How the genome activates or silences transcriptional programmes governs organ formation. Little is known in human embryos undermining our ability to benchmark the fidelity of stem cell differentiation or cell programming, or interpret the pathogenicity of noncoding variation. Here, we study histone modifications across thirteen tissues during human organogenesis. We integrate the data with transcription to build an overview of how the human genome differentially regulates alternative organ fates including by repression. Promoters from nearly 20,000 genes partition into discrete states. Key developmental gene sets are actively repressed outside of the appropriate organ without obvious bivalency. Candidate enhancers, functional in zebrafish, allow imputation of tissue-specific and shared patterns of transcription factor binding. Overlaying more than 700 noncoding mutations from patients with developmental disorders allows correlation to unanticipated target genes. Taken together, the data provide a comprehensive genomic framework for investigating normal and abnormal human development. How the epigenomic landscape linked to transcription regulates human embryonic development is unclear. Here, the authors analyse the dynamics of H3K27Ac, H3K4me3 and H3K27me3 during the period when organs first assemble as a platform for understanding noncoding developmental disorders. The non-coding genome also harbours over 80% of single-nucleotide polymorphisms (SNPs) implicated in genome-wide association studies (GWAS) for developmental disorders, or in GWAS of later onset disease, such as schizophrenia and type 2 diabetes, where contribution is predicted from early development3. These genetic alterations are presumed to lie in enhancers for developmental genes or in other regulatory elements, such as promoters for non-coding RNAs that may only be active in the relevant tissue at the appropriate stage of organogenesis. Aside from rare examples4, this has remained unproven because of lack of data in human embryos. While regulatory data are available in other species at comparable stages5, extrapolation is of limited value because the precise genomic locations of enhancers are poorly conserved7 even allowing for enriched sequence conservation around developmental genes9. Sequence conservation alone is also uninformative for when and in what tissue a putative enhancer might function. Comprehensive regulatory information is available from later fetal development via initiatives such as NIH Roadmap10, but these later stages largely reflect terminally differentiated, albeit immature cells rather than progenitors responsible for organ formation. In contrast, a small number of studies on a handful of isolated tissues, such as limb bud11, craniofacial processes12, pancreas13, or brain14, have demarcated regulatory elements directly during human organogenesis. However, most organs remain unexplored. Moreover, nothing is known about patterns of regulation, including both activation and repression, deployed across tissues, which is an important factor because tissues are often co-affected in developmental disorders. To address these gaps in our knowledge, we set out to build maps of genome regulation integrated with transcription during human organogenesis at comprehensiveness currently unattainable from single-cell analysis.Organogenesis is the key phase when the body’s tissues and organs are first assembled from rudimentary progenitor cells. In human embryos, this is the critical period during weeks 5–8 of gestation when disruption can lead to major developmental disorders. While ~35% of developmental disorders are explained by damaging genetic variation within the exons of protein-coding genes15. Overlaying the data revealed characteristic tissue-specific patterns of promoter and putative enhancer activity, and unannotated human embryonic transcripts. This was particularly noticeable surrounding genes encoding key developmental transcription factors (TFs), such as the example shown for NKX2-5 in the heart 16. Unexpected H3K4me3 and H3K27ac peaks that failed to map to the transcriptional start sites (TSSs) of annotated genes mapped to the TSS of previously unidentified human embryo-enriched transcripts, such as the example shown in Fig. HE-TUCP-C5T408 and HE-LINC-C5T409 . Recognising the importance of features surrounding key developmental genes such as NKX2-5, all the individual data are available to browse as tracks on the UCSC Genome Browser. These preliminary observations encouraged us to undertake full integration of the different datasets to enable a series of genome-scale analyses.Thirteen different types of organs and tissues were contributed by 61 human post-implantation embryos, microdissected and subjected to chromatin immunoprecipitation followed by deep sequencing (ChIPseq) for three histone modifications Fig. : H3K4me3art Fig. . Tissues17. While three histone marks allowed for eight different segmentations, aggregation into fewer states was possible . Taken together, this reliance on an arbitrary threshold is suboptimal. Therefore, we used ngsplot to cluster promoter profiles for each histone mark integrated with transcription over 3 kb either side of 19,791 distinct protein-coding TSS in each tissue21  were actively repressed in at least one tissue compared with 20.0% of non-TF genes . H3K27me3 detection at the TSS was ~50% greater for genes encoding TFs . We tested this in the replicated datasets by comparing genes transcribed uniquely in one tissue for either inactivity (no mark) or active repression elsewhere . Gene ontology (GO) analysis of the uniquely expressed/disallowed elsewhere gene sets identified the appropriate developmental programme in all instances ; while the genes in the second transition specifically related to post-embryonic pancreatic islet cell differentiation and beta-cell function 25.To scrutinise regulatory changes temporally, we focussed on pancreas and included datasets from human pluripotent stem cells (hPSCs) and adult tissue. Different sets of repressed genes lost their H3K27me3 mark to become expressed as cells transitioned from pluripotency to embryonic pancreatic progenitors or from pancreatic progenitors to mature pancreas Fig. . Surpris26 and not detected in the FANTOM5 project27. We developed an algorithm to test for embryonic tissue specificity and filtered for sequence conservation . We manually inspected the remainder for proximity (<1 mb) to genes encoding TFs and, in particular, to increase clinical relevance, to those associated with major developmental disorders. We ensured no H3K4me3 or polyadenylated transcription in the immediate vicinity . We tested 10 such enhancers out of 44 within 1 mb of TBX15, HEY2, ALX1, IRX4, PITX2, HOXD13, NKX2-5, WT1, SOX11 and SOX9 for their ability to direct appropriate GFP expression in stable lines of transgenic zebrafish (WT1 and h-022-mix near SOX11) failed to generate any GFP in any location. The remaining eight all yielded GFP at the predicted site in zebrafish embryos . Taken together, these data imply that our H3K27ac detection marks previously unannotated human enhancers, which function over considerable evolutionally distance, despite the absence of detectable sequence conservation. This is most likely associated with overall conservation of TF activity between homologous organs across species28.Having recognised the disallowed status of developmental TFs in inappropriate tissues, we wanted to test whether our putative intergenic human embryo-enriched enhancers were capable of driving appropriate reporter gene expression at the correct locations in developing zebrafish. We identified H3K27ac marks that were enriched in the human embryo compared with 161 ENCODE or NIH Roadmap datasetsebrafish . Two  compared to 25% of the remaining 141,706 bins . MEF, TBX and bHLH family members emerged in the heart-specific bins  identified enrichment for the ETS motif. ETS transcription factors are involved in cell cycle control and proliferation30.Having integrated our data, we could also uncover regulatory regions that were shared precisely across two or more tissues relevant to developmental disorders which manifest in multiple organs. Enrichment for composite PITX1/bHLH motifs was found in the limb and palate Fig. . GATA-bi31. Previously, as part of the Deciphering Developmental Disorders (DDD) study, we studied 7930 individuals and their parents32. In all, 87% of patients had neurodevelopmental disorders. 10% had congenital heart defects. 68% of patients lacked disease-associated DNMs within exomes (exome-negative) pointing to the likely importance of the non-coding genome2. We sequenced 6139 non-coding regions (4.2 mb) selected as ultra-conserved regions (UCRs: n = 4307), experimentally validated enhancers (EVEs: n = 595) or as putative heart enhancers (PHE: n = 1237) and found 739 non-coding DNMs2. In total, 78% of the 6139 regions were marked by H3K27ac or H3K4me3 in our embryonic tissues, with a higher percentage overlap for the EVEs (87%) and near-perfect overlap for the PHEs (99%)   Fig. . An addi9%) Fig. . Nearly 9%) Fig. . In tota9%) Fig. . Enrichm56) Fig. . This waLINC01572 , in a 4 kb region of brain-specific H3K27ac . Across 18 protein-coding genes in the region, the H3K27ac signal was most highly correlated to expression of ZNF821 (r = 0.92) 550 kb away and anti-correlated to the adjacent gene, ATXN1L . RBM24 is required for cardiac development with knockout mice dying at E12.5-14.5 with ventricular septal defects and compromised cardiac muscle assembly33. We used CRISPR-Cas9 to delete the enhancer in our established NKX2-5-GFP reporter hPSC model35 . We have made all correlations freely available as tracks on the UCSC Genome Browser and present two examples here as preliminary exemplars. A DNM linked to neurodevelopmental disorders in a UCR on chromosome 16 is in the middle of the annotated testicular LINC RNA, ase Fig. . Across ans Fig.  and the l35 Fig. . Cells wors Fig. . This wacus Fig. .Fig. 7NeTaken together, we have assembled datasets of regulatory activity linked to transcription for a range of human