In order to identify pure genomic transcriptional effects of 1,25(OH)2D3, we used VDR cistrome, transcriptome and open chromatin data, obtained from the human monocytic cell line THP-1, for a novel hierarchical analysis applying three bioinformatics approaches. We predicted 75.6% of all early 1,25(OH)2D3-responding (2.5 or 4 h) and 57.4% of the late differentially expressed genes (24 h) to be primary VDR target genes. VDR knockout led to a complete loss of 1,25(OH)2D3–induced genome-wide gene regulation. Thus, there was no indication of any VDR-independent non-genomic actions of 1,25(OH)2D3 modulating its transcriptional response. Among the predicted primary VDR target genes, 47 were coding for transcription factors and thus may mediate secondary 1,25(OH)2D3 responses. CEBPA and ETS1 ChIP-seq data and RNA-seq following CEBPA knockdown were used to validate the predicted regulation of secondary vitamin D target genes by both transcription factors. In conclusion, a directional network containing 47 partly novel primary VDR target transcription factors describes secondary responses in a highly complex vitamin D signaling cascade. The central transcription factor VDR is indispensable for all transcriptome-wide effects of the nuclear hormone.The transcription factor vitamin D receptor (VDR) is the high affinity nuclear target of the biologically active form of vitamin D The endocrine hormone 1,25(OH)2D3 binds with high affinity to the transcription factor vitamin D receptor (VDR), a member of the nuclear receptor superfamily1. Although particularly highly expressed in the small intestine and the kidney, VDR is also found in immune cells and many other organs, albeit at lower level2. This explains why VDR not only plays a role in the regulation of calcium and phosphorus homeostasis3, but also modulates other physiological functions, including innate and adaptive immune responses4. According to the canonical nuclear receptor signaling model, VDR binds as a heterodimer with the nuclear receptor retinoid X receptor (RXR) to response elements formed by a direct repeat of two hexameric sequence motifs spaced by three nucleotides (DR3)6. Numerous transcriptomic and epigenome-wide data obtained in human monocytic THP-1 cells were used to refine this model to better describe genomic vitamin D signaling involving possible modulation of the 3-dimensional chromatin structure as well as the action of pioneer factors like purine-rich box 1 (PU.1 or SPI1) and CCAAT/enhancer binding protein α (CEBPA)17.The lipophilic molecule 1α,25-dihydroxyvitamin D2D3 which are independent of transcriptional regulation. These effects appear to be cell type-specific and are mediated by kinases such as phospholipase A2 (PLA2), phospholipase C (PLC), protein kinase A (PKA), protein kinase C (PKC), Ca2+/calmodulin-dependent protein kinase (PKCaMII), mitogen-activated protein kinases (MAPK), Src tyrosine phosphorylation and phosphatidylinositol 3-kinase (PI3K)25. Reported physiological effects of non-genomic responses to 1,25(OH)2D3 comprise modulations of calcium uptake and secretion, intracellular pH, insulin secretion and cell migration, but their overall significance in vivo is unclear30. There is some controversy in the identification of the receptor(s) which convey 1,25(OH)2D3-induced non-genomic cellular function. Some reports state a completely VDR-independent propagation via a novel membrane receptor34. VDR has been found localized in membrane caveolae and to interact or synergize with the membrane receptor, Protein disulphide isomers family A member 3 39. It has also been reported that 1,25(OH)2D3 can induce non-genomic effects by binding to an alternative ligand binding pocket in the classical nuclear VDR29. Thus, there may be different parallel mechanisms for non-genomic actions of 1,25(OH)2D3, for some of which both VDR and PDIA3 are required43. It is generally agreed that non-genomic 1,25(OH)2D3 signaling facilitates its genomic regulation, for example by activating RXR or other transcription factors by phosphorylation44. In this study, we knocked down the VDR to determine whether any transcriptome-wide effects of 1,25(OH)2D3 can be observed in the absence of its nuclear receptor.In addition to the genomic actions, rapid non-genomic effects have also been described in response to 1,25(OH)45 and the TEPIC 2 framework for transcription factor binding site prediction46 to study primary and secondary effects of vitamin D.Further, we used the EpiRegio database of regulatory elements (REMs)47 is a well responding and physiologically meaningful model system for the investigation of 1,25(OH)2D3-triggered physiological processes, such as innate immunity and cellular growth50. The cells were grown in RPMI 1640 medium, supplemented with 10% fetal calf serum (FCS), 2 mM L-glutamine, 0.1 mg/ml streptomycin and 100 U/ml penicillin, and were kept at 37 °C in a humidified 95% air/5% CO2 incubator. Prior to mRNA or chromatin extraction, cells were grown overnight in phenol red-free medium supplemented with charcoal-stripped FCS and then treated with vehicle (0.1% ethanol (EtOH)) or 100 nM 1,25(OH)2D3 (Sigma-Aldrich).The human acute monocytic leukemia cell line THP-12 incubator.Lenti-X 293 T cells (Takara Bio) were grown in DMEM medium supplemented with 10% FCS, 0.1 mg/ml streptomycin and 100 U/ml penicillin and were kept at 37 °C in a humidified 95% air/5% COTHP-1 cells stably expressing type II CRISPR RNA-guided endonuclease Cas9 (Cas9), generated by lentiviral transduction with the plasmid lentiCas9-Blast  into THP-1 cells from DSMZ (ACC16), and the sequences for the non-targeting control guide RNAs (gRNAs) were kindly provided by Dr. Frank Schnuetgen .VDR gene have been designed using the Benchling life sciences R&D cloud51 and CHOPCHOP52. Sense and antisense oligonucleotides containing the 19 bp gRNA sequence plus the required overhangs matching the BsmBI-cut vector were ordered (Sigma-Aldrich) and annealed (Supplementary Table BsmBI (Esp3I) (Thermo Fisher) and T4 DNA ligase (Thermo Fisher) were used to clone the annealed gRNAs into the plasmid LentiGuide-Puro  with the Golden Gate assembly protocol53. Correct insertion of the gRNAs into the plasmid was verified by Sanger sequencing.Specific gRNAs targeting the Cell-free, lentiviral supernatants were produced by polyethylenimine (PEI)-based transient co-transfection of Lenti-X 293 T cells. Briefly, the pLentiGuide-Puro-gRNA vector, the lentiviral gag/pol packaging plasmid psPAX  and the envelope plasmid encoding the glycoprotein of vesicular stomatitis virus (VSV-G)  were transfected at a molar ratio of 3:1:1 by standard PEI transfection. 24 h and 48 h post transfection, two consecutive viral supernatants were harvested, cleared through 0.45-µm pore-size PVDF membrane filter (Millipore), combined and stored at − 80 °C.6 cells were pelleted and resuspended in 1 ml of the lentiviral particles. Polybrene (8 µg/ml) was added and the cells were centrifuged for 90 min with 1000 × g at room temperature. The supernatant was discarded, the cells resuspended in 1 ml full growth medium and incubated for 48–72 h. Then, the second spinoculation was carried out with 800 µl of lentivirus. After another 48–72 h incubation, puromycin (2 µg/ml) was added to select the transduced cells for 10–14 days.Prior to lentiviral transduction, the THP-1/Lenti-Cas9-Blast cells were re-selected with Blasticidine S hydrochloride (50 µg/ml) for 3 days. The transduction with the gRNA-expressing plasmids was performed in two consecutive spinoculations. Briefly, 0.2 × 10VDR knockout cells were re-selected with puromycin (2 µg/ml) for three days.Prior to each experiment for RNA extraction, the Cells were washed twice with phosphate-buffered saline (PBS) and lysed with Triton X-100 lysis buffer , 40 μg/ml phenylmethylsulfonyl fluoride). Cells were centrifuged for 10 min at full speed at 4 °C. The supernatant was transferred and after determination of protein concentration by the Bradford assay, equal amounts of protein were boiled in sample buffer and separated by SDS-PAGE (8%). The gels were blotted onto a nitrocellulose membrane and blocked in Rotiblock (Carl Roth). The anti-VDR antibody  and the β-actin antibody  were used in a 1:1000 dilution. The blots were developed with infrared-fluorescent-dye-conjugated secondary antibodies (LI-COR Biosciences) and detected with an Odyssey Classic system (LI-COR Biosciences).Total RNA was extracted using the Quick-RNA Miniprep Kit (Zymo Research) according to the manufacturer’s instructions. RNA quality was assessed on a Fragment Analyzer (Agilent). Four independent replicate experiments  were performed for RNA sequencing (RNA-seq).54. Differential gene expression was computed using DESeq2, which implements a negative binomial test over the reads in each two conditions 2D3-treated/solvent-treated or NTC control/VDR knockout), with standard parameters and an adjusted p-value cutoff of 0.0555. Information regarding which batch of THP-1 cells each sample originated from was provided to DESeq2 to correct for batch effects.RNA-seq libraries were prepared from 1 µg total RNA using the VAHTS Stranded mRNA-seq Library Prep Kit for Illumina V2  including poly(A) enrichment. Single end sequencing was conducted to 75 bp read length on the NextSeq500 platform using standard Illumina protocols resulting in roughly 20 M reads per library. The resulting raw reads were assessed for quality, adapter content and duplication rates with FastQC. Then, the high-quality reads were aligned to the reference genome (hg19) using Spliced Transcripts Alignment to a Reference (STAR) with the parameter –quantMode GeneCounts for gene-level quantification and otherwise default parameters.56 with some modifications. After treatment of 20 × 106 THP-1 cells with 100 nM 1,25(OH)2D3 for 24 h, nuclear proteins were cross-linked to genomic DNA by adding formaldehyde directly to the medium to a final concentration of 1% and incubating at room temperature for 10 min on a rocking platform. Cross-linking was stopped by adding glycine to a final concentration of 0.125 M and incubating at room temperature for 10 min on a rocking platform. The cells were collected by centrifugation and washed twice with ice cold PBS. The cell pellets were subsequently resuspended twice in 10 ml cell lysis buffer  and once in 10 ml nuclear lysis buffer . After two washes with cell lysis buffer, the chromatin pellet was resuspended in 700 µl of SDS lysis buffer  and the lysates were sonicated in a Bioruptor Plus (Diagenode) to result in DNA fragments of 200 to 500 bp. Cellular debris was removed by centrifugation. 