat, compared to a diet high in carbohydrate, finding a dramatic reprogramming of the liver transcriptional network. With the spread of the Western lifestyle, including consumption of an energy rich diet high in fat, obesity and NAFLD are an emerging health concern3. Under conditions of calorie excess, the liver adapts to overnutrition through dynamic reprogramming of the transcriptome, one of many adaptations necessary to maintain metabolic homeostasis. Gene regulatory mechanisms mediating liver metabolic adaptation to excessive calorie intake are incompletely understood. Detailed analysis of the role(s) of promoter–enhancer interaction dynamics may provide mechanistic insights into the progression from nutrient-induced metabolic adaptation to disease.Animals have evolved to cope with fluctuations in food availability and nutritional content of food that can have major consequences on health. The responses to nutrient quality and availability range on a continuum from complete lack of nutrients to overabundance. These physiologic states have differing homeostatic requirements ranging from the mobilization of storage depots that provide energy and critical biomolecules to the biosynthesis of molecules mediating deposition of energy and metabolic precursors, with accompanying alterations in gene expression programs to facilitate biological responses. Such metabolic adaptation can lead, in the extreme, to the development and progression of obesity and related diseases including nonalcoholic fatty liver disease (NAFLD)5. These epigenomic events are cell-type specific and relative stable, but still have the flexibility to be fine-tuned in response to environmental cues7. Numerous studies indicate that diet can reprogram the epigenome and gene expression by affecting enhancers10. However, enhancers are usually located long distances away from their target genes, which makes it difficult to accurately assign specific enhancers to their target genes and delineate the details of gene regulation. Hi-C was recently used to identify genome-wide chromatin interactions and provide unique insights into higher-order chromatin organization11. Although unbiased, Hi-C requires very deep sequencing to characterize promoter–enhancer interactions, which are predicted to play important roles in gene regulation12. Promoter capture Hi-C (PCHi-C) was developed to facilitate identification of promoter-anchored chromatin interactions at high resolution13, enabling characterization of both stable and stimulus-responsive promoter–enhancer loops14. To date, limited data are available describing the dynamics of promoter–enhancer interactions during metabolic adaptation to changes in diet and progression to overt physiologic changes leading to disease and how these processes impact gene expression.Within nuclei, chromosomes are organized at multiple levels, from nucleosomal building blocks to more complex structures. Current models suggest that domain organization can reflect genome accessibility as well as regulatory contacts with promoters that modulate gene expression21. Hi-C and PCHi-C in combination with ChIP-seq and RNA-seq are performed on liver tissue from these animals to investigate the dynamics of promoter-anchored chromatin interactions during diet-induced obesity. We annotate genome-wide promoter-interacting regions in liver under different physiologic conditions and analyze how these chromatin interactions regulate metabolism. Furthermore, analysis of factor recruitment at activated enhancers indicates that Hnf4α may play a central role in orchestrating dynamic changes in chromatin organization and gene regulation in response to metabolic cues. These findings provide insights into how chromatin organization contributes to gene regulation during metabolic adaptation and disease.Given this dearth of information, a deeper understanding of the how the liver epigenome responds to diet, obesity, and the dysfunctional metabolic and physiologic state associated with obesity should provide insights into adaptation and disease. Here, we employ a long-standing model of diet-induced obesity in C57BL/6J mice. Male animals are placed on defined diets matched in protein content that differ in carbohydrate content, lipid content, and energy content. C57BL/6J animals on the high energy content, lipid-rich diet regimen typically recapitulate aspects of metabolic syndrome in humans including insulin resistance, impaired glucose tolerance, dyslipidemia, and increased triacylglycerols in liverD12492) for 20 weeks  over the course of the study , carbon dioxide production (VCO2) and energy expenditure (EE) were measured. As shown in Fig. 2 and VCO2 values decreased in the obese (LD) group compared with CD group. Significantly higher plasma concentrations of total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), aspartate transaminase (AST), and alanine transaminase (ALT) were found in the obese group, which indicated a systemic response to obesity that includes NAFLD  for RNA-seq. Differentially expressed gene (DEG) analysis identified 2066 genes upregulated in obese animals on the lipid-rich diet and 1663 genes upregulated on the carbohydrate-rich diet (22 activated by sterol regulatory element binding protein 1c (SREBP-1c) including elongation of long-chain fatty acids family member 6 (Elovl6), fatty acid synthase (Fasn), and stearoyl-CoA desaturase (Scd1) were downregulated in animals on lipid-rich as compared with animals on carbohydrate-rich diet. We also observed upregulation of hallmark genes integral to other aspects of fatty acid metabolism in obese animals , were upregulated23. Likewise, the type two acyl-CoA thioesterases Acot7, Acot8, Acot9, and Acot13 are upregulated in obese animals. These enzymes catalyze the hydrolysis of the thioester linkage between CoA and fatty acids, leading to the generation of free fatty acid and CoA in mitochondria, peroxisomes, and the cytosol24. Their upregulation in dietary conditions where intracellular fatty acid concentration is elevated reflects participation in fatty acid oxidation in mitochondria and peroxisomes as well as potential roles in signaling pathways dependent on unconjugated fatty acids25. Also upregulated in obese animals are genes classically associated with beta oxidation of fatty acids for energy production such as acyl-CoA dehydrogenases , enoyl-CoA hydratase (Ehhadh), and hydroxyacyl-CoA dehydrogenase (Hadh) as well as the mitochondrial carnitine-dependent lipid transporter, Cpt1. These differences in the transcriptome are reflective of the vastly different metabolic states and regulatory networks active in livers of animals on lipid-rich versus carbohydrate-rich diets. We selected for validation a subset of DEGs, those involved in the DNL and FAO pathways  differential enriched signal induced by diet  by chromatin immunoprecipitation sequencing (ChIP-seq) in