embryonic tissues during the period of organogenesis. All data are available to browse as tracks on the UCSC Genome Browser ready for overlay with genetic variants identified by clinical sequencing and GWAS.14. Here, we incorporated epigenomic data with transcription across 13 sites during human organogenesis to build tissue-by-tissue maps of enhancers and promoters linked to gene expression. While similar to prior work in mouse5 and building on our previous transcriptomic atlas15, the integrated approach here offers opportunities to understand how human organ formation is regulated in health and disease.Previous studies of enhancer usage in human embryos have tended to focus on individual tissues inferring, amongst other findings, aspects of genome regulation responsible for human-specific attributes15. While single-cell technology allows deconvolution of organ-level heterogeneity, at present the techniques do not permit the extent of analysis for histone modification or transcription as we have undertaken here. We envisage our integrated atlas will provide a valuable complement to current and future single-cell analyses of human organogenesis.It is important to draw out certain features of the study, including limitations. The datasets comprised pooled tissue samples across several embryos including both sexes. This removed the opportunity for analysis of male or female tissue in isolation; however, it mitigated against the risk of misinterpreting organ-level differences that might be due to sex, for instance related to H3K27me3 and X chromosome inactivation. Moreover, the aggregated data across many embryos reduced the risk of other misleading conclusions due to one-off biological or technical factors, such as deterioration during transport. Our tissue collection and molecular analyses occur on a single campus with immediate processing. For instance, in quality control, we have found overnight delay in preservation media can lead to a 16-fold decline in the levels of some key developmental TFs, despite tissue remaining viable and suitable for immunohistochemistry and subsequent tissue culture. It is also obvious that the bulk analyses contain aspects of tissue heterogeneity, as we reached in all tissues with our deeper analyses of promoter state , it is logical that mutations are located in regulatory regions of the genome that are active in post-implantation human embryos. As evident from Fig. 32. As cost continues to decline, WGS will become an increasingly important tool in main stream clinical investigation, opening up potential genetic diagnoses in the 98.5% of the human genome that lies outside of coding sequences. At this point, we anticipate that our datasets will help to stratify disease-associated variants. Enrichment of tissue-specific TF binding in these enhancers and promoters reinforced our previous findings based solely on computational analysis of 5′ flanking regions for the importance of NR5A1 in the adrenal and HNF4A in the liver15. However, the integrated sampling of numerous sites uncovered far more complex patterns of regulation operating across tissues. The enrichment of PITX1-binding motifs in active regulatory regions uniquely shared across the limb bud and palate fits with mutations in PITX1 causing limb defects and cleft palate37. Similarly, GATA4 and GATA6, inferred from regulatory regions shared uniquely between the heart and pancreas, are the only two TFs linked to the dual phenotype of cardiac malformation and monogenic diabetes39. Overlaying GWAS data with chromosomal conformation studies from older human fetal brain has prioritised target genes for risk of schizophrenia40. These techniques are yet to be applied at scale in much smaller human embryonic tissues. However, because we integrated the data from many tissues we can begin to make correlations of enhancer activity to target genes over megabase distances  in each individual, while only one might be pathogenicDeciphering profiles of H3K27me3 alongside other regulatory marks and expression profiles was informative. We did not observe bivalent marking of developmental promoters poised for gene expression before reaching the limits of resolution, at which gene sets for structures such as mesenchyme and nerves were common to all tissues. Instead, we discovered that organ-specific developmental programmes were disallowed in other human embryonic tissues by active repression at a series of gene promoters. The ontology of these gene sets, including many encoding TFs, inferred they are an important aspect of ensuring correct cell fate decisions. This realisation opens up an opportunity for more rigorous benchmarking of differentiated hPSCs, including organoids, both for proximity to the intended lineage in how appropriate gene expression is activated but also against a clearly defined set of epigenomic features for how undesired cell fates are avoided.41, by providing greater resolution of regulatory information and depth of sequence information. Moreover, our integrated analyses establish a framework for prioritising and interpreting disease-associated variants discovered by WGS42 and provide clear routes towards understanding the underlying mechanisms.In summary, we present an integrated atlas of epigenomic regulation and transcription responsible for human organogenesis. We make all datasets freely available alongside analysis tracks on the UCSC Genome Browser. The uncovering of cryptic regulatory regions and patterns of regulation across organs arose because of direct study of human embryonic tissue. The data complement current international projects such as the Human Cell Atlas15. Tissue collection took place on our co-located clinical academic campus overseen by our research team ensuring immediate transfer to the laboratory. Material was staged by the Carnegie classification, and individual tissues and organs were immediately dissected , were removed under a dissecting microscope. The ureter was removed from the renal pelvis. A window of tissue was removed from the lateral wall of the left ventricle of the heart. The dissected segment of liver avoided the developing gall bladder. The trachea was removed where it entered the lung parenchyma. The stomach was isolated between the gastro-oesophageal and pyloric junctions. The palatal shelves were dissected on either side of the midline. The eye was dissected, and the RPE peeled off mechanically from its posterior surface .Human embryonic material was collected under ethical approval from the North West Research Ethics Committee 18/NW/0096), informed consent from all participants and according to the Codes of Practice of the Human Tissue Authority8/NW/0096Tissues were gently teased apart before cross-linking in 1% formaldehyde for 10 min at room temperature. Fixation was quenched with 125 mM glycine for 5 min at room temperature before centrifugation, removal of the supernatant and washing twice with 1 ml of PBS. The final PBS supernatant was discarded and samples stored at −80 °C until use . The sex3) for 30 min at 65 °C and the beads discarded. The resulting samples were incubated with 167 mM NaCl for 5 h at 65 °C to remove crosslinks followed by 1 h incubation with 14 μg of proteinase K. The resulting chromatin was then purified .All ChIPseq datasets were in biological replicate, except for the stomach and tongue . Each sa® ChIP Sample Preparation Guide . Briefly, sample DNA (5–10 ng) was blunt-ended and phosphorylated, and a single “A” nucleotide added to the 3′ ends of the fragments in preparation for ligation to an adapter with a single base “T” overhang. Omitting the size-selection step, the ligation products were then PCR-amplified to enrich for fragments with adapters on both ends. The final purified product was then quantitated prior to cluster generation on a cBot instrument (Illumina). The loaded flow cell was sequenced (paired-end) on a HiSeq2500 (Illumina). In total, ChIPseq was carried out in three batches with hierarchical clustering analysis to examine for batch effect 44 against a common input sample . MACS parameters used were as follows: band width 300 bp, mfold 5–50 (used in cross-correlation for fragment length estimation), q-value cut-off 0.05. To prioritise candidate enhancers for transgenic testing, H3K27ac data from ENCODE (seven cell lines) and NIH Roadmap (154 samples)26 were mapped similarly. Subsequently, all data, including the external H1 hPSC and adult pancreas data ata Fig. , were ma17 under default parameters labelling samples by tissue and histone modification. The three histone marks allowed for eight segment states.Genomic segmentation was performed using chromHMM (version 1.11)21. Default settings allowed for five clusters based on rank profiles of read counts 3 kb either side of the TSS. The returned clusters were then classified according to characteristics detected in both replicates into five major promoter states  on unnormalized reads for the combined dataset of replicated RNA-seq and ChIPseq for H3K4me3, H3K27ac and H3K27me3.61 on untes Fig.  and 10.47 with modification of the R code to reorder the horizontal splines  within each tissue to keep similar colours together.Alluvial plots were created using the R package Alluvial Diagrams version 0.2-048. Background comprised all remaining annotations used in the ngs.plots either for embryonic tissues 10; and that were undetected in the FANTOM5 project27. To filter these embryonic marks for tissue specificity, an initial dataset was selected at random and peaks called that were >200 bp. The H3K27ac datasets from other embryonic tissues were then overlaid sequentially in random order. Only called peaks >200 bp were included. After each addition, any peaks with <50% overlap between the new and existing dataset were retained. For those retained regions, overlapping sequence was filtered out. Once completed, the final set of human embryo-enriched, tissue-specific sequences were again filtered for regions >200 bp. Re-running the tissue-specificity algorithm for random addition of datasets resulted in a 99.6% match to the first analysis. These candidate enhancer