340 µl aliquots of the lysate were diluted 1:5 in IP dilution buffer . 4 µg anti-ETS1 antibody , previously used by the ENCODE project (data available at Gene Expression Omnibus (GEO)57 at GSE32465) were coated to 60 µl Dynabeads Protein G (Invitrogen) in an overnight incubation at 4 °C. The pre-formed bead-antibody complexes were then washed twice with beads wash buffer  and added to the chromatin aliquots. The samples were incubated for overnight at 4 °C on a rotating wheel to form and collect the immuno-complexes. The beads were washed sequentially for 5 min on a rotating wheel with 1 ml of the following buffers, each: twice cell lysis buffer, once high salt buffer , once ChIP wash buffer  and twice 1 ml TE buffer . Then, the immune complexes were eluted using 250 µl ChIP elution buffer  at 37 °C for 30 min with rotation. The elution was repeated with a 10 min rotation and the supernatants were combined. The immune complexes were reverse cross-linked at 50 °C for 2 h in the presence of proteinase K (Fermentas) in a final concentration of 40 µg/ml. The genomic DNA was isolated with the ChIP DNA Clean&Concentrator Kit (Zymo Research). Two biological replicates have been prepared for the ETS1 sequencing (ChIP-seq).ChIP assays were performed as described by Zhang et al58 with the following essential command line arguments: macs2 callpeak –bw 150 –keep-dup 1 –g hs –qvalue = 0.01 –m 5 50 –bdg. Peaks are declared when enriched regions, compared with the corresponding loci in the input, are found. Otherwise, default parameters were used. ETS1 ChIP-seq raw data are available at GEO at GSE157209.2–10 ng of each ChIP DNA template was used for library preparation with the NEBNext Ultra II kit (New England Biolabs) and libraries were sequenced at 50 bp read length on a HiSeq2000 system using standard Illumina protocols at the Gene Core of the EMBL . ChIP-seq data was aligned with the human reference genome version hg19 using Bowtie software version 1.1.1 with the following essential command line arguments: bowtie -n 1 -m 1 -k 1 -e 70 –best. The aligned input and ETS1 reads were converted to sorted BAM format using samtools and, after merging the read sets per sample, converted to TDF format using igvtools, in order to allow efficient visualization in the Integrative Genomics Viewer (IGV) genome browser. Statistically significant ChIP-seq peaks were identified using Model-based Analysis of ChIP-Seq data (MACS) version 2.11. CEBPA ChIP-seq data can be accessed under GSE11955613 and H3K27ac ChIP-seq under GSE10785112. Formaldehyde-assisted isolation of regulatory elements sequencing (FAIRE-seq) data in THP-1 cells after 24 h of 100 nM 1,25(OH)2D3 treatment is available from the same source under the accessions GSE69297 and GSE69303, originally published by Seuter et al.15. RNA-seq time course data in THP-1 cells with 2.5 h, 4 h and 24 h of 100 nM 1,25(OH)2D3 treatment is accessible at the accession GSE6930315.VDR ChIP-seq data in THP-1 cells is publicly available at NCBI GEO under the accession GSE89431, originally published by Neme et al46. In order to make use of the VDR ChIP-seq data to identify direct VDR target genes, we used (i) TEPIC 2 to associate VDR ChIP-seq peaks to genes by considering a window of 50 kb centered at the transcription start site (TSS) of genes. Further, (ii) we overlapped VDR ChIP-seq peaks with annotated REMs of the EpiRegio database45. REMs in EpiRegio are linked to genes. Each gene that had at least one REM that was linked to it and overlapped a VDR ChIP-seq peak was considered a direct VDR target.TEPIC (version 2.2) is a universal software for the annotation of genes and integration of epigenomic, ChIP-seq and TF motif data2D3 treatment and looked for predicted VDR binding sites using position weight matrices of VDR or RXRA::VDR obtained from merged JASPAR 2020 59, HOCOMOCO 60 and Kellis-ENCODE61 motif sets included in TEPIC 2.2. Transcription factor binding affinities to regions of open chromatin are calculated using TRAP. Predicted VDR sites were assigned to genes if they were present in a window of 50 kb around the TSS of a gene. Only affinities meeting a threshold determined by user-supplied random sequences were used to assign transcription factors to genes. Calculated affinities are subject to exponential decay depending on the distance of the predicted transcription factor binding site from the TSS of the respective gene.Finally, (iii) we made use of FAIRE-seq data in THP-1 cells after 1,25(OH)2D3 treatment measured with RNA-seq.The direct target genes obtained by the three approaches above were intersected with sets of DEGs after 1,25(OH)2D3 treatment. Transcription factor binding sites were predicted using position specific energy matrices derived from JASPAR 202059, HOmo sapiens COmprehensive MOdel COllection (HOCOMOCO)60 and Kellis ENCODE61 motif databases, which are part of the TEPIC 2 repository.We used TEPIC 2.2 to annotate open-chromatin regions detected with FAIRE-seq in THP-1 cells after 24 h of 1,25(OH)2D3 treatment, which were not identified as direct VDR target genes. DYNAMITE used transcription factor-gene scores calculated by TEPIC 2 from the FAIRE-seq data before and after treatment for a set of 720 transcription factors. These scores were used as features for a sparse logistic regression classifier that predicts up- and down-regulated DEGs. The classifier learns a coefficient for each transcription factor included in the analysis, which describes the importance of the respective transcription factor to the modulation of gene expression in the dataset. We normalized the coefficients obtained from the classifier to the range .The DYNAMITE workflow of TEPIC 2, was used to learn the importance of individual transcription factors to gene expression changes of the subset of DEGs in THP-1 cells after 24 h of 100 nM 1,25(OH)igraph package in R (version 4.0.0). Edges were added between VDR and transcription factors predicted to be primary 1,25(OH)2D3 targets. Further edges were added between early-induced transcription factors and the rest of the induced transcription factors, if the early-induced transcription factor was predicted to bind at the gene locus (TSS + /- 25 kb) of the second transcription factor at an affinity greater than a given threshold by TEPIC 2. The network was plotted using the ggraph package in R.The transcription factor network was constructed using the 62. Significantly overrepresented pathways were those with a Benjamini–Hochberg adjusted P < 0.05.Pathway enrichment analysis was performed using the R (4.0.2) package ReactomePA (1.32.0)2D3 for 2.5, 4 or 24 h, the total number of DEGs had been reported by Neme et al.11 as 3650. Among these were 19 non-annotated transcripts, which were excluded for this study. Of the remaining 3631 annotated genes, 513 genes were differentially expressed after 2.5 or 4 h of stimulation and as such were classified as early 1,25(OH)2D3-responsive genes. No genes were transiently differentially expressed. Commonly, early responding genes are considered directly regulated by a transcription factor. Thus, in our previous studies in THP-1 cells, we have classified genes as primary vitamin D targets if they were differentially expressed at early time points of 1,25(OH)2D3 treatment, whereas those differentially expressed only at a late time point (24 h) were classified as secondary target genes63. According to this classification, the vast majority of all differentially expressed genes (DEGs) were secondary vitamin D target genes , while only 517 genes were early responding primary targets. However, since there are many VDR-bound regions (ChIP-seq) close to many of these so-called secondary vitamin D target genes, it seems likely that these genes can be regulated by VDR-bound enhancer elements. Hence, a better method to identify direct transcription factor targets was needed.In THP-1 cells treated with 1,25(OH)65. Thus, in the present study, we combined three different approaches to predict primary vitamin D target genes from 1,25(OH)2D3-responding genes  used REMs linked to genes from the EpiRegio database45. EpiRegio holds elements which have been identified by correlations between chromatin accessibility and gene expression at loci in a window of gene body ± 100 kb using data from the BLUEPRINT66 and the Roadmap consortia67. VDR ChIP-seq peaks overlapping with an REM from EpiRegio were linked to DEGs45. In the third approach (iii), the TEPIC 2 framework was used to search for VDR binding motifs obtained from diverse motif databases (see Methods) overlapping FAIRE-seq peaks that were located in a window of ± 25 kb around the TSS of 1,25(OH)2D3 DEGs. TEPIC 2 is a method that computes transcription factor gene scores based on position specific energy matrices of transcription factors and the signal of an open-chromatin assay46. By limiting the search space around genes to nearby regions of open chromatin, the prediction of contextually important transcription factor binding is more likely than using a continuous search space of the gene region without epigenetic information.However, linking regulatory regions to target genes is not easy and each method has its limitations2D3-responsive genes (75.6%) were predicted to be primary VDR targets by any of the three methods. The remaining 130 genes (24.4%) were classified as non-primary , heparin binding EGF like growth factor (HBEGF) and ninjurin 1 (NINJ1)69.According to the primary VDR target gene criteria set out above, 383 early-induced 1,25(OH)2D3 treatment, which were not differentially expressed at either 2.5 h or 4 h, and as such were classified as late 1,25(OH)2D3-responsive genes. Of these late-responding genes, 1,788 (57.4%) were classified as primary VDR targets by the aforementioned criteria, leaving 1,329 (42.6%) of the late-responding 1,25(OH)2D3-regulated genes as secondary vitamin D target genes Of the early induced vitamin D target genes, 61.1% (234 of 383) of the primary VDR target gene predictions have been confirmed by at least two of the three approaches, 79 genes (20.6%) by all three. 