liver. H3K27ac peaks were called by SICER and showed a significant overlap (∼73–89%) across biological replicates, regardless of group , bZIP , and C/EBP  family transcription factors  families were highly enriched  on carbohydrate-rich and lipid-rich diets11. As expected, high correlation between regimens indicated diet and obesity, with all its associated physiologic and metabolic alterations, did not affect the patterning of A and B compartments . To obtain a high-resolution view of promoter-anchored chromatin interactions in liver and their changes upon adaptation to obesity, we performed promoter capture Hi-C (PCHi-C) . Hi-C libraries were hybridized with 39,021 biotinylated probes targeting 25,747 promoters12 and each library was sequenced to an average depth of ~1 billion reads. After QC by HICUP39, we obtained an average 400 million unique and valid di-tags per replicate in each group, respectively (Supplementary Data 40. Using the CHiCAGO interaction score (cutoff = 5), we identified around 170,000 interactions in each diet sample, which is six times more than that identified in Hi-C. After filtering out trans-interactions and interactions that span more than 1 Mb, 149,798 and 151,203 cis interactions were retained  defined using mouse ENCODE data41 ; 705 were increased in frequency in lipid-rich diet and 1257 were more frequent in animals on carbohydrate-rich diet   Fig. . Taken tp < 0.0001). We note that the number of promoter–enhancer loops in which activation status of the enhancer changes (6452) in a preformed loop greatly exceeds the number of new loops formed following a change in conditions (1962). These findings suggest that activation of enhancers already interacting with a promoter represents an important mode of distal gene regulation during metabolic stress  of the enhancers identified as interacting with a given promoter in our capture Hi-C data reflected gene expression, we examined the overlap of the distal end of promoter interactions with H3K27ac differentially enriched loci, regardless of whether the number of contacts changed with condition. We discovered 4449 promoter–enhancer interactions in which H3K27ac was differentially enriched in lipid-rich diet and 2003 cases where this mark was enriched in carbohydrate-rich diet Fig. . The proess Fig. .42. We previously found that Hnf4α serves as a modulator to regulate the epigenome and gene expression during diet-induced obesity in the colon10. Since Hnf4α itself was differentially expressed in our system, we first validated its expression by qPCR and immunoblotting  a known ligand for HNF4α43, downregulation of HNF4α protein is potentially consistent with the increased turnover noted with other nuclear receptors upon ligand binding44. We do not at this time understand downregulation of HNF4α mRNA. Next, we performed ChIP-seq to understand how Hnf4α localization responds to obesity . Peak calling was done by HOMER and resulted in 45,830 peaks in carbohydrate-rich diet and lipid-rich diet. Most of the peaks were located (within 500 kb) near a TSS. We compared the differentially enriched Hnf4α binding regions across condition  and identified 3222 peaks more enriched in lipid-rich versus carbohydrate-rich diet and 1161 peaks with the opposite enrichment   Fig. , which s01) Fig. . In agre46 to determine which transcription factor binding motifs were present in these Hnf4α bound activated regions. As expected, among 36,125 Hnf4α-bound activated regions, the most significantly enriched consensus motif is that for HNF4α. Interestingly, the second most common motif is that for the basic leucine zipper domain (bZIP) family of transcription factors, which includes the C/EBP family . Using the same cutoff criteria as for Hnf4α, we observed 7327 differentially bound sites including 5102 more enriched in lipid-rich diet and 2225 sites more enriched in carbohydrate-rich diet  and found 55% of C/EBPα peaks colocalized with Hnf4α in liver  for chronic periods exhibit alterations at the physiologic, cellular, and molecular levels. Animals consuming excess calories from a diet rich in lipid exhibit extreme weight gain at both the whole-animal level as well as in specific tissues including liver and adipose. Physiologic measures such as fasting serum hormone levels, serum lipid levels, glucose, or insulin tolerance, and measures of energy expenditure and respiration, are likewise aberrant. In addition, chronic consumption of a lipid-rich diet leads to visible accumulation of lipid droplets in liver, to increased size of adipocytes and to accumulation of liver enzymes in serum, indicative of hepatocyte damage. These multi-system changes reflect adaptation of metabolism and physiology of the animal to an extreme diet. Not surprisingly, the chronic consumption of extreme diet and the adaptive responses described above are accompanied by molecular alterations in hepatocytes underlying dramatic remodeling of the transcriptome. Our results define physiologic responses leading to dynamic activation of pre-existing promoter–enhancer interactions and to formation of new enhancer/promoter loops  is one of several obesity-related diseases that is part of an emerging epidemic of metabolism-based pathologiesops Fig. , b. In t25. Thus, the transcriptional outcomes of adaptation to chronic consumption of lipid-rich diet likely feed forward to impact activation of master regulatory transcription factors governing the response.That the metabolic response to different diets at the level of chromatin interactions differs is potentially reflective of the underlying biology. The liver response to carbohydrates is complex, but includes insulin signaling pathways that converge on the transcriptional responses mediated by the sterol regulatory element binding protein 1c (SREBP-1c). This transcription factor is sequestered in an inactive state outside the nucleus until signaling events mediate its nuclear accumulation at target genes, including a gene set differentially expressed in this study. We anticipate that such a mode of transcriptional regulation may be facilitated by the de novo creation of enhancer/promoter loops downstream of binding by SREBP-1c. In contrast, the hepatocyte transcriptome, in response to the many alterations in physiology initiated by chronic consumption of a lipid-rich diet involved differential expression of genes regulated by a pair of nuclear receptors, peroxisome proliferator-activated receptor alpha (PPARα) and hepatocyte nuclear factor 4 alpha (HNF4α). Both PPARα and HNF4α are constitutively localized to the nucleus and are activated by ligand binding. Preferential utilization of preformed promoter/enhancer loops by ligand-activated transcription factors may be reflective of a general feature of this class of transcription factors. Ligand availability for PPARα and HNF4α is expected to be modulated by diet, and