regions were filtered for sequence conservation (PhastCons LOD score >50)50 and correlated with surrounding transcription (≤1 mb in either direction). We manually inspected the remainder for proximity (<1 mb) to genes encoding TFs associated with major developmental disorders and ensured no H3K4me3 or polyadenylated transcription in the immediate vicinity . This resulted in 44 candidate enhancers from which we tested ten. The candidate sequences were first cloned in TOPO vector using pCR8/GW/TOPO TA cloning kit  and then recombined to the reporter vector Minitol2-GwB-zgata2-GFP-4851 using the Gateway LR clonase II Enzyme mix . The reporter vector contains a robust midbrain enhancer as an internal control for transgenesis.A systematic approach identified candidate enhancers that were human embryo-enriched and tissue-specific. We identified marks from the first batch of H3K27ac with RPKM ≥ 25 and ≥2.5-fold enrichment in the human embryo compared to ENCODE (7 cell lines)52. Danio rerio embryos were collected from natural spawning and injected in the yolk at the one-cell stage. The injection mixture contained 50 ng/μl Tol2 transposase mRNA, purified enhancer test vector and 0.05% phenol red. The concentration of the enhancer test vector was between 15 and 30 ng/μl. Injected embryos were visualised from 24 hpf to 48 hpf in an Olympus stereomicroscope coupled to a fluorescence excitation light source in order to detect the pattern of GFP.Transgenic fish were generated with the Tol2 transposon/transposase method of transgenesisEmbryos and adults zebrafish were maintained under standard laboratory conditions. They were manipulated according to Spanish and European regulation. All protocols used have been approved by the Ethics Committee of the Andalusian Government (license numbers 450-1839 and 182-41106 for CABD-CSIC-UPO).53. Reads from mitochondrial and unplaced chromosome annotations were removed. A further 697 bins were filtered out for possessing >10,000 reads in all samples or if the mean read count from input controls was ≥50% of the mean read count of all samples or for being situated in pericentromeric regions x axis. Our code (arseFromElbow) to determine these thresholds from a vector of counts is available on github55. Phi correlation was used to measure the agreement between tissue replicates called by the 1 kb binning method compared to MACS44. Hierarchical clustering of datasets was undertaken to assess potential batch effect (displayed by heatmap) based on the combined set of the 10,000 most highly ranked bins from each sample. Sets of tissue-specific (replicated in exactly one tissue) and tissue-selective bins  were produced for each embryonic tissue. EulerGrids showing pattern frequencies of bins across samples were produced using the function plotEuler56 as an adaptation of a proposal from Reynolds and colleagues57 on Biostars.org58.Downsampling of read counts to the 99th percentile was used to generate the custom elbow threshold that called bins as marked or not for subsequent downstream analyses. When read counts were ordered and plotted by rank, the resulting graph was typically exponential with most bins having zero or very few reads (below the elbow threshold) and a small number of bins with very high read counts (above the elbow threshold). The elbow was defined as the point on the line with the shortest Euclidian distance to the maximum rank intercept with the 59. For selected 1 kb bins marked with H3K27Ac, the background set was the remainder of bins with replicated H3K27Ac across all tissues .HOMER v4.9 was used to search for enriched motifs in selected sets of binsNKX2-5eGFP/w hPSCs (NKX2-5-GFP reporter) were maintained on mouse embryonic fibroblasts and passaged using TrypLE Select (Thermo Fisher Scientific) with cardiomyocyte differentiation performed via embryoid body (EB) formation61; except for modified concentrations of BMP4 (25 ng/ml) and Activin A (25 ng/ml). Day 14 EBs were imaged for GFP and then collected into Trizol Reagent (Thermo Fisher Scientific) for RNA extraction according to the manufacturer’s specifications followed by treatment with DNaseI Kit (Sigma). First-strand cDNA synthesis and qPCR were performed using the Tetro cDNA synthesis kit (Bioline) and Power Up Sybr Green (Invitrogen). Primers for genes in the RBM24 locus are in Supplementary Data ATXN1 using CHOPCHOP62 version 3  through CellSens software (Olympus). Images were processed and analysed using Fiji ImageJ with GFP quantified as a proportion of the total EB area. Montages for bright-field and FITC channels were created consistently for all EBs using a Gaussian Blur with sigma radius of 5:00, and a colour threshold:brightness lower limit of 52:255. Regions of interest were mapped and the relative GFP-positive area quantified. Significance was assessed using two-tailed Student’s Further information on research design is available in the Supplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary Files


Following entry into host cells, the KSHV genome undergoes circularization and chromatinization into an extrachromosomal episome ultimately leading to the establishment of latency. The KSHV episome is organized into distinct chromatin domains marked by variations in repressive or activating epigenetic modifications, including DNA methylation, histone methylation, and histone acetylation. Thus, the development of KSHV latency is believed to be governed by epigenetic regulation. In the past decade, interrogation of the KSHV epitome by genome-wide approaches has revealed a complex epigenetic mark landscape across KSHV genome and has uncovered the important regulatory roles of epigenetic modifications in governing the development of KSHV latency. Here, we highlight many of the findings regarding the role of DNA methylation, histone modification, post-translational modification (PTM) of chromatin remodeling proteins, the contribution of long non-coding RNAs (lncRNAs) in regulating KSHV latency development, and the role of higher-order episomal chromatin architecture in the maintenance of latency and the latent-to-lytic switch.Kaposi’s sarcoma-associated herpesvirus (KSHV) is an oncogenic  Accurate epigenetic status is essential for normal development and maintenance of tissue-specific gene expression in mammals, and disruption of epigenetic regulation can cause aberrant gene expression and diseases, such as cancer. Different from genetic variation, epigenetics is a reversible mechanism that modifies the genome, and thus, repair of epigenetic lesions has been envisioned to be more feasible than correction of DNA mutations. Epigenetic therapies are therefore emerging as an active area of preclinical and clinical cancer research.There are three primary interconnected epigenetic mechanisms , includi(i)de novo methylation. 5-mC within CpG islands in mammalian promoter regions is associated with transcription repression and aberrant DNA methylation is a common lesion related to carcinogenesis (Methylation of the 5th carbon on cytosine (5-mC) was the first identified, and one of the most well-studied epigenetic marks related to cancer . DNA metogenesis . Cytosinogenesis . 5-hmC sogenesis .(ii)acetylating the ε-amino group of lysine (Lys) using histone acetyltransferases (HATs), acetylation neutralizes the net positive charge on histones, leading to the unfolding of chromatin and exposure of negatively charged DNA to DNA-binding proteins, and consequently activation of gene transcription  residues are known to be methylated. Arg can be mono- or di-methylated, with the latter in a symmetrical or asymmetrical manner, by protein arginine methyltransferases (PRMTs) (Histone  (PRMTs) . Lys has (PRMTs) . In rece (PRMTs) . Histone(iii)Non-coding RNAs (ncRNAs) are RNA transcripts that do not encode proteins. Based on the length, ncRNAs are divided into two classes, (i) small ncRNAs (sncRNAs), with transcripts shorter than 200 nucleotides (nts), and (ii) long ncRNAs (lncRNAs), with transcripts longer than 200 nts that are devoid of protein-coding potential , though (iv)Architectural/spatial epigenetics considers the three-dimensional (3D) structure of a genome and its impact on gene expression and other nuclear activities. How the 3D organization of a genome operates with the addition of dynamics across time and its relationship to nuclear processes including transcription, DNA replication, and chromosome segregation are included in this class of mechanisms .γ-herpesvirus Kaposi’s sarcoma-associated herpesvirus  is one of the seven recognized human oncogenic viruses and has been linked to Kaposi’s sarcoma (KS) (The human oma (KS) , primaryoma (KS) , and an oma (KS) . KSHV isoma (KS) . After ede novo infection (de novo infection. This notion was supported by a report from DNA methylation on CpG islands is associated with gene silencing. It has been shown that the DNA methyltransferase inhibitor 5-Azacytidine (5-AzaC) is a stimulator of KSHV lytic reactivation , suggestnfection . They shnfection , indicatIt is important to note that both de novo infection. Initially  with the help of K-Rta, KSHV genomes rapidly acquired the active histone marks H3K4me3 and H3K27-ac. This was followed by (1st to 3rd dpi) sequential deposition of repressive histone marks H3K27me3 by PRC2 and H2AK119-Ub by Polycomb repressive complex 1 (PRC1). It is believed that binding of CBX in PRC1 to H3K27me3 deposited by PRC2 helps recruit PRC1 to the KSHV genome and RING1B in PRC1 ubiquitinated H2A at K119  modification, were initially identified as reversible protein modifications that regulate signal transduction. Among the PTMs, accumulating evidence suggests that the SUMO system plays an important role in regulating chromatin organization and transcription . In addiAs SUMO modifications regulate chromatin organization, we showed that SUMOylation of the chromatin binding protein Krüppel-associated box domain-associated protein-1 (KAP-1) is essential for its association with the KSHV genome and for maintaining viral latency. Phosphorylation of KAP-1 by KSHV vPK (ORF36) counteracts KAP-1 SUMOylation-dependent binding and thereby