100 genes (26.1%) were assigned primary early VDR targets only based on window-based VDR binding  were predicted to be primary VDR target genes by all three approaches, and 42.4%  by at least two of the three methods. There were 368 genes (20.6%) assigned as primary VDR targets by the combination of transcription factor binding site prediction using TEPIC 2 in conjunction with 1,25(OH)2D3-stimulated FAIRE-seq data and a window based VDR peak to gene assignment and another 636 (35.6%) by the VDR association alone 2D3-responding and 57.4% of the late DEGs were predicted to be primary VDR target genes.Taken together, using three bioinformatics approaches including VDR ChIP-seq data, VDR motif search in open chromatin regions and REMs identified from multiple published genome-wide datasets, 75.6% of all early 1,25(OH)2D3-regulated gene expression. The knockout was achieved by lentiviral clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-mediated gene editing. Sufficient knockout on protein level was confirmed by Western blot for knockout lines generated with four different gRNAs 2D3 were 109 and 1,341, respectively 2D3 exposure 2D3. Three observations from the differential expression analysis between VDR knockout and control cells confirmed the complete dependence of 1,25(OH)2D3 regulation on the presence of VDR: (i) in ligand-treated cells similar numbers of genes were differentially expressed as after stimulation with 1,25(OH)2D3 in control cells 2D3-stimulated cells for both time points  demonstrates that for most genes the effect of 1,25(OH)2D3 on mRNA expression is not only reduced but completely lost upon VDR depletion  and potassium voltage-gated channel modifier subfamily F member 1 (KCNF1), the late primary VDR targets peroxisome proliferator activated receptor gamma (PPARG) and C-X-C motif chemokine ligand 8 ) and the late secondary target leukocyte receptor tyrosine kinase (LTK) 2D3–induced gene regulation in monocytic THP-1 cells, i.e., transcriptome-wide effects of vitamin D were entirely mediated by VDR.In summary, VDR knockout led to a complete loss of 1,25(OH)70. To globally identify the indirect mechanisms by which the 1,517 non-primary vitamin D target genes (41.8%) were regulated, we screened the primary VDR target genes for transcription factor genes and identified 47 among them. A subset of these genes were early vitamin D target genes, including the pioneer factors CEBPA and ETS proto-oncogene 1, transcription factor (ETS1) as well as BCL6 and NFE2. The remaining 34 transcription factor genes only showed upregulation after 24 h of 1,25(OH)2D3 treatment. Among these were genes such as nuclear factor I A (NFIA), the nuclear receptor PPARG and cut like homeobox 1 (CUX1) ) in regions of open chromatin as determined by VDR ChIP-seq and FAIRE-seq, respectively. This supported the prediction that these genes are direct VDR targets  were predicted to be primary VDR targets according to the criteria and methods used in this study. For the physiological impact of vitamin D signaling, secondary responses are as important as primary effects. Previously, the four transcription factors B-cell CLL/lymphoma 6 (BCL6), nuclear factor erythroid 2 (NFE2), E74-like ETS transcription factor 4 (ELF4) and POU class 4 homeobox 2 (POU4F2) have been shown to mediate secondary effects of vitamin D2D3 and being predicted to be primary VDR targets 47 were coding for transcription factors.Taken together, among the genes differentially expressed by 1,25(OH)2D3 treatment was characterized. Factors were prioritized that could explain secondary 1,25(OH)2D3-responsive genes, for which no direct VDR regulation could be established. For this purpose the DYNAMITE approach (see Methods) was used, in which motif based transcription factor binding prediction identifies meaningful transcription factors for a set of up- and down-regulated genes46. There were 22 transcription factors predicted to be of importance for the regulation of the secondary vitamin D target genes, amongst them CUX1, interferon regulatory factor 5 (IRF5), PPARG, NFE2 and ETS1 TS1 Fig. .Figure 42D3, the TEPIC 2 framework was used to predict which of the late (24 h only) 1,25(OH)2D3-responsive genes are bound by the 47 primary vitamin D target transcription factors  and bound more often to primary than to secondary vitamin D target genes , membrane spanning 4-domains A14 (MS4A14), membrane spanning 4-domains A4A (MS4A4) and LTK. The ChIP-seq data confirmed strong ETS1 binding to a region close to the TSS of COX7A2L located in open chromatin and marked with the enhancer specific H3K27ac histone modification. CEBPA co-located to the same region and bound more strongly to another region in the locus  was also confirmed by the CEBPA ChIP-seq data he late 2 h only 1he late 2 h only 1he late 2 h only 1he late 2 h only 1he late 2 h only 1In order to determine whether the subsets of CEBPA or ETS1 bound vitamin D target genes represent different physiological functions, a pathway enrichment analysis was performed. No pathways were significantly connected to the vitamin D targets associated by CEBPA 2D3 treatment, at the locus of the late 1,25(OH)2D3-induced IRF5 gene. VDR associated strongly close to the TSS of the gene, to an intergenic enhancer and to an enhancer approximately 25 kb upstream of the TSS. CEBPA and ETS1 bound to at least two enhancers each  and ETS1 and the predicted binding of VDR and ETS1 to the gene coding for the general transcription factor TATA-box binding protein (TBP) were confirmed by the ChIP-seq data CEBPA verified that the predicted binding of CEBPA correlated with functional effects13. Of the vitamin D target genes identified from the wildtype THP-1 RNA-seq, 1,256 have been predicted to bind CEBPA. More than half of these (734) were also differentially expressed by 1,25(OH)2D3 in control or CEBPA knockdown cells, or both 2D3 only in the absence of CEBPA. A rather high number of 448 vitamin D target genes were conserved vitamin D targets irrespective of the CEBPA silencing. However, as can be seen from the heatmap displaying the induction 2D3) of the top 50 of these genes, for most of them the CEBPA knockdown reduced the response to the VDR ligand  and GLIS family zinc finger 3 (GLIS3) 2D3 following CEBPA depletion. A directional network containing 47 primary VDR target transcription factors indicated a high complexity of vitamin D signaling and suggests novel factors.In summary, CEBPA and ETS1 ChIP-seq data validated the predicted association of these transcription factors to secondary vitamin D target genes. Approximately half of the vitamin D target genes predicted to bind CEBPA showed lost, gained or modulated response to 1,25(OH)2D3-regulated transcriptome in monocytic THP-1 cells to predict primary VDR target genes on the basis of RNA-seq, VDR ChIP-seq, FAIRE-seq, VDR motif analysis and the EpiRegio database of REMs. Our analysis indicated that the classification of 1,25(OH)2D3 DEGs into primary and secondary vitamin D target genes just by time points is an oversimplification. A high proportion (72.1%) of all early (2.5 or 4 h) 1,25(OH)2D3-regulated genes were predicted to be direct VDR targets, which may not seem surprising as it fits well with the time point-based classification. However, from the late-responding genes (24 h), 56.3% were assigned primary targets by our approach, which is a high ratio considering that these genes are usually considered secondary target genes due to their slow response. From the three approaches we applied, VDR ChIP-seq peaks were assigned to the highest numbers of vitamin D-responsive genes. Among them were many genes for which a VDR binding site was not predicted by TEPIC 2. This is likely due to imperfect motif models of the complex VDR binding profile, or the binding of VDR with co-factors resulting in the presence of VDR in regions where no canonical VDR binding motif is present. It is also in line with the observation that only 47.6% of the VDR cistrome carried a DR3-type VDR motif11.In this study, we used a hierarchical analysis of the 1,25(OH)2D3-responding genes can be a secondary target gene and thus our predictions have been too stringent. Inducible transcription factors lead to bursts of transcription and the bursting period commonly lasts several hours during which several transcriptional events take place75. For a secondary response, after the several hours of primary transcriptional activation the transcription factor has to be translated and then may induce a second transcriptional activation on its own. Thus, it is very unlikely that secondary responses can be detected in the time frame of 4 h. However, no matter which stringency we apply, a major outcome of this study is that a significant number of all DEGs that are unique to the 24 h time point of 1,25(OH)2D3 stimulation are primary VDR target genes. There is some inaccuracy in all RNA-seq data, which we have observed for example for the CXCL8 (IL8) gene. According to both RNA-seq datasets used in this study, the gene is not significantly regulated after 2.5 or 4 h stimulation with 1,25(OH)2D3 and was therefore classified as late response gene. However, in a previous report the gene has been shown to be significantly upregulated by 1,25(OH)2D3 in THP-1 cells already after 1 h by RT-qPCR76. If the same were true for many of the late responding genes, the discordance between primary target gene predictions and time point-based classification would be reduced. A gene may be directly regulated by VDR, but mRNA accumulation may be low if either the transcriptional burst is small or if mRNA stability is low. In such cases, the detection by RNA-seq may only be possible with very high sequencing depth. Assuming that not only technical reasons prevented the detection of weak mRNA levels accurately, our data indicate that for some primary vitamin D target genes the transcriptional response is delayed. This could be due to differences in co-activator recruitment or co-repressor disposal or to lower local chromatin