potentially by the action of genes differentially expressed in this system such as the acyl-CoA thioesterases9. In line with this notion, chromatin structure, as revealed by topologically associated domains, appears relatively stable in NAFLD48 despite the dramatic impact on the animal.In contrast to differential promoter/enhancer loops, we observed that higher-order chromatin organization, including A/B compartments and TAD boundaries, in liver is largely unchanged, even when comparing animals with such vastly different physiologic and metabolic profiles. Long-range chromatin architecture is closely associated with transcriptional programs in a cell-type-specific manner. Although our mouse model has dramatic physiologic and metabolic alterations, the observed liver phenotypes are reversible on removal of the lipid-rich diet49, in which FAO and DNL still cooperate. Gene expression change during NAFLD development is likely mediated through effects on transcription factors. Using HOMER, we found that the Hnf4α binding motif was enriched in the promoter regions of DEGs, a finding validated by Hnf4α ChIP-seq. HNF4α is an orphan nuclear receptor involved in metabolic regulation with the potential to both activate and repress transcription51. In our study, we observed that nearly 80% of Hnf4α binding sites overlapped with H3K27ac enriched loci, which suggests that Hnf4α prefers to bind at regions of open chromatin. HNF4α is primarily located in the nucleus and the protein level of Hnf4α decreased with NAFLD, suggesting that Hnf4α might be activated by its ligands and relocated in the genome to modify gene expression in NAFLD.In hepatocytes, the balance between de novo lipogenesis (DNL) and fatty acid oxidation (FAO) maintains homeostasis. In NAFLD, cellular concentrations of fatty acids are higher than normal; only the oxidative conversion of fatty acids and a decrease in lipogenesis minimizes lipotoxicity in liver. Our gene expression data indicate that genes involved in FAO are upregulated and genes involved in DNL are downregulated Fig. , e. This50. In our study, around 90% of HNF4α binding sites were stable between carbohydrate-rich diet and lipid-rich diet group, which led us to hypothesize that Hnf4α co-binding with other transcription factors may play an important role in response to external stimuli and physiologic change. Motif analysis suggested that Hnf4α binding sites were also enriched for the binding motif of C/EBPα, another important transcription factor in metabolism. C/EBPα ChIP-seq data showed that more than half the binding sites overlapped with Hnf4α binding sites, suggesting they might work together to regulate gene expression. Importantly, we observed that more than 20% of C/EBPα binding sites were changed by different diets, which was more than the number of HNF4α sites that changed (10%). CD36 is target gene of both HNF4α and C/EBPα, and the knockout of CD36 protects against NAFLD during exposure to a lipid-rich diet52. We found that CD36 expression was elevated under lipid-rich diet and showed an activated promoter–enhancer interaction. In the distal end of the interaction, HNF4α may function to act as a bookmark for C/EBPα, looping to the promoter of CD36 to upregulate its expression. Collectively, these data are consistent with a model in which HNF4α premarks specific positions for C/EBPα to regulate metabolism genes  for up to 20 weeks as previous described10. All animal experiments were approved by the NIEHS Institutional Animal Care and Use Committee and were performed according to the NRC Guide for the Care and Use of Laboratory Animals.Five-week-old C57BL/6 mice were purchased from Jackson Laboratory, were acclimated to the NIEHS animal facility  for one week and were subsequently placed on either a carbohydrate-rich diet  or a lipid-rich diet   were performed at 19 weeks of LD and CD. Mice were fasted and glucose was injected intraperitoneally at a concentration of 2 g/kg body weight. Blood glucose was measured with a glucometer at 0, 20, 40, 60, and 120 min. Intraperitoneal insulin tolerance tests (IPITTs) were performed 1 week later. Mice were fasted and insulin was injected intraperitoneally at a dose of 0.75 IU/kg body weight. Blood glucose was measured with a glucometer at 0, 15, 30, 45, 60, and 120 min.Mice were fasted overnight and blood samples were collected after 20 weeks of LD and CD. Plasma were collected and subjected to insulin measurements using the Mouse Insulin ELISA kit  and leptin measurements using the Mouse/Rat Leptin ELISA  following the manufacturer’s instructions. Samples were measured in triplicate. Plasma cholesterol, HDL, LDL, ALT, and AST were measured by Clinical Pathology Laboratory in NIEHS.2, VCO2, and energy expenditure were measured at 19 weeks of LD and CD using the TSE phenoMaster System  in NIEHS. TSE phenoMaster System is an open circuit indirect calorimetry system and air flow passed through each cage was controlled at ~0.4 l/min in our study. VO2 and VCO2 were analyzed for each individual cage every 27 min. Energy expenditure was calculated according to the TSE default setting using the following equation: total energy expenditure  = 3.941 × VO2 (l/h) + 1.106 × VCO2 (l/h). Data were collected for 3 days and normalized by weight; the first day was considered the acclimation period and was excluded from data analysis. Mice had unlimited access to food and water for the entire duration in CaloCages.Mice VO54. Livers tissue was collected, homogenized and filtered with a 70-μm nylon cell strainer. Around 10 million cells were fixed with 1% formaldehyde in PBS for 10 min at room temperature and cross-linking was terminated by addition of glycine at 0.125 M for 5 min. Cells were lysed with 10 mM Tris-HCl pH 8.0, 10 mM NaCl, 0.2% NP-40 and protease inhibitors for 30 min on ice. Nuclei were resuspended in 1 × NEBuffer2 with 1% SDS and incubated at 62 °C for 10 min. After incubation, nuclei were collected and resuspended in 1 × NEBuffer2 with 1% Triton X-100. HindIII  enzyme was added to the solution and the nuclei were digested at 37 °C overnight with agitation (750 rpm). Another 400 unit HindIII was added to the solution at 37 °C for another 1 h the next day. After digestion, 37.5 μl of 0.4 mM biotin-14-dATP , 1.5 μl of 10 mM dCTP, 1.5 μl of 10 mM dGTP, 1.5 μl of 10 mM dTTP, and 8 μl of 5 U/ul Klenow  were added and rotated at 37 °C for 1 h. After dATP filled in, the solution was incubated at 65 °C for 10 min to inactive the enzyme. Ligation was performed by adding 667 μl of water, 120 μl of 10X NEB T4 DNA ligase buffer, 100 μl of 10% Triton X-100, 12 μl of 10 mg/ml BSA, and 0.5 μl of 400 U/ul T4 DNA Ligase  and incubating at 16 °C for 4 h. Reverse cross-linking was done by adding 10 μl of proteinase K , 100 μl of 10% SDS, and 100 μl of 5 M NaCl, and incubated at 65 °C overnight. DNA was purified by phenol–chloroform extraction