facilitates viral reactivation . BindingIn 2015, the genome-wide landscape of SUMO paralog modifications on the KSHV genome was revealed using ChIP-seq assays. The results showed similar SUMO-1 and SUMO-2/3 binding patterns on KSHV latent viral genomes and detailed a significant increase of SUMO-2/3 deposition upon reactivation . MechaniGene expression profiling of the KSHV life cycle using real-time PCR, oligonucleotide arrays, and Northern blotting  has dete5 copies per cell  assay, Interaction of PAN RNA with additional viral factors has also been examined. Through direct interaction with LANA, PAN RNA dissociates LANA from the KSHV genome and disrupts viral latency . Using aThe development of high-resolution chromosome conformation capture (3C)-based methodologies  has estade novo infection. Although de novo infection, detailed mechanisms of the initial high-order structuring and nuclear residences of KSHV episomes are unknown.The looping/conformation model described above has only been studied in the context of latently infected cells and, as such, attempts to explain the maintenance of latency and the latent-to-lytic switch. However, it is currently not known how initial episomal chromatin conformations are established following de novo infection, the KSHV IE protein K-Rta may help viral episomes acquire active histone marks (Genome-wide analysis of KSHV episomes have revealed distinct temporal and spatial chromatin modification patterns on the viral genome. In summary, following ne marks  and cellne marks . The binne marks  and hSETne marks  onto thene marks . Howeverne marks , 2017, rne marks . As the ne marks  and at tne marks  suggestiThe compartmentalization of the epigenetic marks on the KSHV genome might be beneficial for transcription regulation of viral gene expression during its distinct life cycle. The well-organized DNA methylation and different histone modification patterns on the KSHV genome reflects the precise recruitment of cellular chromatin modifying complexes employed by the virus. KSHV latency is essential for persistent infection as well as the development of KSHV-associated malignancies. In recent years, more and more epigenetic modifications and the corresponding modifying enzymes have been identified. Drugs targeting epigenetic modification enzymes have now evolved into a potential viable strategy for controlling persistent viral infections . Thus, eMC wrote and edited the manuscript. W-SY helped writing the manuscript. C-HK constructed the figures. P-CC worked on the constructs and the manuscript writing. WY contributed to the final revision with modification of manuscript and The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


For a long time, the nuclear lamina was thought to be the sole scaffold for the attachment of chromosomes to the nuclear envelope (NE) in metazoans. However, accumulating evidence indicates that nuclear pore complexes (NPCs) comprised of nucleoporins (Nups) participate in this process as well. One of the Nups, Elys, initiates NPC reassembly at the end of mitosis. Elys directly binds the decondensing chromatin and interacts with the Nup107–160 subcomplex of NPCs, thus serving as a seeding point for the subsequent recruitment of other NPC subcomplexes and connecting chromatin with the re-forming NE. Recent studies also uncovered the important functions of Elys during interphase where it interacts with chromatin and affects its compactness. Therefore, Elys seems to be one of the key Nups regulating chromatin organization. This review summarizes the current state of our knowledge about the participation of Elys in the post-mitotic NPC reassembly as well as the role that Elys and other Nups play in the maintenance of genome architecture. Drosophila and mammals. In mammals, this mechanism may be exemplified by the interaction of interferon-inducible genes with the nucleoplasmic Nups [Drosophila, ecdysone-inducible genes interact with the NPC-linked Nups [In eukaryotic cells, the nuclear-cytoplasmic transport of macromolecules occurs through channels in the nuclear envelope (NE) formed by nuclear pore complexes (NPCs). NPCs are large multiprotein structures consisting of ~30 nucleoporins (Nups) each represented by 8–32 copies . Apart fmic Nups . In contked Nups .Drosophila, mammals and nematode [Numerous data suggest that peripheral chromatin interacts with both components of the NE–the nuclear lamina (NL) and NPCs. The NL is a meshwork of lamins and lamin-associated proteins which lines the inner nuclear membrane . A decadnematode ,28,29,30nematode ,40,41,42C. elegans, the single X chromosome in males has a more peripheral position in the nucleus than the two X chromosomes in hermaphrodites and this spatial organization correlates with more pronounced interactions of the single X chromosome with one of the Nups [Drosophila SL-2 cells appears to be more strongly bound with Nups, such as Megator (Mtor) and Nup153, and is located closer to the NE than the autosomes. This proximal position of the X chromosome relative to the NE becomes more distal upon Nup153 depletion [Drosophila S2 cells results in the relocalization of two ecdysone-inducible genes from the NE to the nuclear interior [Several findings indicate that NPCs may also be involved in the anchorage of chromosomes to the NE. In the Nups . Similarepletion . Furtherepletion . Additiointerior . CollectThe involvement of Nups in these interactions with chromatin arouses a particular interest in Elys (embryonic large molecule derived from yolk sac), also known as Mel-28  or AHCTF1 (AT-hook containing transcription factor 1), as the Nup that can bind chromatin directly. Although several recently published reviews have described participation of Nups in chromatin organization ,45,46,47Xenopus, nematode and Drosophila [Drosophila, Elys was not observed to reside at the kinetochores [Drosophila [Drosophila.Elys was initially identified as an AT-hook-containing transcription factor which is strongly expressed in embryonic hematopoietic tissues and more weakly in other mice tissues, localizing to both the nucleus and cytoplasm . Howeverosophila ,52,53,54osophila ,56  group proteins that are enriched with H3K27me3 histone modifications [However, in agreement with the earlier results in human cells , ChIP-seications ,18. In pications . As the ications ,79, Nup9ications ,18. TherNup153 is a component of the nuclear basket of NPCs  and thusDrosophila [Interestingly, the summit in the genome-averaged NPC-linked Nup98 DamID profile exactly corresponds to the dip in the average DamID profile of lamin in osophila , thus inosophila ,85  subunit of the nonspecific lethal (NSL) complex, as well as by H3K4-specific histone methyltransferase Trithorax (Trx) . Moreovepromoter .Drosophila S2 cell extracts. Earlier, the association of Elys with the SWI/SNF subunits was detected in C. elegans [Drosophila larvae brains coincided with Elys-binding regions [Recently, the work of the same group has shed light on this mechanism. The authors have shown that artificial tethering of Nup62 or Sec13 to genomic loci leads to the recruitment of Elys to these sites correlated with decompaction of their chromatin. This was accompanied by transcriptional activation of some of these loci . Additio elegans . PBAP is elegans . Therefo elegans . It seem elegans ,61,70,72 elegans , it is n regions . Accordi regions , ~80% of regions  to the ty-responsive genes after 48 h of interferon induction was shown to be required for the efficient transcriptional reactivation of these genes in HeLa cells. Nup98 binding to these promoters, occurring in the nucleoplasm, is accompanied by the appearance of paused RNA polymerase II and H3K4 methylation at the promoters, both of which are thought to ensure their more effective transcriptional reactivation [Drosophila. The authors have found that the binding of Nup98 with the promoters and enhancers of ecdysone-inducible genes mediates the formation of a chromatin loop between these regulatory elements. This spatial connection of enhancer and promoter is necessary for rapid reactivation of expression of these genes upon repeated treatment with ecdysone. Moreover, Nup98 was found to be associated with the architectural proteins CP190, CTCF and Su(Hw) (suppressor of Hairy-wing) which may also participate in the looping out of chromatin. These findings support a mechanistic model whereby NPC-anchored enhancer-promoter looping of poised genes, preserved after removal of an inductor, may facilitate more rapid reactivation of these genes in response to external stimuli [Similarly to yeast, some of the gene promoters occupied by Nups in metazoans demonstrate transcriptional memory—a more rapid and strong induction of transcription in response to repeated external signals ,16,19,24 stimuli . In thisIt seems likely that the sites on decondensing chromatin, to which Elys is attached during NPC reassembly at the end of mitosis, remain bound with Elys (and with NPCs via Elys) in interphase. The number of nuclear pores as well as the number of NPCs incorporated in the NE doubles during interphase ,100. AltSince the nucleoplasmic fraction of Nups interacts with the broad genomic domains enriched with active chromatin marks, whereas NPC-linked Nups interact with the short genomic sites embedded in LADs, or in active chromatin regions, or in Pc domains ,18, it iIn summary, Elys is the major player in the post-mitotic reassembly of NPCs. Additionally, it partially affects the restoration of other components of the NE at the end of mitosis. Apart from these functions, Elys likely determines the sites by which the genome is attached to the NPCs. A subset of these interactions results in transcriptional memory—more rapid reactivation of induced genes. In addition, Elys together with PBAP may be recruited by other dynamic Nups, such as Nup98, to the active chromatin regions located in the nuclear interior which makes these regions more favorable for transcription. These findings highlight Elys as one of the key factors participating in establishing and maintenance of genome architecture in metazoans.