accessibility. Also, the number and position of VDR sites in the gene locus may play a role. It has to be noted, though, that there is a rather big gap between the 4 h and the 24 h time-point and some genes may already be significantly regulated after 6 h or 8 h and thus may not be that late-responding after all. In contrast to the TEPIC 2-based approaches, the REMs in the EpiRegio database have been determined in a window of + /100 kb around and including the gene body. In spite of that less stringent window size, there are rather few primary target gene predictions made by this approach that have not been confirmed by at least one of the other two approaches. The REMs have been determined from large numbers of genome-wide datasets, which highly increased the statistical power of this method and rendered it more quantitative.Increasing the stringency by classifying only those DEGs as primary, which have been predicted by at least two of the three prediction methods reduced the ratio of primary target genes to 45.6% and 24.4% for early and late responding genes, respectively. Thus, this would indicate that 54.4% of the early regulated genes might in fact be secondary vitamin D target genes. The stringency of the prediction methods greatly depends on the window sizes that have been applied. For the FAIRE-seq motif and ChIP-seq approaches performed in TEPIC 2, we used a window of ± 25 kb around TSSs of the genes. Whilst we were aware that enhancers could be more distal from target genes than the threshold suggested by that window size, an increased window size would have led to many more false positive primary target gene predictions. On the other hand, it could be argued that none of the early 1,25(OH)VDR gene completely abolished 1,25(OH)2D3-induced transcriptomic changes as measured by RNA-seq. This on the one hand validated the identified vitamin D target genes, which could no longer be regulated by 1,25(OH)2D3 if its nuclear receptor VDR was not present to act as a ligand-activated transcription factor. It also indicated that even weakly up- or downregulated genes are indeed true vitamin D target genes and presumably their regulation plays a biological role. On the other hand, in light of reports about non-genomic effects of 1,25(OH)2D3 which have been described to also alter the activity of transcription factors like RXR and thus crosstalk with transcriptional regulation by its heterodimerization partner VDR44, it is surprising that there are no genes differentially expressed by 1,25(OH)2D3 in the VDR knockout cells. This contradicts reports about such rapid 1,25(OH)2D3 actions being independently of VDR mediated by an alternative receptor as PDIA339 and indicates that in the monocytic THP-1 cells such rapid effects of 1,25(OH)2D3 either involve the classical VDR by binding to its alternative binding pocket29, or are not present at all.Knockout of the 2D3 and which were predicted to be responsible for secondary effects of the active form of vitamin D. Each of these transcription factors was predicted to bind at many vitamin D-responsive genes, with some linked to thousands of DEGs. Among these were primary and secondary target genes, some of these being transcription factors as well. Using ChIP-seq data for the pioneer factors ETS1 and CEBPA, we could validate some of the predicted associations to vitamin D target gene loci. The myeloid lineage-determining transcription factor CEBPA is known to co-locate with VDR at more than 5,000 regions and, as an additional layer of complexity, at ≈1,500 sites CEBPA binding was modulated by the VDR ligand13. CEBPA silencing affected both the basal expression levels and the induction by 1,25(OH)2D3 of more than half of all strongly regulated vitamin D target genes. The latter represents both the pioneering function of the transcription factor, where CEBPA co-locates with VDR at primary VDR target genes, and the regulation of genes by CEBPA that are no direct VDR targets. This regulation was increased by the 1,25(OH)2D3-induced upregulation of CEBPA expression and by increased binding of CEBPA to some of its binding sites. We could correlate the predicted binding of CEBPA to a few hundred vitamin D target genes to functional changes by the CEBPA knockdown. This confirmed about a quarter (based on significant changes of the vitamin D response) to half (including non-significant changes) of our predictions for this transcription factor. There are additionally more than 1,000 genes that either lost or gained response to 1,25(OH)2D3 when depleting CEBPA, but were not predicted to bind CEBPA. These genes may represent secondary CEBPA target genes, or they were not predicted as CEBPA-bound due to imperfect CEBPA motif models or indirect DNA-association of CEBPA in a complex with other transcription factors. Previously, VDR and CEBPA have been shown to be tightly co-expressed in a module that is up-regulated in monocytes compared to cells from other hematopoietic lineages77. According to that study many transcription factors can be assembled into densely interconnected transcriptional circuits, which can provide a mechanism for robust gene regulation. Our results are inline with these findings and may be an explanation why monocytic cells are much more responsive to 1,25(OH)2D3 than some epithelial cell-types even though VDR expression is similarly high in both. Most probably other modules are highly expressed in these epithelial cells and if certain co-activators, chromatin-modifiers or vitamin D-regulated transcription factors are not in the expressed modules together with VDR the responsiveness to the VDR ligand will be lower. We presented a VDR-centered directional network of 47 VDR targeted transcription factors to describe the complexity of global vitamin D signaling in THP-1 cells. Most probably this network is cell-type specific and will contain partly other transcription factors in other cells. The often very strong increase in mRNA expression levels of primary vitamin D target genes from short to long time-points of ligand stimulation can partly be explained by such additional secondary 1,25(OH)2D3 responses.Another major result of this study is that there are 47 transcription factors whose expression was regulated by 1,25(OH)2D3 actions and to mechanistically characterize secondary effects of the VDR ligand in THP-1 cells. We predicted a high proportion of late-responding DEGs, that commonly are considered secondary target genes, to be in fact primary VDR targets. The remaining secondary VDR target genes appear to be regulated by a network of 47 transcription factors whose expression is directly regulated by 1,25(OH)2D3. This network is VDR-centered as we could show that all transcriptome-wide effects of the VDR ligand depended strictly on the presence of VDR. Thus, any non-genomic effects of 1,25(OH)2D3 that may modulate its transcriptional function, have to be mediated by VDR as well. The main findings from this study refined our previously published model of vitamin D signaling78 of which an adapted version is depicted in Supplementary Figure In summary, we applied a novel hierarchical approach to distinguish primary from secondary 1,25(OH)Supplementary files.Supplementary Table 2Supplementary Table 3Supplementary Table 4


Following changing cellular signals, various genes adjust their activities and initiate transcripts with the right rates. The precision of such a transcriptional response has a fundamental role in the survival and development of lives. Quite unexpectedly, gene transcription has been uncovered to occur in sporadic bursts, rather than in a continuous manner. This has raised a provoking issue of how the bursting transmits regulatory signals, and it remains controversial whether the burst size, frequency, or both, take the role of signal transmission. Here, this study showed that only the burst frequency was subject to modulation by activators that carry the regulatory signals. A higher activator concentration led to a larger frequency, whereas the size remains unchanged. When very high, the burst cluster emerged, which may be mistaken as a large burst. This work thus supports the conclusion that transcription regulation is in a “digital” way.Gene transcription has been uncovered to occur in sporadic bursts. However, due to technical difficulties in differentiating individual transcription initiation events, it remains debated as to whether the burst size, frequency, or both are subject to modulation by transcriptional activators. Here, to bypass technical constraints, we addressed this issue by introducing two independent theoretical methods including analytical research based on the classic two-model and information entropy research based on the architecture of transcription apparatus. Both methods connect the signaling mechanism of transcriptional bursting to the characteristics of transcriptional uncertainty . By comparing the theoretical predictions with abundant experimental data collected from published papers, the results exclusively support frequency modulation. To further validate this conclusion, we showed that the data that appeared to support size modulation essentially supported frequency modulation taking into account the existence of burst clusters. This work provides a unified scheme that reconciles the debate on burst signaling. In individual eukaryotic cells, transcription universally occurs in episodic bursts ,10,11,12To characterize transcriptional bursting, the bursting dynamics can be usually depicted by three parameters including (1) burst duration, the persistence time of the active period; (2) magnitude, the number of transcripts initiated during an active period, and (3) frequency, the number of bursts that occur per unit time. The burst duration and magnitude are collectively termed as burst size ,7,8,20. The controversy could be solved if there were no technical resolution constraints. Direct recording of transcriptional bursting is realized by tracing the intensity evolution of the fluorescence that labels nascent transcripts, mature RNAs, or proteins ,27,28,29Ideally, the technical resolution constraint would also be overcome if one could observe how the whole transcription apparatus including the