and 20 μg of DNA was incubated with T4 DNA polymerase  for 4 h at room temperature to remove the biotin from non-ligated ends. Then, DNA was sheared by Covaris and double size selected using AMPure XP beads as described in ref. 54. MyOne Streptavidin T1 DynaBeads  were washed, resuspended in 300 μl of wash buffer  and added in 300 μl of 2× binding buffer  with sheared DNA for 15 min at room temperature. The pulled down fragments were end-repaired, adenine-tailed, and ligated to adaptors  on beads as described in ref. 54. The immobilized Hi-C libraries were amplified using primers mix from NEXTflex Pre and Post Capture Kit  with 12 PCR amplification cycles. The resulting libraries were sequenced on Illumina Novaseq as 50 bp paired-end.In situ Hi-C was carried out as previous described with some modificationsHindIII restriction fragments that overlap Ensembl-annotated promoters of protein-coding, noncoding, antisense, snRNA, miRNA, and snoRNA transcripts12. Seven hundred and fifty nanograms of Hi-C library was captured according to the manufacturer’s instructions12. Briefly, DNA was denatured for 5 min at 95 °C and hybridized with RNA baits at 65 °C for 24 h. MyOne Streptavidin T1 DynaBeads  were used to pull down the biotinylated DNA/RNA. After library enrichment, the NEXTflex Pre and Post Capture Kit  was used to generate libraries with 10 PCR amplification cycles. The resulting libraries were sequenced on Illumina Novaseq as 50 bp paired-end.Promoter Capture Hi-C (PCHi-C) was carried out using the Agilent SureSelect target enrichment (SureSelectXT Custom 3–5.9 Mb library) system. Biotinylated RNA baits were designed to the ends of 39. The percentage of mapping efficiency, unique and validated di-tags were listed in Supplementary Data 36 was used to generate contact matrices . Normalization was performed by HiCExplorer hicCorrectMatrix (--filterThreshold -1.237 4 for LD group and --filterThreshold -1.248 4 for CD group), limiting to canonical autosomes, chrX, and chrY. TADs and TAD boundaries were defined by HiCExplorer hicFindTADs (--thresholdComparisons 0.05 --delta 0.1 --correctForMultipleTesting fdr). HiCExplorer hicPlotTADs was used for Hi-C matrix and TADs visualization. Chromatin loops were called by HiCExplorer hicFindEnrichedContacts (--method z-score --maxDepth 100000000) and filtered with z-score > = 6 and loop distance < = 1 Mb. Aggregate peak analysis plots were generated by summing the signal of pixels at loops  in 10 kb bins, excluding any loops within 300 kb of the matrix diagonal, then scaling by the average of the 4 corner pixels of the local background submatrix. HOMER was used to identify the A/B compartments at 50 kb bins using the following method: makeTagDirectory with parameters “-tbp 1 -restrictionSite AAGCTT -genome mm9 -removeSelfLigation”, combine the two replicates per group (makeTagDirectory), then runHiCpca with parameters “-res 50000 -genome mm9”.Hi-C reads were processed by HiCUP pipeline, which aligns reads, filters artifact fragments (such as circularized reads and re-ligations), and removes duplicates39. The percentage of mapping efficiency, unique and validated di-tags and capture efficiency were listed in Supplementary Data 40 to call the significant interactions with score cutoff 5. All the trans-interactions and those interactions span more than 1 Mb were discarded. EdgeR was used to identify the significantly increased and decreased promoter interactions with cutoff 0.001 as described in ref. 38. When calculating the correlation between gene expression and interaction number, TPM was divided in four categories: TPM = 0, TPM = 0–1, TPM = 1–10, and TPM > 10. To test whether H3K27ac at the distal end of the interaction, we categorized interactions according to whether at least one distal end overlapped an H3K27ac peak. To avoid the dramatic difference between nonoverlap and overlap with H3K27ac, we split the genes according to the number of total associated loops, resulting no interaction, 1 interaction with/without H3K27ac, 2–5 interaction with/without H3K27ac and > 5 interaction with/without H3K27ac. Overlap of interactions with functional genomic elements was performed using EpiExplorer41. EpiExplorer automatically calculates control sets for user-uploaded region sets, which is done by reshuffling the genomic positions while retaining the overall number of regions and the distribution of region sizes. The selected overlap criterion is at least 10% overlap. We divided the diet-induced promoter interaction in two categories: (1) rewired interactions that were significantly different between diets which is from EdgeR (p < 0.001), (2) interactions that were not significantly different but with differential H3K27ac enrichment at the distal end.PCHi-C reads were also processed by HiCUP pipeline10. The 20 μg chromatin in IP buffer ) was subjected to immunoprecipitation with 1 μg of H3K27ac , 5 μg Hnf4α  or 5 μg C/EBPα  antibody and incubated overnight. The samples were incubated with Dynabeads A/G beads for another 3 h. The beads were washed with the low salt , high salt , LiCl  and then twice with TE buffer as previously described10. After elution from the beads, the protein-DNA complexes were reverse crosslinked and DNA was purified by AMPure XP beads (Beckman). ChIPed DNA (1–2 ng) was prepared using NEXTflex Rapid Illumina DNA-Seq Library Prep Kit according to the manufacturer’s instructions . The resulting libraries were sequenced on Illumina Nextseq500 as 35 bp pair-end.Livers tissue was collected, homogenized and fixed with 1% formaldehyde in PBS for 10 min at room temperature. Cross-linking was terminated by addition of glycine at 0.125 M. Fixed tissue was lysed , protease inhibitor cocktail) and sonicated using a Covaris to generate ~200 bp fragments for immunoprecipitation as previously described55  . Mapped read counts per gene were reported by Subread featureCounts (version 1.5.0-p1) with parameters -s2 -Sfr -p57. Differentially expressed genes (DEGs) were identified by DESeq2 (v1.10.1) using filtering thresholds of FDR < 0.05 and fold change > 1.258. Normalized read counts are reported as transcripts per million (TPM). The gene models used for the RNA-seq analysis are taken from mm9 RefSeq annotations, as downloaded from the UCSC Genome Browser on September 9, 2016. Promoter motif analysis was done by HOMER with default setting. DEGs in DNL and FAO pathways were validated by qPCR or western blot and primers are listed in Supplementary Data RNA-sequencing was done by the NIEHS epigenomics core. Libraries were sequenced as PE-76mers on an Illumina NextSeq500. Raw read pairs were filtered to require a minimum average base quality score of 20. Filtered read pairs were mapped against the mm9 reference genome by STARLivers and adipose tissues were removed immediately after cervical dislocation and fixed in 10% buffered formalin. Paraffin sections (5.0 mm) were stained with hematoxylin and eosin (H&E) and analyzed using a microscope.t test, Mann–Whitney U test or Chi-squared test using GraphPad Prism7  and RStudio. The level of significance was set at p < 0.05 and indicated as follows: ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001. All data were expressed as means ± SEM.All statistical analyses were performed by two-tailed Student’s Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting Summary