Although genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers for tumor detection, subtyping, and classification, their direct biological impacts at the individual gene level remain elusive. Here we present MethylationToActivity (M2A), a machine learning framework that uses convolutional neural networks to infer promoter activities based on H3K4me3 and H3K27ac enrichment, from DNA methylation patterns for individual genes. Using publicly available datasets in real-world test scenarios, we demonstrate that M2A is highly accurate and robust in revealing promoter activity landscapes in various pediatric and adult cancers, including both solid and hematologic malignant neoplasms.The online version contains supplementary material available at 10.1186/s13059-020-02220-y. Transcriptional regulation is fundamental to the identity and function of cells. Deregulation of gene expression is a defining feature of common diseases, including cancers. Promoters, the regulatory regions surrounding the transcription starting sites (TSSs), integrate signals from distal enhancers and local histone modifications (HMs) to initiate transcription. Almost half of human protein-coding genes harbor multiple TSSs; consequently, promoter activities determine both the level of transcription and the transcript isoforms that are expressed, with the latter potentially having different translation efficiencies and encoding different protein sequences . Tumors Promoter activities can be determined experimentally through transcriptomic approaches, such as CAP analysis gene expression (CAGE), or through epigenomic approaches, including chromatin immunoprecipitation followed by sequencing (ChIP-seq) . BecauseDNA methylation (DNAm) is a well-studied, relatively stable, and inheritable epigenetic regulatory mechanism that involves transferring a methyl group to cytosine (C) to form 5-methylcytosine (5mC), mostly in the CpG context. In contrast to HMs, DNAm can be accurately and robustly profiled in various tissues, including archival formalin-fixed, paraffin-embedded (FFPE) tumor samples, through both array , 16 and R2 = 0.50–0.66) [RB1, CDKN2A, and MGMT) [The DNAm pattern is mechanistically connected with transcription factor binding and HMs –25. It a50–0.66) –30 and c50–0.66) , 32. Rec50–0.66) , 34. How50–0.66) , 36, the50–0.66) . Conseqund MGMT) , the connd MGMT) –41. Recend MGMT) . UndoubtN = 10, WGBS) from the same cohort. We further confirmed its accuracy and generalizability in diverse tumor types from four publicly available datasets representing real-world applications, including (1) pediatric rhabdomyosarcoma (RMS) O-PDX tumors profiled in the Pediatric Cancer Genome Project  [N = 9, WGBS) [N = 19, WGBS) [N = 140) [To address these challenges, we have developed MethylationToActivity (M2A), a deep-learning framework. The central hypothesis of M2A is that the complex relation between DNAm signatures and promoter activities (measured as H3K4me3 and H3K27ac enrichment in the TSS ± 1 kb region) can be captured by incorporating both summary statistics extracted from window-based CpG methylation levels and high-order spatial information from these windows in the promoter and flanking regions (up to 25 kb from the TSS). Using a cohort of six pediatric neuroblastoma (NBL) orthoptic patient-derived xenograft (O-PDX) samples profiled in the Pediatric Cancer Genome Project (PCGP) , we trai6, WGBS) ; (2) a s9, WGBS) ; (3) pri9, WGBS) ; and (4)N = 140) . These aMYCN amplification with no other major oncogenic mutations. All samples displayed a bimodal distribution of promoter H3K27ac levels across the genome . However, there were extensive variations in the promoter activities in both the cell line models  or between two O-PDX models   between each feature (both raw input and CNN-extracted features) and the response variable (H3K27ac) in the training set, The analysis revealed that CNN-extracted features have significantly higher R2 with the response , poised (high H3K4me3 and low H3K27ac), and inactive promoters (low H3K4me3 and low H3K27ac) in the six NBL O-PDX training samples. These features show distinct patterns among the three promoter categories    from the next highest performer by 17.8%  and 12.4%  for the model topologies for H3K4me3 and H3K27ac, respectively , including one O-PDX tumor, one primary autopsy tumor, and eight cell lines. Using the validation set, we compared the performance of the M2A framework of three CNN layers and two FC layers . Conversely, M2A produced highly accurate promoter activity landscapes, significantly outperforming the observed consistency between training and testing samples for both H3K4me3  and H3K27ac . The same pattern was observed for H3K4me3 levels, with M2A being more accurate for nine of 10 samples , demonstrating the accuracy of M2A in revealing individual tumor promoter activity landscapes. Finally, the predictive accuracy of M2A was comparable to the experimental consistency observed between replicates from the same cell lines profiled in ENCODE for H3K4me3    in real-world applications: (1) generalizability, i.e., M2A needs to achieve a similar performance with a set of unseen test samples, including tumor/tissue types not used in the model training, and (2) scalability, i.e., M2A must be able to be applied efficiently to external data.R2 = 0.937 ± 0.017, P = 0.30; RMSE = 0.639 ± 0.119, P = 0.82, Wilcoxon rank-sum test)   and H3K27ac . The accuracy of the inferred H3K4me3 activity was comparable to the inter-replicate consistency of the ENCODE samples .We first demonstrated the accuracy, generalizability, and scalability of M2A by using test samples from rhabdomyosarcoma (RMS) O-PDX tumors. The RMS O-PDX dataset consists of 16 pediatric RMS tumors . As with the NBL cohort, each RMS sample was extensively profiled, including by WGBS, RNA-seq, and ChIP-seq of H3K4me3 and H3K27ac. Using the vanilla M2A model (the 3CNN-FC model trained on the six NBL PDX samples), M2A achieved an overall predictive accuracy with the RMS dataset that was comparable to that of the NBL test group for both H3K4me3  between the promoter H3K27ac level of a random RMS test tumor and the most similar NBL training tumor  and the RMS test set (R2 = 0.715 ± 0.044) . Neural networks often fall victim to this problem through a combination of factors, including relatively small training datasets and/or over-parameterization. The relatively consistent expression of housekeeping genes across different tissues may lead to an inaccurate (often inflated) interpretation of the performance measurement in such a model, as evidenced by the relatively high https://github.com/chenlab-sj/M2A), the training of the vanilla M2A model (with six NBL O-PDX tumors) takes approximately 16 min (using a Tesla P100-16GB GPU). The feature extraction and promoter activity prediction from WGBS data (as a genome-wide DNAm level file in a tab-delimited text format) can be executed on a personal computer  and takes 15–19 min. Moreover, we have implemented a cloud version of M2A (https://platform.stjude.cloud/workflows/methylation-to-activity), available to the general research community.M2A is efficient and scalable. For a local implementation of M2A   . This analysis demonstrated the value of both the pretrained CNN layers for general feature extraction and a single profiled sample in the target domain. Consequently, we applied transfer learning to both the EWS and AML datasets. However, transfer was not feasible in the ENCODE dataset because those cell lines were derived from different tissues.In M2A, the CNN layers capture generalized DNAm features and the FC layers learn the mapping function between the DNAm features and the promoter activities. Here we start with the pretrained vanilla M2A model, fix the feature-extraction layers (CNN layers), and use a single sample from the target tumor type to update the mapping function (the weights and biases of the FC layers). Because the consistency of M2A for H3K4me3 approached the inter-replicate consistency in both NBL and RMS datasets, we focused on H3K27ac inference for transfer learning. Upon performing transfer learning with a single sample in the RMS dataset, we observed significantly improved accuracy . However, the predicted activities remained highly correlated with the experimental measurements    . Strikingly, the consistency between the observed promoter activity and gene expression was highly predictive of the performance of M2A with individual samples    primary patient samples collected by the BLUEPRINT consortium, which is part of the International Human Epigenome Consortium (IHEC) . AnalyseR2 = 0.713 for DE genes with a single annotated promoter; 0.621 when all annotated promoters for DE genes were included)   is a primary research focus in cancer epigenome studies . To thisGAS2 is a gene selectively expressed in ERMS [P = 0.01, Wilcoxon rank-sum test)   Fig. b, d. Simst) Fig. c, e, conAlthough