activators, the general transcription factors such as transcription factor IIA (TFIIA), the Mediator, and RNA polymerase II (Pol II) dynamically operate and initiate transcripts at a promoter. However, this remains a challenge far beyond current technologies due to difficulties such as in labeling a huge number of unstable protein complexes ,35.Thus, this paper sought to bypass the technical difficulties. For a population of identical cells in homeostasis, transcriptional bursting leads to differences in the number of the gene’s mRNAs or proteins. In turn, the differences reflect the characteristics of transcriptional bursting. Such differences are depicted by the standard deviations from the average. Of note, the measured standard deviation is also a manifestation of other factors such as extrinsic noise and measurement itself. We thus collected those data that mainly reflected intrinsic transcriptional uncertainty rather than extrinsic noise or methodology. As far as we could, we found the data of seven different genes in published papers ,32,33. TWe wondered whether the common characteristic of the standard deviation data were capable of inferring the bursting modulation mode. Thus, we introduced two independent theoretical methods. One was an analytical study based on the classic two-state model , and the other was an information entropy study based on the general architecture of eukaryotic transcription apparatus. By comparing the experimental data with that predicted by the two methods, the results exclusively support frequency modulation. Moreover, the results unify the other views that appear to be irrelevant. We further demonstrate that the data supporting size modulation actually support frequency modulation, while being aware of the existence of burst clusters.All the experimental data used were from published papers. As far as we could find, the standard deviation data as functions of activator level or average transcriptional level for seven eukaryotic genes are available ,32,33. AThe two-state model is rather simple and analytically tractable. It assumes that gene promoter switches between alternative “ON” and “OFF” states. It has four parameters including activation rate  of mRNA ,37,38. α follows :(1)M=εδαThe standard deviation The Fano factor Both For magnitude modulation, ion of M A. This iecreases A. That io factor B. F, cortuations ,39,40,41In summary, the two-state model experimentally predicts consistent properties of the intrinsic noise, given that transcriptional regulation is achieved via frequency modulation rather than size modulation. Nevertheless, the disadvantages of this investigation are obvious. The two-state model is somehow too simple and the standard deviation curve is not well reproduced. Additionally, it remains to be inferred how the burst frequency encodes the activator level, as this is beyond the two-state model.The two-state model is too simplistic for an in-depth investigation. We thus introduced the information entropy theory, which naturally characterized the uncertainty of signaling without introducing additional parameters, to study the average uncertainty inherent in transcription regulation. The information entropy obtained .During transcriptional progression, transcriptional activators cyclically bind to and depart from the enhancers ,44,45,46The general transcriptional factors (GTFs) and RNA polymerase II (Pol II) assemble to the core promoter, leading to the formation of the preinitiation complex (PIC) ,51,52. AThe enhancer-bound activator stimulates transcription initiation via the Mediator complex ,53. In tThe degree to which the transcription apparatus disassembles and the chromatin structure recovers, before the reconstruction of the transcription apparatus, determines the time interval between transcriptional bursts. In other words, transcriptional bursts are separated by non-transcription intervals affected by many factors such as the stability of SCF and the competition of nucleosomes ,5.Based on the general mechanism of transcriptional regulation related above  to (5), it yields,Equation (9) represents a general model that describes the probability a transcription initiation event will occur. Then, the information entropy of transcription regulation follows:ble role C. Recallby chaos . With thproduced D.The successful reproduction sources from two points, which relate to the signaling mechanism of transcription regulation. One is the factor The signaling mechanism can thus be comprehended in two equivalent ways. The first is, referring to Equations (8) and (9), frequency modulation. The activator level changes burst frequency rather than size. The second is, referring to Equations (6) and (7), the time fraction that the enhancer is occupied by the activators that act to direct transcription initiation. This is essentially the same as the views that explain transcription regulation without the use of “frequency” ,42.In conclusion, information entropy analyses connect the architecture of transcription apparatus to transcriptional uncertainty. This suggests that transcription regulation is via burst frequency, which satisfies Equation (9). It also suggests that “the time fraction of enhancer occupation by activators that direct transcription initiation” is an equivalent expression of “frequency modulation”.Both investigations above suggest that an increase in activator level leads to an increase in burst frequency, with the burst size maintaining unchanged and the transcriptional level elevated. In the following, we further tested this conclusion with the data that appeared to support size modulation. A quantitative analysis on 8000 individual human genomic loci suggested that both burst size and frequency are altered by transcriptional activators . At low Indeed, frequency modulation explains and reconciles such findings. According to our conclusions, at low transcriptional levels, the probability of a transcription initiation event to occur is low and it is thus easy to differentiate individual bursts. This explains the observation that the burst frequency increases while the burst size remains unchanged. At high transcriptional levels, as the probability of a transcription initiation event to occur becomes high, the intervals between bursts shrinks. Dense bursts means the individuals are no longer easy to differentiate. Instead, due to the resolution constraint, a cluster of bursts is likely to be taken as a single burst, and consequently, the recorded burst frequency decreases. That is, our conclusions explain the conflict.The explanation above can be quantitatively tested. For a gene that expresses stably, the average number  effects ,33,58. Tfs=N/k−b A–C. ResuIn summary, with awareness of the technical resolution constraint in discriminating individual bursts with a burst cluster, the data appear to support size modulation intrinsically and perfectly support frequency modulation.In response to signals from the cellular cascade pathways, the nuclear abundance of transcriptional activators changes, followed by changes in the transcriptional activities of related genes. Given the discovery that transcription generally occurs in discontinuous bursts, we questioned how the activators modulate bursting dynamics and how the bursting transmits information. In principle, both burst size and frequency are possible regulatory targets, and indeed, there appeared to be evidence for both scenarios ,21,22,23Experimentally, it is hard to ascertain whether the burst size, frequency, or both are subject to modulation. This is due to technical difficulties in differentiating individual transcription initiation events. While manipulating with a time resolved fluorescence intensity curve that represents the dynamics of transcriptional bursting, it is inevitable to define artificial detection thresholds in quantifying burst size and magnitude ,28,29,30Noting that transcriptional uncertainty is a manifestation of transcriptional dynamics, we thus collected those data that reflected intrinsic transcriptional uncertainty rather than extrinsic noise or methodology. The common characteristics of transcriptional uncertainty imply that the bursting dynamics are likely modulated by the same way. This inspired us to investigate the burst modulation with the use of two theoretical methods that independently explored such uncertainty data from the cell population measurement. Such a strategy takes advantage of bypassing the technical resolution constraints.Both the theoretical methods are classic. One is the master equation modeling of the two-state model . The twoThrough comparing the results by theoretical investigations with experimental data, we showed that transcriptional regulation is achieved via modulating burst frequency. Investigations based on the two-state model have shown that cellular signaling benefit from low noise if transcriptional regulation is by modulating burst frequency rather than size, and that only frequency modulation presents the correct intrinsic noise of gene expression. Based on the known molecular mechanism of transcription, the information entropy of transcriptional regulation reproduces experimental data from different promoters, suggesting that burst frequency encodes regulatory information obeying a probability function. This also suggests that “frequency modulation” and “time fraction of functional enhancer occupation” are equivalent expressions of transcriptional regulation. We further demonstrate that, taking into account the existence of burst clusters that can be mistaken as individual bursts of large size, the data supporting size modulation intrinsically support frequency modulation. This work thus provides a unified scheme that reconciles the burst debate, independent of technical resolution constraint.In summary, transcription signals in a “digital” way, supported by two independent theoretical investigations that bypass technical resolution constraints. A higher activator level is followed by a larger burst frequency, whereas the burst size remains unchanged. The increase in burst frequency is due to the increase in the time fraction of enhancer occupation by activators, whereas the observed burst size increase is actually a manifestation of overlapping bursts. This work thus provides a unified scheme that reconciles the debate on burst signaling.