Epigenetic marks do not change the sequence of DNA but affect gene expression in a cell-type specific manner by altering the activities of regulatory elements. Development of new molecular biology assays, sequencing technologies, and computational approaches enables us to profile the human epigenome in three-dimensional structure genome-wide. Here we describe various molecular biology techniques and bioinformatic tools that have been developed to measure the activities of regulatory elements and their chromatin interactions. Moreover, we list currently available three-dimensional epigenomic data sets that are generated in various human cell types and tissues to assist in the design and analysis of research projects. Nearly every cell in the human body has the same DNA. However, each cell has a distinct gene expression profile. The cell-type specific gene expression patterns come from differences in the epigenome Fig. a. The epChromatin states and interactions not only change among cell types but also change between inactive and active status when normal cells become diseased cells, and vice versa Fig. b. DysregThe development of molecular biology techniques coupled with next generation sequencing now enables us to map epigenomes genome-wide. For example, ChIP-seq (Chromatin immunoprecipitation sequencing), CUT & RUN (Cleavage under targets and release using nuclease) sequencing, and CUT & TAG (Cleavage under targets and tagmentation) sequencing are used to profile histone modification and TF enrichment. DNase-seq (Deoxyribonuclease I hypersensitive sites sequencing), MNase-seq , FAIRE-seq , ATAC-seq (Assay of Transpose Accessible Chromatin sequencing), and NOMe-seq (Nucleosome Occupancy and Methylome sequencing) are used to assess chromatin accessibility and nucleosome positioning. DNA methylation arrays and bisulfite sequencing are used to measure global DNA methylation levels. Chromatin interactions are mapped using 3C (chromatin conformation capture), 4C, 5C, Capture-C, Hi-C, DNase Hi-C, Micro-C, ChIA-PET  and HiChIP Fig. c.In this paper, we aim to introduce methods that are commonly used to map regulatory elements, chromatin accessibility, and chromatin interactions genome-wide. To facilitate researchers who are new in either molecular or computational biology, we describe wet lab as well as dry lab protocols for each of the methods. In particular, we detail chromatin conformation interaction methods and analysis tools, which are relatively new. We also discuss the advantages and limitations of each method and introduce recently developed single-cell based methods. Furthermore, we list currently available 3D epigenomic data sets that are generated in various human cell types and tissues. Introduction of epigenomic methods and resources described here will assist many researchers in the design and analysis of their research projects.The advancement of molecular biology techniques and next generation sequencing has led to the development of methods to identify regulatory elements throughout the entire genome by analyzing protein-DNA interaction, histone modification, chromatin accessibility, and DNA methylation. The enrichment of specific DNA-binding protein and histone modification is used to identify regulatory elements. Chromatin accessibility analysis reveals open and closed chromatin regions and nucleosome positioning. DNA methylation studies identify the location of methylated CpG sites, which is used to infer chromatin states of regulatory elements and their influences in gene expression . As diffChIP-seq  is one oChIP-seq bioinformatic pipeline includes (1) mapping of sequenced reads to the genome, (2) quality check (QC) of sequenced data sets, (3) calling peaks to identify TF binding sites or histone mark enriched regions, and (4) downstream analysis steps to characterize TF binding sites or identify regulatory elements. First, the sequenced reads  are aligned to the reference genome  a.k.a. hg38) using mapping software such as BWA  or BowtiAs the traditional ChIP-seq protocol uses sonication to fragment DNA, the resolution of data is not high. Therefore, ChIP-exo, which is a modified ChIP-seq method that uses exonuclease digestion after ChIP, has been developed . ChIP-exChromatin accessibility can be measured to identify active regulatory elements and nucleosome depleted regions (NDRs), where TFs bind Fig. b. CommonDNase-seq utilizes the Deoxyribonuclease I (DNase I) enzyme that digests accessible DNA regions. Therefore, DNase I hypersensitivity sites (DHS) identified by DNase-seq include open chromatin regulatory regions, where TFs bind . DNase-sMNase-seq determines chromatin accessibility with micrococcal nuclease (MNase) that preferentially digests nucleosome-free, protein-unbound DNA regions , 69. MNaFAIRE-seq is a method, which simply isolates NDRs from chromatin, not using an antibody to target histone mark or TF . FAIRE-sDNase-seq, MNase-seq, and FAIRE-seq require a relatively large number of cells and have high background noise level. Therefore, ATAC-seq was developed to supplement. ATAC-seq uses hyperactive Tn5 transposase that preferentially cuts accessible chromatin regions and simultaneously inserts adapters to the fragmented region . ATAC-sempG, GpCm in the human genome does not occur naturally in most cell types . U. U58]. Ull types –95. Therng steps . First, ng steps . Open ch element . Bioinfo element , BWA-MET element , Bismark element , BS-SEEK element , or Bisc element . Second, element , Samtool element , and Bam element  are used element  or Biscu element  programs element , and plo element . Unlike  element ; sonicatmpG, because proteins bound at open chromatin regions block the DNA methyltransferase (DNMT) complex, needed to methylate cytosine in the regions [Quantification of DNA methylation level in regulatory elements also helps us to understand the activities of regulatory elements Fig. c 4]. Ac. Ac4]. A regions . On the  regions . The mos regions , DNA met regions , and who regions  are used regions . For gen regions , 450,000 regions , and 850 regions  methylat regions . Similar regions , BSMAP [ regions , Bismark regions  and BS-S regions . Quality regions  and Samt regions , and met regions  and meth regions . Illumin regions , sesame  regions , and DMR regions .The processed sequencing data can be visualized in genome browsers like UCSC Genome Browser , IntegraRecently, advanced