DNA methylation patterns, including differentially methylated regions (DMRs), are well-established biomarkers for diverse diseases and have revealed molecularly and clinically different subtypes for many cancers, their functional importance in individual gene regulation is less clear –41. For To summarize unambiguously the contribution of DMRs to differential promoter activities, we focused on 371 genes in ERMS and ARMS that have a single annotated promoter and that are both differentially expressed  and differentially methylated  in the two major RMS subtypes . The M2A-inferred promoter activity landscape revealed 276 genes for which there was a significant difference in promoter usage between the subtypes , and 210 of them matched the ground truth   is a known target of the PAX3/7–FOXO1 fusion protein [PDZRN3. Functional studies have shown that PDZRN3 regulates myoblast differentiation into myotubes through transcriptional and posttranslational regulation of Id2 [PDZRN3-006) located 191 kbp downstream of the canonical promoter (PDZRN3-001)  Fig. a. The su01) Fig. a. ComparR2 = 0.718, 0.628, and 0.702 for the three samples with HM profiles, using leave-one-out prediction)   , and five candidate gene isoforms , and we followed this with backwards stepwise model selection. The final model , a marginally protective role for candidate transcript RET (ENST00000479913), and a candidate transcript associated with a poor prognosis, SLC27A6 (ENST00000508645).Ewing sarcomas with mutations in rognosis . We explTNS1 encodes the well-studied protein Tensin 1 and is involved in several key aspects of cell function, including extracellular matrix formation, actin cytoskeleton formation, and signal transduction [TNS1 expression [TNS1 is a candidate prognostic indicator for EWS. Further studies are needed to draw more attention to the functional roles of these genes/transcripts in EWS progression.sduction , 65. Morsduction , althougpression  is assocMYCN-amplified NBL tumors revealed both commonly active promoters and promoters that were active in some tumors but not in others. Moreover, these sample-specific active promoters are functionally important, as they drive the expression of several cancer consensus genes, including MYC. This observation is consistent with recent reports of heterogeneous enhancer activities of cell line-defined super-enhancers in primary gastric cancers [Although epigenetic studies in disease models  and in a limited number of primary tumor samples have demonstrated the oncogenic contributions of epigenetic deregulations to cancer initiation, progression, and response to treatment , 68, 69, cancers , emphasiMethylationToActivity allows us to answer several questions that have been impossible to address previously. For example, landscapes of comprehensive epigenetic deregulations in promoter regions can be inferred in large primary patient tumor cohorts preserved in FFPE, which previously has been intractable due to technical and financial challenges. The M2A inferred promoter activity landscape will greatly increase sample size, allowing robust statistical analysis to identify recurrent epigenetic deregulations in promoters.R2 = 0.668 for ENCODE data, 0.722 for K562, 0.705 for GM12878, and 0.536 for H1-ESC) surpassed that with the state-of-art BPR model [R2 values were 0.49 for K562, 0.37 for GM12878, and 0.25 for H1-ESC). Strikingly, the correlation of M2A’s predicted promoter H3K27ac activities with gene expression levels across nine ENCODE cell lines (average R2 = 0.668) was comparable to the predictive accuracy of a model built on 11 HMs, one histone variant, and DNase I hypersensitivity [Although our framework was strictly trained on HM levels, the inferred promoter activity was highly correlated with the transcript-based gene expression levels quantified by RNA-seq  high-level features from DNAm patterns that are common among different tumors and (2) tumor subtype-specific mapping functions from the mapping of high-level DNAm features to promoter activities in individual tumor subtypes by using transfer learning (when feasible). Although our deep-learning model cannot establish a causal relation between DNAm and promoter activities, these findings nevertheless shed light on both the general and tumor subtype-specific rules for interpreting DNAm patterns.P = 0.0039, Wilcoxon signed-rank test, Additional file In evaluating our predictions, we found that several samples (the “poor performers”) showed abnormally low predictive accuracy with both the ENCODE and BLUEPRINT datasets. Investigations of the promoter H3K27ac levels revealed that the fraction of active promoters in these samples was substantially lower than in other samples. Furthermore, joint analyses with RNA-seq data from the matching samples indicated that 1) in contrast to the predictive accuracy of H3K27ac levels, the “poor performers” achieved comparable consistency between the M2A-inferred promoter activity and gene expression; (2) the “poor performers” showed significantly lower correlation between the measured promoter H3K27ac level and gene expression; (3) the correlation between the measured promoter H3K27ac level and gene expression was highly predictive of the accuracy of the H3K27ac prediction . This demonstrates the importance of analyzing alternative promoter usage in epigenomic studies.Alternative promoter usage increases the transcriptomic diversity during normal tissue development and oncogenesis. Recent work demonstrated that an alternative promoter of Finally, our analysis quantitatively emphasizes that promoter activity is one of the mechanisms that regulate the final transcriptional output: (1) both the observed and predicted promoter activities account for 50–70% of the variation in gene expression, and (2) approximately 40% of the genes differentially expressed in ERMS and ARMS tumors show significant differences in their promoter activities. In addition to promoter activities, other epigenetic mechanisms, including enhancer activities, contribute substantially to gene regulation . Recent We have demonstrated that MethylationToActivity overcomes the unique challenges of systematically characterizing promoter activities from DNA methylation signatures. It achieved an accurate, robust, and generalizable performance in various pediatric and adult cancers, including both solid and hematologic malignant neoplasms. MethylationToActivity will serve as a valuable tool to provide functional interpretation of DNAm deregulation, characterize promoter activity differences from DNAm patterns, and reveal alternate promoter usage in patient tumors, which will facilitate precision medicine by tailoring treatments based on both genetic variants and epigenetic deregulation.N = 16) [N = 9) [N = 19) [N = 140) [Five separate publicly available datasets were used in this study, including a pediatric NBL O-PDX dataset (N = 16) ; an RMS N = 16) ; ENCODE  [N = 9) ; a DCC B[N = 19) ; and a pN = 140)  (Additiowww.gencodegenes.org/); the NBL, RMS, ENCODE, and EWS datasets were evaluated using GENECODE GRCh37.p13 (release 19), and the AML dataset was evaluated using GENCODE GRCh38.p13 (release 32). Promoter regions are defined as the TSS ± 1 kbp, where the TSS is defined as each unique transcript start position. To avoid using identical or near-identical promoter regions in training and baseline performance, only TSSs with promoter regions with less than 50% overlap were considered. Gene orientation was taken into account, and any promoters with overlying regions but opposite orientations were not considered as overlapping. Because of differences in sex among samples, all chromosome X and Y promoter regions were removed from consideration. This resulted in a total of 96,756 and 104,722 non-overlapping promoter regions from the annotation files GRCh37.p13 and GRCh38.p13, respectively. For gene expression analysis, we followed the definition in [All datasets were evaluated using GENCODE annotation definitions  and a minimum value of where MethylatedWij = {Wi1, Wi2, Wi3…, Wi20} is the vector of windows where i represents a particular TSS and j represents a particular window corresponding with the ith TSS. This means that Wi10 and Wi11 represent the windows immediately downstream and upstream of the ith TSS. Therefore,M2A uses a promoter region-based windowed approach, comprising 20 windows of two sizes (250 bp and 2.5 kbp) and a step size equal to the window size, centered on a given TSS. For instance, Wi2 = TSSi − 9th window, …M-value mean, variance, and the fraction of the SSD of M-values (FSSD), were calculated and represented by the feature vectors Mavei, Mvari, and Mfssdi. Therefore, the features for a particular window, denoted as Wij, would be calculated as follows:Each feature was calculated in this manner. The DNA methylation features, including the windowed i represents the promoter, j represents a specific