IGF2/H19 and CDKN1C/KCNQ1OT1 domains fold in complex chromatin conformations, that facilitate the control of IGs mediated by distant enhancers. In patient-derived cell lines, we observed a profound impairment of such a chromatin architecture. Specifically, we identified a cross-talk between IGF2/H19 and CDKN1C/KCNQ1OT1 domains, consisting in in cis, monoallelic interactions, that are present in healthy cells but lost in patient cell lines: an inter-domain association that sees ICR2 move close to IGF2 on one allele, and to H19 on the other. Moreover, an intra-domain association within the CDKN1C/KCNQ1OT1 locus seems to be crucial for maintaining the 3D organization of the region.Beckwith-Wiedemann syndrome (BWS) and Silver-Russell syndrome (SRS) are imprinting-related disorders associated with genetic/epigenetic alterations of the 11p15.5 region, which harbours two clusters of imprinted genes (IGs). 11p15.5 IGs are regulated by the methylation status of imprinting control regions ICR1 and ICR2. 3D chromatin structure is thought to play a pivotal role in gene expression control; however, chromatin architecture models are still poorly defined in most cases, particularly for IGs. Our study aimed at elucidating 11p15.5 3D structure, via 3C and 3D FISH analyses of cell lines derived from healthy, BWS or SRS children. We found that, in healthy cells,  Various perturbations of epigenetic programming are associated with human diseases, including imprinting disorders2.Genomic imprinting is a finely tuned epigenetic process fundamental for mammalian development, whereby, through specific patterns of DNA methylation and chromatin modification, only one copy of an imprinted gene (IG) is expressed, according to its parental originIGF2/H19 and CDKN1C/KCNQ1OT1, two clusters of IGs that are regulated by the methylation status of two imprinting control regions, ICR1 and ICR2, respectively3 . In normal individuals, the paternally derived ICR1 allele is methylated, while the maternal allele is unmethylated; at ICR2, the opposite methylation pattern occurs. IGF2 and KCNQ1OT1 are expressed by the paternal allele, whereas H19 and CDKN1C are expressed by the maternal allele4  and Silver-Russell (SRS OMIM #180860) syndromes are rare imprinting disorders that exhibit opposite growth abnormalities. The syndromes are caused by genetic and/or epigenetic alterations of the chromosome 11p15.5 region and show genetic heterogeneity ; hypermethylation at ICR1 (5% of cases)    and/or defective expression of IGs from the maternal chromosome (H19 and CDKN1C). In SRS, by contrast, they cause an excess of H19 expressed from the maternal chromosome and defective expression of IGF2 from the paternal allele5. Importantly, in the majority of cases of BWS and SRS, the molecular defect is a mosaic condition; that is, it is present only in a fraction of cells3.In BWS, these molecular alterations trigger over-expression of paternal chromosome IGs 11, in which chromosomes are partitioned at the sub-megabase scale15. The most important TAD architectural proteins are CTCF (CCCTC-binding factor) and cohesins18.In eukaryotes, 3D chromatin organisation has various functions in numerous aspects of genome regulation including maintenance of genome stability, chromosome transmission, DNA replication, and gene expression. Indeed, transcriptional regulation is strongly affected by chromatin folding, where looping interactions facilitate the long-range control mediated by distant regulatory elements, such as enhancersIGF2/H19 locus differs between maternal and paternal alleles, and these parent-specific structures are required for correct expression of the IGs within this domain.Chromatin structure at the human IGF2/H19 domain contains binding sites for several trans-acting factors such as ZFP57, involved in the establishment and maintenance of DNA methylation in imprinting control centres, OCT4 and SOX2, participating in maintaining hypomethylation of the maternal allele20. Moreover, the IGF2/H19 locus harbours a number of CTCF-binding site clusters that function cooperatively to form chromatin loops. These structures bring the enhancer into spatial proximity with its target promoter21. In particular, the unmethylated ICR1 of the maternal allele allows CTCF binding and prevents the IGF2 gene from accessing enhancer downstream of H19. By contrast, the methylated ICR1 of the paternal allele does not bind CTCF, enabling the IGF2 promoter and the enhancer region to interact23. The effects of abnormal methylation at ICR1 on the underlying chromatin and long-range associations with neighbouring CTCF sites are poorly understood25; however, Nativio and collaborators24 proposed that, in ICR1-related syndromes, a switch from the maternal to paternal conformation may occur in BWS and vice versa in SRS. No comprehensive description of 3D chromatin conformation at the CDKN1C/KCNQ1OT1 locus has been reported to date.The IGF2/H19 and CDKN1C/KCNQ1OT1 regions characterise both imprinting disorders. Interestingly, we identified a cross-talk between the IGF2/H19 and CDKN1C/KCNQ1OT1 domains, based on a higher order of chromatin fold comprising the entire 11p15.5 imprinted region, resulting in complex 3D structural deregulation in the BWS and SRS cells.In this study, we investigated the 3D chromatin organisation of the 11p15.5 imprinted region in cells from healthy individuals and from patients with BWS and SRS, and found that profound alterations in the chromatin architecture of the cis regulatory elements, including enhancers and CTCF-binding sites. To achieve this aim, we have chosen the chromatin conformation capture (3C) approach since this method is appropriate to investigate the structure of a defined genomic region. Furthermore, to visualise the chromatin interactions, 3D FISH (Fluorescence In Situ Hybridization) using probe pairs across the region interrogated by 3C was applied.We sought to investigate in depth the chromatin structure of chromosome region 11p15.5 and its potential deregulation in the related imprinting diseases (BWS and SRS), focusing particularly on IGF2/H19 and CDKN1C/KCNQ1OT1 regions subjected to 3C analysis is depicted Fig. https://genome.ucsc.edu) and harbours two IG clusters: IGF2/H19 and CDKN1C/KCNQ1OT1 15 derived from the lymphoblastoid cell line GM12878, revealed that both IGF2/H19 and CDKN1C/KCNQ1OT1 domains localised in the same TAD , one downstream of H19 (CTCF Down), one in the centrally conserved domain (CCD) region, and one in ICR1 , one at the 3′ of KCNQ1 (CTCF2), one close to ICR2 and two in the KCNQ1 gene (CTCF3 and 4)  Fig. .cis-acting regulatory elements, such as enhancers, we searched for enhancer candidate regions, matching data published in the literature and the publicly available chromatin segmentation data from GM12878 cells, obtained via the Roadmap Epigenomics Project . For the IGF2/H19 region, many studies suggest the involvement of shared enhancers regulating both IGF2 and H19 expression. In particular, two different regions that can act as enhancers have been identified , upstream of ICR2 (Enh 2), and in the middle of KCNQ1OT1 (Enh 3)  Fig. .32. Using 3C assay, we studied physical contacts between genes and regulatory elements at the human IGF2/H19 and CDKN1C/KCNQ1OT1 loci.At imprinted regions, chromatin can differ between the maternal and paternal alleles, and looping structures, mediated by regional CTCF-binding sites, are required for the correct expression of IGsIGF2/H19 and CDKN1C/KCNQ1OT1 domains  generated from healthy children, showing normal methylation levels at both ICR1 and ICR2, by pyrosequencing analysis33 .To identify the main interactions within these ines CTRL and 2 geOur results represent the superimposition of all frequent conformation states of these regions; moreover, 3C analysis revealed the sum of data from the two parental alleles.IGF2/H19 region, we used four anchors: CTCF Up, ICR1, Enh A, and CTCF Dw , Enh A (5′), and CTCF Up (3′)  Fig. .24, our results confirm interactions that bring Enh A into spatial proximity with the H19 and IGF2 promoters. In addition, our data indicate that, also the Enh B, being close to the Enh A, is dragged near to the same promoters, suggesting a possible role of this enhancer in the IGs regulation.Consistent with previous studiesIGF2/H19 locus, models of parental allele-specific chromatin conformations were previously described. These reports allocated the associations CTCF Up-Enh A and CTCF Up-CTCF Dw to the paternal allele, and ICR1-CTCF Dw to the maternal allele. The interactions between CTCF Up-CCD and ICR1-CTCF Up were characterised as biallelic25. We could verify the allele specificity of the ICR1-CTCF Up and ICR1-Enh A associations, combining the 3C assay with SNPs analysis of the ligated products. This approach allows, in heterozygous samples, to distinguish whether the interactions involve one or both alleles. The analysis of the 3C products obtained from CTRL1 cell line, heterozygous for two SNPs (rs59121562 T/A and rs80047492 T/A) in the ICR1 restriction fragment, revealed that both interactions were predominantly monoallelic   Fig. . The 3C KCNQ1 gene. Both CTCF1 and ICR2 interacted with CTCF2–4, Enh 1, and Enh 3. Also in this domain, we found that the 5′ and 3′ regions of the IGs  associated with the CTCF-binding sites, CTCF1 and ICR2 .We observed strong interactions between ICR2 and the CTCF-binding sites (CTCF1–4) within the domain. ICR2 also associated with two enhancers (Enh 2 and 3) within the CR2 Fig. . These dKCNQ1OT1, Enh 2, and Enh 3; Fig. To assign allele specificity to the main ICR2 interactions, we performed 3C-SNP analysis in the control CTRL2 cell line, which resulted heterozygous at two SNPs (rs2283197 G/C and rs2283196 T/G) in the ICR2 restriction fragment Fig. . We founIGF2/H19 locus, we overlaid our 3C looping profiles with the virtual 4C plots of the CDKN1C/KCNQ1OT1 locus and confirmed the main 3C interactions ; BWS-ICR1 cell line with ICR1 hypermethylation ; BWS-ICR2 cell line with ICR2 hypomethylation ; BWS-UPD with methylation defects in both ICR1 and ICR2 due to paternal UPD 11p15.5  , together with a