techniques using single cell sequencing have been developed to better understand heterogeneity of individual cells. For example, single-cell ATAC-seq, which improves the low input requirement of ATAC-seq further by capturing and assaying cells using a programmable microfluidics platform, has been developed . The spehttps://www.encodeproject.org/). From the phase III of the ENCODE project, a registry of 926,535 human and 339,815 mouse candidate regulatory elements is developed [http://www.roadmapepigenomics.org/). Unlike ENCODE, REMC only profiles the human epigenomes, and it does not produce TF ChIP-seq data sets including CTCF ChIP-seq data that mark insulators. The REMC database has searching tools and a matrix, which allows a user to search data sets based on experiment, cell-, and/or tissue-type. Moreover, it has options to visualize data sets in the UCSC genome browser.Large consortia such as ENCODE (Encyclopedia of DNA Elements)  and REMCeveloped . Data sehttps://www.cancer.gov/tcga). PsychENCODE has generated ChIP-seq, ATAC-seq, and DNA methylation data sets of more than 750 samples (http://www.psychencode.org/). Blueprint project includes histone modification ChIP-seq, DNase-seq and WGBS data sets (https://www.blueprint-epigenome.eu/), while TCGA mainly contains ATAC-seq and DNA methylation arrays to profile the epigenomes of tumors. Over 400 chromatin accessibility data sets and over 12,300 methylation data sets generated by TCGA are available in GDC data portal .There are additional consortia that profile the human epigenomes focusing on specific tissues or diseases. For example, PsychENCODE has profiled the epigenomes of brain cells and tissues obtained from patients who suffer from psychiatric diseases . On the https://www.ncbi.nlm.nih.gov/geo/). GEO is a public functional genomics database that archives and freely distributes numerous genomic data sets as part of the National Center for Biotechnology Information (NCBI) [https://www.ebi.ac.uk/ena/). Moreover, the International Human Epigenome Consortium (IHEC) coordinates the production of epigenomes from healthy and diseased human cells [http://ihec-epigenomes.org/). Currently, IHEC data portal archives thousands of epigenome data sets generated from ENCODE, REMC, Blueprint, Canadian Epigenetics, Environment and Health Research Consortium (CEEHRC), Japan Agency of Medical Research and Development & Core Research for Evolutional Science and Technology (AMED-CREST), Korean National Institute of Health (KINH), and Deutsches Epigenom Programm (DEEP). Individual research groups also maintain databases by collecting and processing epigenome data sets generated and reported by the research community. For example, Cistrome Data Browser [http://cistrome.org/) encompasses TF, histone ChIP-seq and chromatin accessibility data from GEO, ENCODE, and REMC. ReMap2020 database [http://remap.univ-amu.fr/) collects data sets specialized in transcriptional regulators of DNA-binding experiments in Homo sapiens and Arabidopsis thaliana.With the increased amount of epigenome data sets generated by researchers, multiple epigenome databases have been developed and maintained. The most commonly used database for regulatory elements data sets is GEO (Gene Expression Omnibus) (n (NCBI) . While Gn (NCBI)  allows sn (NCBI)  led by tan cells   based techniques are one of useful methods to study the chromatin interactions and the spatial organization of the human genome  identifies all possible interactions between a locus of interest with other DNA sequences  . 4C wet Carbon Copy Chromosome Conformation Capture (5C) detects interactions between all restriction fragments within given regions (many vs many) . 5C wet Unlike 3C, 4C, and 5C, Hi-C can map all possible chromatin interactions across the genome  , 157–159With the increasing popularity of Hi-C experiments, numerous Hi-C analysis bioinformatic tools have been recently developed. Hi-C bioinformatic pipelines include 1) matrix generation, (2) topologically associating domains (TAD) calling, (3) loop calling, and (4) reproducibility and differential analysis steps   combinesRecently, single-cell Hi-C is developed to analyze genome organization and variability in individual cells. The first single-cell Hi-C relied on physical separation of cells and resulted in low throughput . Howeverhttps://www.4dnucleome.org/). Currently, 4DN Data Portal encompasses hundreds of experimental data sets, including Hi-C, Micro-C and DNA FISH data. ENCODE [Because chromatin interaction method is a relatively new technique, there are currently few studies that have generated genome-wide chromatin contact maps in human cells. The 4D Nucleome (4DN) consortium  aims to . ENCODE  has also. ENCODE , 212, an. ENCODE , 213. ThThere have been striking improvements in both molecular and computational methods to analyze regulatory elements over the last decade. Chromatin immunoprecipitation, chromatin accessibility, and DNA methylation assays have annotated regulatory elements and revealed interactions between TFs and regulatory elements. Recently developed 3C-based methods have shown how these regulatory elements interact with each other genome-wide. Moreover, new methods enable further research of regulatory elements and their interactions in single cell and single molecule resolution. Although thousands of epigenomic data sets have been generated up until now, profiling of regulatory elements and chromatin structures in additional normal and diseased cells is in great demand, because 3D epigenetic signatures are distinct among cell types and cell populations. Further identification and characterization of regulatory elements that control transcription in a cell-type specific manner will enlighten novel molecular mechanisms of gene regulation and diseases.


Hydra belongs to the phylum Cnidaria where the first asymmetry in the body plan was observed and the oral-aboral axis originated. Wnt signaling has been shown to determine the head organizer function in the basal metazoan Hydra.Axis patterning during development is accompanied by large-scale gene expression changes. These are brought about by changes in the histone modifications leading to dynamic alterations in chromatin architecture. The cis regulatory elements and associated chromatin signatures, we ectopically activated the Wnt signaling pathway in Hydra and monitored the genome-wide alterations in key histone modifications. Motif analysis of putative intergenic enhancer elements from Hydra revealed the conservation of bilaterian cis regulatory elements that play critical roles in development. Differentially regulated enhancer elements were identified upon ectopic activation of Wnt signaling and found to regulate many head organizer specific genes. Enhancer activity of many of the identified cis regulatory elements was confirmed by luciferase reporter assay. Quantitative