window for a particular promoter, and Mvalk represents the Mval for individual CpGs in a region where Mvalk(j) is the Mval for an individual CpG in a specific window. Each feature was interleaved by window size to provide model input wherein each window contained a number of “channels” equal to the number of features, with the feature array shape being , where N represents the total number of TSSs in a sample, 2 represents the number of window sizes (250 bp and 2.5 kbp), 20 represents the number of windows, and 4 represents the number of features per window. All features were scaled from 0.1 to 1 ; in instances where windows overlapped regions without methylation data, resulting in NaNs , these feature values were marked as 0.Here, ith promoter region is calculated as follows:The response variable was calculated for each non-overlapping promoter region, a 2000-bp region centered on each TSS. Histone modification enrichment (HM) for the α represents the 25th percentile of the where M2A is a machine learning framework that leverages canonical deep-learning strategies, including convolutional neural network (CNN) and fully connected (FC) layers. Each layer employs a LeakyReLU  and a kernel constraint by L2-normalization using maxnorm (3). CNN layers are two-dimensional, with zero padding, a stepsize of , and a kernel size of  to maintain feature space and prevent convolutions across features from different window sizes. To test the efficacy of this approach, we compared the performance of a traditional artificial neural network (ANN) consisting of two FC layers versus three CNN layers in addition to two FC layers. During transfer learning, weights corresponding to each of the three CNN layers of the six-NBL O-PDX M2A model trained previously were frozen; only the weights corresponding to the two FC layers were optimized. A summary of all model topologies and parameters can be found in Additional file n = 10). Each parameter configuration was tested holding all other parameters constant, and models with a numerical performance advantage were chosen. For batch size, three configurations were tested . Four kernel size configurations  training set consisted of six O-PDX samples from the 16-sample NBL cohort  . To avoid artificially inflated values from genes with multiple TSSs, only cancer consensus genes with a single TSS according to GENCODE GRCh37.p13 (release 19) definitions were considered.Promoter region-based H3K27ac distributions clearly show a bimodal distribution . To measure the accuracy of M2A in predicting expressed genes, we calculated the AUC-ROC by using roc_curve and the precision-recall curve AUC by using average_precision_score from sklearn 0.22. Paired analyses were tested for statistical significance by using a Wilcoxon signed-rank test (R v3.4.1). To determine outliers, a median-based method was implemented using the “outlier” function in the R package GmAMisc [http://genome.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeMapability). For GRCh38.p13 annotations, we used liftOver from UCSC (http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/) to convert the mappability track to GRCh38. For all performance-related analyses, the average value of this track within each non-overlapping 2000-bp promoter region was calculated, and promoter regions with mappability > 0.75 were retained for evaluation.When determining prediction performance, two primary metrics were considered, namely the  GmAMisc . To ensuIn analyses between H3K27ac levels  and gene expression, we followed the gene filtering steps described in : all autMultivariate adaptive regression splines (MARS) was implemented by “earth” package in R , and the random forest baseline model was implemented by the “sklearn.ensemble. RandomForestRegressor” package in python 3.7.0 (max_features = ‘sqrt’). For comparison purposes, each model tested used identical feature input to M2A.The lists of genes that are differentially expressed in ERMS and ARMS samples and genes with DMRs were previously reported in reference .H3K27ac) > 1 for the average samples in the subtype. Next, primary promoters from multi-promoter genes were determined by the average H3K27ac level within a specific subtype, and the promoter with the maximum activity from a given gene was counted as the primary promoter. The differences in promoter usage between two subtypes were defined as the difference between the activity sum of the primary promoters and the activity sum of the secondary promoters in the two subtypes.To infer alternative promoter usage that was specific to the RMS subtypes ARMS and ERMS, we first delineated the “active” vs. “inactive” promoters in a subtype (ERMS or ARMS) by applying a threshold of mean using Cox proportional hazard models (R 3.4.1). The final model was derived from backward stepwise selection from a Cox proportional hazards model including TP53 and STAG2 mutation status and all potential markers .In the same manner as the RMS subtype analysis, EWS alternative promoter usage was determined in two patient sample groups: N = 6) and stratified by promoter status. Promoter status was determined by class occupancy of both H3K27ac and H3K4me3 (active versus inactive), where (H3K27ac = active), , and , represent active, poised, and inactive promoters, respectively. Class occupancy was determined by applying a Gaussian mixture model (GaussianMixture from sklearn version 0.22 with n_components = 2).To determine the merit of a CNN-based approach, each feature average for a particular window position and window size were plotted with a 95% confidence interval. The plotted feature distribution was calculated from all M2A vanilla model training data input  and (2) the performance (Pearson’s R2) of the best performing feature identified in (1) when applied to each sample in the validation set  training sample input feature predictive performance as compared to CNN-extracted feature performance, calculated by Pearson’s  trainingN = 16) [N = 16) [https://www.encodeproject.org/) with matching H3K27ac and H3K4me3 histone mark ChIP-seq, RNA-seq, and WGBS experimental data (N = 9) [N = 19) [N = 140) [The datasets used in this report have been published, including a pediatric NBL O-PDX dataset (N = 16) , 75; an [N = 16) , 76; ENC (N = 9) , 77; a D[N = 19) , 78; andN = 140) , 79, 80.https://github.com/chenlab-sj/M2A, and the latest release is hosted by Zenodo [https://platform.stjude.cloud/workflows/methylation-to-activity).The latest M2A models, feature generation, prediction pipeline, and a Docker image of the M2A environment pre-loaded are available for download at y Zenodo , under tAdditional file 1: Figure S1. NBL sample H3K27ac promoter distribution. Figure S2. DNAm input feature pattern analysis. Figure S3. Feature performance comparison: Input features vs CNN mapped features. Figure S4. M2A prediction generalizability analysis. Figure S5. M2A with transfer learning outperforms a vanilla M2A model of the same cancer type. Figure S6. Signal-to-noise analysis of ENCODE and NBL datasets. Figure S7. M2A ENCODE cohort performance. Figure S8. Analysis of outlier H1-ESC. Figure S9. Predicting gene expression in the ENCODE dataset. Figure S10. Consistency of gene expression and H3K27ac promoter levels in the AML cohort. Figure S11. M2A accurately determines subtype differences between embryonal and alveolar RMS. Figure S12. Kaplan–Meier log-rank analysis by mutation status in EWS. Figure S13. CpG distribution by window relative to the TSS.Additional file 2: Table S1. H3K27ac active cancer consensus genes in 3 NBL cell lines, and 3 NBL O–PDX samples. Table S2. Baseline models vs. vanilla M2A predictive performance comparison. Table S3. M2A predictive performance in NBL cell line samples. Table S4. Observed H3K27ac and H3K4me3 ENCODE replicate consistencies. Table S5. M2A predictive performance in RMS O–PDX samples. Table S6. M2A RMS transfer model predictive performance in RMS O–PDX samples. Table S7. M2A predictive performance in ENCODE dataset. Table S8. M2A predictive performance in AML samples. Table S9. ERMS vs. ARMS DMRs and associated genes (overexpressed in ERMS). Table S10. ERMS vs. ARMS DMRs and associated genes (overexpressed in ARMS). Table S11. M2A alternate promoter usage predictive performance between ARMS and ERMS samples. Table S12. M2A predictive performance in EWS samples, before and after transfer. Table S13. A univariate survival analysis of differential H3K27ac promoter activity between TP53 mutant and TP53 wild–type EWS tumors. Table S14. A univariate survival analysis of alternate promoter usage between TP53 mutant and TP53 wild–type EWS tumors. Table S15. ENCODE H3K27ac replicate consistency with gene expression. Table S16. Dataset availability. Table S17. M2A model topologies. Table S18. Parameter tuning: mean performance in the NBL validation set (R2). Table S19. Sample summary information.Additional file 3. Review history.