loss of contact between IGF2 and CTCF Dw  increase in the BWS-ICR1 cell line  revealed opposite variations in the ain Fig. . In BWS-GF2 Fig. . By contH19 regions and 3′ IGF2 and ICR1 , particularly between the CCD and CR1 Fig. .CDKN1C/KCNQ1OT1 domain , and a decrease in the associations among CTCF1 and ICR2/Enh 3 and between ICR2 and 5′ CDKN1C. Finally, we observed loss of the contact between CTCF1 and Enh 1, along with a new interaction of ICR2 and 3′ KCNQ1 , we found variations in strength or complete loss of several ROIs in the ain Fig. . In partNQ1 Fig. . These dIn the BWS-ICR2 cell line the monoallelic ICR2-CTCF4 interaction was lost Fig. . We presIGF2/H19 and CDKN1C/KCNQ1OT1 domains  to evaluate the ICR1 association, and in the 3C products of the CTRL2 (heterozygous for SNPs in ICR2 restriction fragment as described above) to assess ICR2 contacts. Interestingly, ICR2 monoallelically associated with the region upstream of IGF2 (GT haplotype) and with the CTCF Dw (CG haplotype), whereas ICR1 biallelically interacted with Enh 2 , and between the green-red doublet of one allele and that of the other allele, to detect the trans-associations . For cis-interactions we considered the inter-probe FISH distance ≤ 0.35 µm  as indicative of colocalisation37. For trans-associations, a distance of 1 μm between the doublet of the two alleles was chosen as upper cut-off39.To validate the 3C results, we performed 3D DNA FISH experiments in controls and BWS-ICR2 cell lines. We chose to analyse the BWS-ICR2 cell line because it lost almost all the interactions between the two domains observed in 3C experiments and did not show new contacts Fig. . Two procis-colocalisation, with a 44.06% of the alleles showing an inter-probe distance ≤ 0.35 µm (mean of the CTRLs). These data confirm the 3C results on the interactions between IGF2/H19 and CDKN1C/KCNQ1OT1 domains, clearly showing that the most frequently associations are in cis.Figure cis-colocalisation was drastically reduced  in BWS-ICR2 cell line ; 2) +/− when one allele showed cis-association (distance ≤ 0.35 μm) and the other did not (distance > 0.35 μm); 3) −/− or cis-interaction absent in both alleles (distance > 0.35 μm).To understand how the two parental alleles appeared in each nucleus, we classified the nuclei into three different categories Fig. , e: 1) +cis-associations on both alleles (P ≤ 0.0001), whereas the BWS-ICR2 nuclei without interactions increased up to 50% , the 42.23% +/− (40.07% CTRL1 and 44.5% CTRL2) and the 34.8% −/− (34.44% CTRL1 and 35.17% CTRL2) Fig. . In BWS-cis- and trans-interactions.Overall, evidence from visual analyses corroborates the 3C results and indicates that in BWS-ICR2 cells one allele, purportedly the maternal one, is dysregulated due to the loss of both We did not observe significant difference of the +/− category. This may be because the loss of the interaction between the two domains of one allele in BWS-ICR2 cells cause the shift from +/+ to +/− and from +/− to −/− of some nuclei, maintaining unchanged the category +/−.IGF2/H19 and CDKN1C/KCNQ1OT1 domains using 3C assays and 3D FISH in normal cell lines, to define the regional configuration of the physical interactions between the target genes and elements involved in gene regulation. Our data extend the available information regarding the structure of the IGF2/H19 domain and define, for the first time, the interactome of the CDKN1C/KCNQ1OT1 domain and the long-range contacts in which the two domains engage. We also evaluated the 3D architecture of these regions in cells from patients with BWS and SRS to assess the effects of genetic and/or epigenetic defects at 11p15.5 on regional chromatin architecture, and we identified profound alterations in the structure of the entire imprinted domain.Knowledge of the 3D chromatin organisation at chromosome 11p15.5 may help to improve understanding of the pathomechanisms underlying BWS and SRS imprinted disorders, typically associated with 11p15.5 alterations. Here, we studied the chromatin conformation of the IGF2/H19 and CDKN1C/KCNQ1OT1 loci, which appear to be stable and reveal a complex chromatin structure, reflecting the fine-tuning of parental allele-specific regulation of IGs. Most of these interactions were confirmed by the comparison between our 3C results and the virtual 4C data , while isolating the silenced alleles .Very few data are available about chromatin interactions at the IGF2/H19 and CDKN1C/KCNQ1OT1 domains were discovered. Interestingly, we identified syndrome-specific associations that recurred among the patient cell lines irrespective of their genetic/epigenetic defect, suggesting the existence of novel alleles with abnormal chromatin conformation. These data may indicate that, even if in BWS and SRS the methylation of an ICR shifts from maternal to paternal status or vice versa, this is not sufficient for the opposite chromatin conformation to be assumed. In Supplementary Fig. CDKN1C/KCNQ1OT1 domain, the loss of interactions between CDKN1C and the regional enhancers observed in BWS-ICR2 is consistent with the altered expression of this gene observed in patients with BWS and ICR2 hypomethylation.This study included cell lines generated from patients with BWS and SRS, in which drastic perturbations of the interaction profiles of both KCNQ1 (CTCF4) that is lost in BWS-ICR2 cells. In this CTCF cluster Demars and collaborators41 previously reported genetic variants that affect CTCF binding and confer a risk of BWS on maternal transmission. Taken together these observations suggest a key role of ICR2-CTCF4 interaction in establishing the 3D structure of the region. Therefore, the loss of this interaction could lead to the alteration of the imprinted genes expression.Interestingly, we identified a crucial monoallelic interaction between ICR2 and a CTCF-binding site within Overall, these data highlight the importance of long-range interactions in the control of IGs expression.IGF2/H19 domain interactome in a BWS cell line with a driving defect in ICR2 and vice versa. These results suggest that the conformation of one locus influences the 3D structure of the other. Also, SRS-ICR1 cells exhibited changes at the CDKN1C/KCNQ1OT1 domain, supporting the idea of interconnection between the chromatin conformations of the two regions. By 3C and 3D FISH approaches on controls cells, we demonstrate the existence of inter-domain interactions that are lost or changed under pathological conditions. These data lead us to conclude that the 3D chromatin structure at 11p15.5 is more complex than expected, with different orders of chromatin organisation, from the level of the individual domains to a higher order structure between the two domains. These inter-domains contacts could be critical to the correct looping within the single domains: the maternal allele of the IGF2/H19 domain could require the maternal allele of the CDKN1C/KCNQ1OT1 domain to assume/maintain the proper chromatin conformation and vice versa, with the same phenomenon occurring at the paternal allele.A significant finding of this study is the observation of unexpected alterations in the IGF2 and ICR2-CTCF Dw (H19) are monoallelic (3C-SNP data) and are cis-interactions (3D FISH data), and they move IGF2 close to ICR2 on one parental allele, and H19 into proximity to ICR2 on the other allele. These data suggest the involvement of a mechanism to coordinate genes with the same expression status, even those distant from each other, as previously described for other genes and regions42 in this context, IGF2 and KCNQ1OT1 on the paternal allele and H19 and CDKN1C on the maternal allele.It is important to note that the inter-domain interactions between ICR2-region upstream of loci. These data could suggest significant rearrangements within the TAD, where the two control regions belong. The reconfiguration of the TAD could affect the expression of the imprinted genes and so have dramatic effect on the phenotype.The 3C and 3D FISH results obtained in pathological cell lines highlight the alteration of the contacts between the two IGF2/H19 and CDKN1C/KCN1OT1 loci in the organisation of the 3D chromatin structure. Nevertheless, other regions beyond the 3C analysed range, could be involved in the chromatin architecture of the 11p15.5 imprinting domain.In this study we focused our attention on the role played by the interactions within and between IGF2/H19 and CDKN1C/KCNQ1OT1 domains, based on cis inter-domains interactions, that was lost in BWS and in SRS; these inter-domain associations seem to be crucial to the formation/maintenance of the normal looping conformations within each domain. Moreover, we defined a monoallelic interaction between ICR2 with CTCF4 that seems to be crucial to establish the 3D structure of the CDKN1C/KCNQ1OT1 region.In summary, we provide a refined characterisation of the chromatin architecture of the 11p15.5 imprinted domain and identified a cross-talk between the Based on these evidences, we also provided hypothetical models that attempt to explain the complex structure of 11p15.5 region and highlight regions with a key role in imprinting regulation and monoallelic expression, including those distant from ICRs, such as CTCF4.Experiments were carried out using lymphoblastoid cell lines generated from two healthy children with normal ICR1 and ICR2 methylation (CTRL1 and CTRL2), three children patients with BWS, carrying different ICR1 and/or ICR2 methylation defects , and one child with SRS with hypomethylation of ICR1 (SRS-ICR1) . Both normal and patient lymphoblastoid cell lines were cultured in RPMI 1640 medium supplemented with 10% foetal bovine serum (Euroclone) and antibiotics  at 37 °C in 5% COThe study was approved by the Ethics Committee of Fondazione IRCSS Ca’ Granda Ospedale Maggiore Policlinico (no. 526/2015). Appropriate written informed consent was obtained from parents’ patients. All the procedures performed in this study were in accordance with the 1964 Helsinki declaration and its later amendments.43.Total DNA was extracted from lymphoblastoid cell lines using the QIAamp DNA Mini kit (Qiagen) according to the manufacturer’s instructions. Analyses of ICR1 and ICR2 methylation levels were performed as previously described46. In