chromatin immunoprecipitation analysis upon activation of Wnt signaling further confirmed the enrichment of H3K27ac on the enhancer elements of Hv_Wnt5a, Hv_Wnt11 and head organizer genes Hv_Bra1, CnGsc and Hv_Pitx1. Additionally, perturbation of the putative H3K27me3 eraser activity using a specific inhibitor affected the ectopic activation of Wnt signaling indicating the importance of the dynamic changes in the H3K27 modifications towards regulation of the genes involved in the head organizer activity.To gain insights into the evolution of cis regulatory elements which harbor sites for key transcription factors involved in developmental processes. Differentially regulated enhancers exhibited motifs for many zinc-finger, T-box and ETS related TFs whose homologs have a head specific expression in Hydra and could be a part of the pioneer TF network in the patterning of the head. The ability to differentially modify the H3K27 residue is critical for the patterning of Hydra axis revealing a dynamic acetylation/methylation switch to regulate gene expression and chromatin architecture.The activation-associated histone marks H3K4me3, H3K27ac and H3K9ac mark chromatin in a similar manner as seen in bilaterians. We identified intergenic  Hox genes. These homeotic proteins ensure proper patterning and development of an organism and the presence of a cluster of Hox genes was thought to be a bilaterian invention [Hox genes in cnidarians and ctenophores suggested that the zootype hypothesis might not be true [Hox patterning system were identified and the Wnt/β-catenin signaling pathway was shown to play a critical role in regulating Hox gene expression. Across Metazoa, the Wnt/β-catenin signaling pathway is a part of the posterior patterning during development [Hox genes led to postulations that Wnt might be the major player in the primary axis formation [During the development of an organism, the morphology is determined through a series of patterning events discretizing the body via multiple axes such as the anterior/posterior, dorsal/ventral and left/right. The break in the symmetry of the animal Bauplan was the consequence of axis generation. Elegant studies in the past have shown that this is due to molecular polarization via distribution of morphogens into distinct territories creating localized patterning , 2. Highnvention  The iden be true –11. Furtelopment . Additioormation , 13.Hox genes do not display polarized localization across the axis [Hydra and other cnidarians, the canonical Wnt/β-catenin signaling pathway is known to form the head organizer and recent evidence suggests that it could play an initiating role in the formation of the foot as well [Hydra an ideal model system to study the process of axis patterning where the primary longitudinal axis is the oral-aboral axis. We have identified the downstream molecular players of the Wnt/β-catenin signaling pathway which resulted in the identification of multiple homeobox transcription factors that play a critical role in Hydra axis formation [The origin of a defined body axis in the animal kingdom occurred in the phylum Cnidaria that exhibit an oral-aboral axis. Studies in cnidarians and ctenophores have established that unlike in higher phyla, the axis , 14. In  as well –18. Thisormation .cis regulatory elements that include promoters and enhancers. While promoters are the more proximal regulatory regions and surround the transcription start sites (TSS) of gene bodies, enhancers are distal elements that are located from few Kb to 1 Mb away from the gene body [Regulation of gene expression is orchestrated by multiple short DNA sequences called ene body . In genoene body –23. The ene body . Enhanceene body . Additioene body , 27. Enhene body . EnhanceHydra the process of axis patterning is also recapitulated during the process of regeneration [Hydra which have been described previously in bilaterians. We describe the cis-regulatory elements that exhibit differential H3K27ac modification in response to systemic activation of Wnt signaling. We also show that the nearest neighboring genes of these DNA elements display head specific expression implying their role in head pattern formation. Further, using a specific pharmacological inhibitor of the JMJD3/UTX enzymes that regulate the demethylation of H3K27me3/2, we demonstrate their role in Wnt signaling mediated phenotypic transformation.Studying enhancers in the process of axis patterning is critical to understand the mechanisms through which the various signaling processes operate. In neration , 29. Durneration , 31. HerHydra, it is important to characterize the epigenomic landscape of the head organizer. Previous studies have characterized the histone repertoire in Hydra and have identified unique variants such as H2A.X.2 in the gastric region of polyps [Hydra adult polyps. Towards this goal, we performed chromatin immunoprecipitation followed by high throughout sequencing (ChIP-seq) for activation-associated histone modifications namely, H3K4me3, H3K27ac and H3K9ac. Correlation analysis of the ChIP-seq reads suggests characteristic genomic occupancy specific to each modification , a GSK3β inhibitor to induce systemic activation of the Wnt/β-catenin signaling pathway in the polyps. Following activation of the Wnt pathway, to decipher the epigenetic regulation of transcription, we performed ChIP-seq for H3K4me3, H3K27ac and H3K9ac in both treated and control animals. We then retrieved intergenic regions which show increased occupancy of H3K27ac upon activation of Wnt signaling  and the combined cluster3_4 (clusters 3 & 4) with head specific genes obtained by comparing publicly available RNA-Seq datasets of Hydra head and foot. Here we observed that there are 774 genes that are common between the head and cluster3_4 which correlates with the presence of a larger number of Wnt signaling pathway genes important for head patterning , the head organizer gene Hv_Bra1 and Hv_Pitx1 exhibit enrichment of H3K27ac . ALP treatment results in multiple molecular and structural changes in the polyp leading to the transformation of the entire body column into a head by giving rise to mis-localized tentacles [Hydra, we used a specific pharmacological inhibitor for KDM6A/6B and activated the Wnt/β-catenin signaling pathway using ALP. We observed that when the ALP treated polyps were subjected to the specific inhibitor GSK-J4 that acts against the histone lysine demethylases (KDMs) UTX/JMJD3 (KDM6A/KDM6B) [In entacles . As has A/KDM6B) , it resuA/KDM6B) , 43.Fig.cis regulatory elements—CREs) primarily due to their paramount contribution towards the differential regulation of gene regulatory networks (GRNs) comprising conserved gene repertoires in an organism/cell type-specific manner. It is fairly established that CREs play a critical role in the evolution of morphological and cell type diversity [The post-genomic era evidenced a shift in focus to non-coding regulatory sequences  leads to a rescue of the ectopic tentacle phenotype caused by the systemic activation of the Wnt/β-catenin signaling. This is a result of an altered ability of the H3K27 residue to be acetylated due to disruption in its ability to be demethylated, which transmutes the manifestation of the molecular and morphological changes in Hydra. We propose this H3K27 ac/me switch between the acetylated (ac) and the methylated (me) states as a critical player in the epigenetic regulation of the Wnt/β-catenin mediated axis patterning in Hydra  (Sigma–A4847) in hydra medium for 24 h. Control treatments with hydra medium and DMSO as a vehicle control were performed. Alsterpaullone was removed after the pulse and this time point is considered as 0 h post treatment. The polyps were incubated in hydra medium for an additional 72 h to observe the morphology of the polyps.Fifty polyps were used for each of the inhibitor treatments. The chemical inhibitor GSK-J4 (for KDM6A and KDM6B) was obtained from Sigma Aldrich (Cat. No. SML0701). GSK-J4 was used at a final concentration of 3 µM to perform all the experiments. The polyps were treated with the demethylase inhibitor for 24 h before the ALP treatmnetw as started. The demethylase inhibitor was changed every day and the treatment continued for 96 h while the ALP treatment was performed as previously mentioned. After 96 h, the animals were fixed in 4% PFA overnight at 4 °C following relaxation with 2% urethane. The polyps were stored in 50% glycerol and imaged. For whole mount in situ hybridization, treatment with GSK-J4 was performed 24 h before the ALP pulse and the polyps were fixed 24 h after the ALP pulse.Hydra polyps each for DMSO control and ALP treatment were cross-linked with 1% formaldehyde, lysed and sonicated in sonication buffer  to obtain an average chromatin size of 300 bp. Chromatin was pre-cleared using 50 μl of a 50% protein A sepharose  slurry for 1 h at 4 °C with gentle inverting. Immunoprecipitations were carried out in 1 ml of ChIP buffer  with Anti-H3K4me3, Anti-H3K27ac, Anti-H3K9ac and Anti-H3. An appropriate IgG control was also used with inverting at 4 °C for 14–16 h. The samples were then incubated with 50 μl of a 50% Protein A sepharose slurry (saturated with 0.5% BSA and 10 mg/ml yeast tRNA) for 3 h at 4 °C with gentle inverting. ChIP samples were reverse-crosslinked, and the DNA was purified using a Qiaquick column (Qiagen). Input chromatin was obtained after preclearing, by de-crosslinking and purifying input DNA using a Qiaquick column (Qiagen) according to manufacturer’s instructions. Purified DNA was subjected to library preparation for sequencing or used for quantitative PCR. The fold enrichment was calculated using the formula—Fold enrichment = 2(Ct (Target antibody)−Ct (IgG)). The details of the primers used to perform the ChIP RT-PCR are provided in Additional file Two thousand ChIP-seq was performed using the SOLiD 5500 platform (Applied Biosystems). ChIP-seq libraries were prepared from 5 ng of DNA extracted after chromatin immunoprecipitation for different histone modifications using fragment library construction kit from Life technologies. Here, end repairing of the ChIPed DNA was carried out and then adaptors were ligated using T4 DNA ligase. Adaptor ligated products were amplified using adaptor specific primers for 12 cycles. As per the manufacturer’s protocol DNA purification was performed using Agencourt XP beads (Beckman Coulter). Quality and quantity of the libraries were analyzed using Agilent bioanalyzer 2100 high-sensitivity DNA kit. The pooled libraries were used for sequencing.Hydra vulgaris reference genome from NCBI was downloaded (GCF_000004095.1_Hydra_RP_1.0) and used for alignment and other analysis [analysis . The genanalysis . Using Sanalysis  aligned analysis . Identifanalysis . Motif eanalysis . Followianalysis  was usedData from NCBI-SRA was downloaded and used for this analysis. Here, sequence reads were aligned to the reference genome using HISAT2 . AlignedRenilla luciferase as a transfection control. The cells were harvested 48 h post transfection and processed as per the given procedure using the Dual-Luciferase® Reporter Assay System (Promega). The luminescence was recorded for the target firefly luciferase and Renilla luciferase using EnSight™ Multimode Microplate Reader (PerkinElmer). The firefly luminescence values were normalised to the Renilla luminescence values and the fold change was calculated over the pGL3 basic empty vector normalised values.The selected intergenic regions were cloned into pGL3-Basic vector downstream of the luciferase ORF to eliminate chances of promoter-based activity. HEK293T cells were transfected with the target clones along with pRL-TK vector expressing Hv_Wnt3a and Hv_Bra1 were prepared by in vitro transcriptions from templates amplified from a recombinant pCR BluntII TOPO (Invitrogen) plasmids containing the Hv_Wnt3a and Hv_Bra1 cDNA using PCR. . Whole-mount in situ hybridization was performed on the polyps as described [Digoxigenin-labeled RNA probes for escribed  with theAdditional file 1. Additional figures.Additional file 2: Table S1. Bed file for locations of ChIP-Seq and RNA-Seq peaks for genic regions in Hydra.Additional file 3: Table S2. (a) ChIP-Seq peak annotation for H3K4me3 in whole Hydra polyps. (b) ChIP-Seq peak annotation for H3K27ac in whole Hydra polyps. (c) ChIP-Seq peak annotation for H3K9ac in whole Hydra polyps.Additional file 4: Table S3. BED file for H3K27ac ChIP-Seq peaks on intergenic regions.Additional file 5: Table S4. BED file for H3K27ac ChIP-Seq peaks on intergenic regions enriched after ALP treatment.Additional file 6: Table S5. (a) Annotated protein list for genes nearest to intergenic regions with H3K27ac enriched after ALP treatment in cluster1. (b) Annotated protein list for genes nearest to intergenic regions with H3K27ac enriched after ALP treatment in cluster2. (c) Annotated protein list for genes nearest to intergenic regions with H3K27ac enriched after ALP treatment in cluster3. (d) Annotated protein list for genes nearest to intergenic regions with H3K27ac enriched after ALP treatment in cluster4.Additional file 7: Table S6. (a) Gene lists for the four clusters in Fig 3c. (b) List of genes upregulated in the head. (c) List of genes downregulated in the head.Additional file 8: Table S7. Enrichment of PFAM terms in network analysis. Clustering of the genes identified in the STRING db and colour codes for the clusters generated.Additional file 9: Table S8. (a) Primer used for ChIP-qRT-PCR. (b) Relative luminiscence readings for the luciferase assay performed with the target intergenic regions  ddCt values of H3K27ac occupancy estimated using ChIP-RT qPCR  ddCt values of H3K27ac occupancy estimated using ChIP-RT qPCR (Source data for Fig.