detail, 13 × 106 lymphoblastoid cells were resuspended in 5 ml of crosslinking buffer  and crosslinked with 2% formaldehyde for 10 min at room temperature. Reactions were quenched by the addition of glycine, and cells were subsequently washed with ice-cold PBS. Cells were then recovered and suspended in 10 ml of cold cell lysis buffer  and incubated on ice for 10 min. Lysis was completed with approximately twenty strokes using a Dounce homogenizer (Tight). Nuclei were collected by centrifugation and resuspended in 500 µl of 1.5× BglII restriction enzyme buffer  with SDS to a final concentration of 0.3%, and samples were incubated at 37 °C for 1 h with gentle agitation. Triton X-100 was then added to a final concentration of 1.8% and incubated for a further 1 h at 37 °C. Digestion was performed overnight with 600 U of BglII at 37 °C with gentle agitation  and incubated for 1 h at 37 °C with gentle agitation. Ligation was then performed by addition of 2000 U of T4 DNA Ligase (New England Biolabs) and incubation at 16 °C for 8 h. To reverse the crosslinking and eliminate proteins, samples were treated with 1 mg of Proteinase K and incubated at 65 °C overnight with agitation. The following day, products were treated with RNase cocktail (Ambion), to eliminate RNA, and the ligated chromatin was phenol-chloroform purified and ethanol precipitated. The quality of the 3C samples was checked as previously described46. In particular, the efficiency of BglII digestion was checked by PCR by amplification of a fragment across each restriction site in undigested (UND), digested not re-ligated (DIG), and ligated samples (3C)  was performed as previously described with a few modificationslocus, a second digestion of 3C template DNA was performed with BamHI. Briefly, 8 µg of 3C DNA was digested overnight with 24 U of BamHI at 37 °C. The next day, DNA was diluted in 1× T4 ligase buffer (NEB) containing 320 U of T4 ligase and incubated at 16 °C for 8 h. The ligated DNA was then phenol-chloroform purified and ethanol precipitated.To analyse the ICR2 BglII, followed by ligation in a 50 µl reaction volume. The mixture was purified by phenol extraction and ethanol precipitation. An appropriate amount of DNA, which could be amplified within the linear range (2.5 ng for control template and 25 ng for 3C ligation products), was used for subsequent experiments47.Results generated from control template, containing all possible ligation products in equal amounts, were used for normalization of 3C PCR data. One Bacterial Artificial Chromosome (BAC) (RP11-889I17) and three fosmids  covering the ICR1 region, and four BACs  covering the ICR2 region, were used to generate the PCR control template .Both the 3C ligation products and control template libraries were PCR amplified (35 cycles) using GoTaq Flexi (Promega) with reagents provided by the manufacturer. PCR products were separated by 2% agarose gel electrophoresis and quantified using Image J software  and dbSNP (https://www.ncbi.nlm.nih.gov/projects/SNP/) databases, were selected. Analysed SNPs and their corresponding genomic positions and MAFs are reported in Supplementary Table SNPs in ligated products were analysed to define the allele specificity of chromatin associations in control lymphoblastoid cell lines (CTRL1 and CTRL2). Heterozygous SNPs, mapping within the restriction fragments generated in 3C experiments, with minor allele frequencies (MAFs) close to 0.5, as reported by UCSC (http://www.ncbi.nlm.nih.gov/genbank/).Qualitative analysis of SNPs was performed by Sanger sequencing. In detail, 3C ligated products were separated by 2% agarose gel electrophoresis, excised, and gel purified using the MinElute Gel Extraction kit (Qiagen), according to the manufacturer’s protocol. Eluted PCR products were quantified using an ND-1000 Spectrophotometer (NanoDrop products) and sequenced using the BigDye Terminator v3.1 Cycle Sequencing Kit on an ABI 3100XL capillary sequencer (Applied Biosystems). Electropherograms were analysed using ChromasPro software 1.42 (Technelysium Pty Ltd), and aligned to the wild-type chromosome 11 NCBI reference sequence (NC_000011.10)  and 26R-24R (Enh A-ICR1) 3C products from CTRL1 was performed by MassARRAY analyzer 4 (Agena Bioscience), using the single base extension technique. Amplification and extension primers were designed using Assay Designer Suite v.1.0 (Agena Bioscience), and their sequences are provided in Supplementary Table 48.The 3D multicolor DNA FISH assay was performed as previously described with a few modificationsTo produce the two probes, we used the BAC RP11-66E9 (ICR2-Enh 2) and a mixture of the fosmids G248P87866G7, G248P82515A8 and G248P88924E6 (ICR1-CTCF Dw). 1500 ng of BAC or fosmid-mix DNA were labelled with biotin-11-dUTP (Thermo Fisher Scientific) or digoxigenin-11-dUTP (Roche Diagnostics) respectively through nick translation at 16 °C for 90 min, to obtain an average probe size of 50 bp. Probes were collected by ethanol precipitation, resuspended in Elution Solution  and then quantified using a Nanodrop 1000 Spectrophotometer (Thermo Fisher Scientific). For a single experiment, 150-300 ng of each probe was precipitated with 3.5 μg of Human Cot-1 DNA (Thermo Fisher Scientific) and 20 μg of Deoxyribonucleic acid, single stranded from salmon testes , and then resuspended in 6 μl of Hybridization solution (50% formamide pH 7.0 (FA)/2× saline-sodium citrate (SSC)/10% Dextran sulfate).4 lymphoblastoid cells of CTRL1, CTRL2 and BWS-ICR2 were plated on poly-lysine (Sigma-Aldrich)-treated coverslips and fixed with 3% Paraformaldehyde (PFA) in 1× PBS/0.1% TWEEN 20 (PBS-T) for 10 min at room temperature (RT). During the last minute, few drops of 0.5% Triton X-100 in 1× PBS were added and then cells were washed with 0.05% Triton X-100 in PBS three times for 3 min at RT. Cells were first permeabilised with 0.5% Triton X-100 in PBS for 10 min at RT and then were treated with RNase Cocktail Enzyme Mix (Thermo Fisher Scientific) for 1 h at 37 °C, to remove RNA. Samples were incubated overnight at RT with 20% Glycerol in PBS and then conserved at +4 °C. Cells were subjected to other steps of permeabilisation by four cycles of freeze and thaw, interleaved by soak with 20% Glycerol in PBS. Permeabilised cells were washed with 0.5% Triton X-100 in PBS for 5 min at RT and twice with 0.05% Triton X-100 in PBS for 5 min. Cells were then incubated in 0.1 M HCl for 15 min at RT, followed by a rinse with 2× SSC and then incubated in 50% FA in 2× SSC overnight at RT.25 × 102 in PBS for 5 min. Nuclei were post-fixed with 1% PFA in PBS for 1 min, washed with PBS for 5 min and twice with 2× SSC, and then incubated with 50% FA in 2× SSC for at least 30 min at RT.Slides were equilibrated in 2× SSC for 2 min, washed in PBS for 3 min and then treated with 0.0025% pepsin (Sigma-Aldrich) in 0.01 N HCl for 2 min at RT, to eliminate cytoskeleton. Pepsin was inactivated by two washes with 50 mM MgClHybridization between fixed cells (kept in 50% FA/2× SSC) and hybridization solution (probes in 50% FA/2× SSC/10% dextran sulphate) was performed as follows. Hybridization solution was loaded on a clean microscopic slide, coverslip with nuclei was turned upside down on the drop of hybridization mixture and sealed with rubber cement. Samples were denatured for 4 min at 75 °C and leaved to hybridise in a metallic box floating in a 37 °C water bath at least overnight. After hybridization, cells were washed three times with 2× SSC at 37 °C for 5 min and then three times with 0.1× SSC at 60 °C for 5 min, followed by a rinse with 0.2% TWEEN 20 in 4× SSC. Blocking of unspecific sites was obtained using Blocking solution (4% BSA in 4× SSC/0.2% TWEEN 20) for 30 min at 37 °C. Samples were then incubated for 35 min in a dark and wet chamber at 37 °C with the appropriate concentration of Streptavidin, Alexa Fluor 647 conjugate (Thermo Fisher Scientific)  or DyLight 488 Labeled Anti-Digoxigenin/Digoxin (Vector Laboratories) (1:100) diluted in Blocking solution. Samples were washed with 0.2% TWEEN 20 in 4× SSC three times for 3 min at 37 °C, equilibrated in PBS and post-fixed with 2% Formaldehyde in PBS for 10 min at RT. Finally, the 3D-fixed nuclei were washed with PBS three times for 5 min at RT, counterstained with 1 ng/μl DAPI (Thermo Fisher Scientific) in PBS for 10 min at RT and washed with PBS twice for 5 min at RT. Coverslips were mounted with Prolong Diamond (Thermo Fisher Scientific) and slides were stored at +4 °C until image acquisition. Images were acquired using Eclipse Ti-E microscope (Nikon Instruments) at 100× magnification, with an axial distance of 200 nm consecutive sections. Image analyses were performed with Volocity software (PerkinElmer). Distances (µm) between thresholded signal centroids within each nucleus were measured.Regarding 3C data, all experiments were performed in duplicate, from two independent 3C assays. The frequencies of associations are expressed as mean ± standard deviation. The controls mean indicates the mean of four independent 3C assays, two from CTRL1 and two from CTRL2. The mean of each pathological cell line derives from two independent 3C assays. Differences in association frequencies between controls and patients cell lines were evaluated using the two-way ANOVA test followed by the Bonferroni post-test in the GraphPad Prism program.Regarding 3D FISH experiments, for comparisons of CTRLs and BWS-ICR2 inter-probe distances, the unpaired t-test was applied, in the GraphPad Prism program. To determine the significance between the number of CTRLs and BWS-ICR2 nuclei, classified by inter-probe distances, the Fisher’s exact test was applied.Statistical significance is indicated as ****P ≤ 0.0001; ***P ≤ 0.001; **P ≤ 0.01; *P ≤ 0.05.https://www.ncbi.nlm.nih.gov/projects/SNP/.dbSNP, https://www.ensembl.org/index.html.Ensembl Genome Browser, http://www.ncbi.nlm.nih.gov/genbank/.GenBank, http://compbio.mit.edu/ChromHMM/.ChromHMM sofware, https://imagej.nih.gov/ij/.Image J software, http://www.omim.org/.OMIM, https://genome.ucsc.edu.UCSC Genome Browser, http://epigenomegateway.wustl.edu/.WashU Epigenome Browser, http://promoter.bx.psu.edu/hi-c/virtual4c.php.Computational and Functional Genomics/Epigenomics, Supplementary information.