Deep work has been made in the last years to highlight the combinations of histone marks that concomitantly occur within a nucleosome, with the new front-line focused to evidence whether a specific modification is present on one or on both residues of each histone pair. Accordingly, growing evidence has accumulated on the existence and function of the so-called nucleosomal “bivalent domains” where activating and repressive PTMs added on different residues coexist at the promoters of developmentally regulated genes in embryonic stem cells that resolve this apparently contradictory pattern upon differentiation5.Nucleosomes, the basic structural units of chromatin, consist of a 147 bp DNA fragment wrapped around a protein octamer containing two copies each of histones H2A, H2B, H3, and H4. Histones exhibit a variety of posttranslational modifications (PTMs) that are recognized by multimeric effector proteins and act in concert to control gene expression6. This residue is peculiar, as it can be also subjected to acetylation, behaving, under this condition, as an activating signal for transcription7. Therefore, we questioned whether nucleosomes at the promoters of inducible genes showed, when not stimulated, both lysines methylated or if one residue was unmodified or, more interestingly, acetylated, presumably as an outcome of previous transcriptional activation. To address this issue, we have analyzed by chromatin immunoprecipitation (ChIP) the promoter of pS2 gene in the human breast MCF-7 cells, that is not transcribed in the absence of estrogen (E2) and can be activated by hormone addition to the culture medium8.We asked whether asymmetric nucleosomes are present also in somatic cells and pointed our attention on one of the best characterized inhibitory PTMs, trimethylated lysine 9 in histone H3 (H3K9me3)5  at pS2 promoter, also accompanied by targeting of the p300 histone acetyltransferase (HAT)10 and appearance of phosphorylated serine 10 in the same histone (H3S10ph), a mark that labels active genes11. Reported ChIP assays revealed that pS2 promoter was occupied by nucleosomes with H3 histones where lysine 9 was either methylated and acetylated, even though with changing percentages throughout the examined time-course. Therefore, to be sure that we were observing bivalent nucleosomes and to exclude that each lysine 9 was differently marked in any of the two allelic pS2 genes, we performed sequential ChIP (Re-IP) experiments by reimmunoprecipitating H3K9me3 immune-complexes with antibodies to H3K9ac. Our results supported the combinatorial model in which acetylation, gained through recruitment of p300, and trimethylation of K9 were present within the same nucleosome that also harbored H3S10ph  should work as a more unambiguous approach. As a first step, to avoid the stabilization of any looping, we digested native (not cross-linked) chromatin with micrococcal nuclease (DNase) to generate mononucleosomes that were, then, purified by sucrose gradient in order to collect excluysively mononucleosomes5 Fig. . As show0ph Fig. , left.Fi12. Interestingly, the same nucleosome showed an H3S10ph whose presence has already been reported to prevent methylation of the preceding lysine13. We, then, speculated that asymmetry emerged after the first cycles of gene expression and, to test this hypothesis, we analyzed the estrogen receptor α− (ERα−) breast MDA-231 cells that, being unable to respond to E2 challenge, should lack histone asymmetry at the pS2 promoter. As predicted, these cells, even under estrogen stimulation, showed a stable level of H3K9me3 and absence of H3K9ac, with the switch (together with targeting of p300 and appearance of H3S10ph) generated exclusively after internalization of exogenous ERα by transfection  while enhancing that of HATs14, only after 20 min of E2 challenge  cells, showed the appearance of H3K9ac and H3S10ph, a mark, by the way, whose pre-requisite is demethylation of the preceding residuenge Fig. . In factnge Fig. .14. Successively, one of the two serines is dephosphorylated and the adjacent lysine can be, thus, remethylated, representing the basis for next cycles of demethylation by the specific demethylases with oxidation of nearby guanines that allow transcription6. We speculate that, leaving only one of two lysines 9 to be demethylated helps the machinery triggered by recruitment of ERα to find the right direction for transcription, as oxidation of guanines has been reported to have consequences also on DNA methylation in a strand-specific manner15.According to our data, we propose the following model: when the estrogen receptor triggers the first transcriptional cycle, it induces demethylation of both lysines 9, a condition permissive for assembly of the transcriptional machinery. This step is followed by phosphorylation of serine 10 in the same histone that acts as a barrier to fast remethylation6. This model appears to be not restricted to the reported estrogen-responsive gene, as we have evidences that a very similar picture is present on the paradigmatic retinoic acid target gene cyp26A1. Incidentally, we remind that the two hormones have the opposite effect on cellular life and, then, we can imagine this as a more general phenomenon, by which cells should be able to respond quickly to cognate stimuli. We are aware that our data need further investigation at a genome-wide level and deeper analysis of the molecular mechanism elicited; however, we are persuaded that highlighting the existence of somatic nucleosomal asymmetry that, in particular, is characterized by two opposite marks deposited onto the same residue, could be significant per se.In this study, we show the first example of histone asymmetry affecting the same residue subjected to modifications with an inverse effect on gene expression. Interestingly, the involved residue is the one that drives estrogen-induced transcription through generation of reactive oxygen species (ROS) triggered by its demethylation


OPG and RANKL level by decreasing the expression level of OPG and increasing level of RANKL. Here, were aimed to decipher whether glucocorticoid receptor directly influences RANKL promoter activity and its transcriptional regulation. We demonstrate that overexpression of glucocorticoid receptor (GR) NR3C1 increased RANKL promoter activity in human osteosarcoma, cervical cancer (2-fold) and adenocarcinoma cells (4.5-fold). Mutational analysis revealed that +352 site in the RANKL promoter is functional glucocorticoid responsive element (GRE) since the effect of GR on RANKL promoter activity was diminished by mutation at this site. Overexpression of NR3C1 upregulated RANKL mRNA expression 1.5-fold in human A549 and HOS cells. On the other hand silencing of NR3C1 caused slight decrease in RANKL mRNA level, suggesting that NR3C1 directly accounts for RANKL transcriptional regulation. Using electrophoretic mobility shift assay we demonstrate that NR3C1 binds to the proximal RANKL promoter region. Our study provides evidences that NR3C1 directly upregulates RANKL transcription in human cell lines and connects the missing link in the mechanism of RANK/RANKL/OPG imbalance of glucocorticoid induced osteoporosis.Glucocorticoid osteoporosis is a serious side effect of long term glucocorticoid uptake and it is caused by osteoblast apoptosis and imbalance in the major bone remodeling pathway RANK/RANKL/OPG. The impact of glucocorticoid on the maintenance of RANK/RANKL/OPG is well explored; dexamethasone was shown to disturb the ratio between  RANKL in osteoblasts, increases RANKL secretion that activates osteoclasts formation and an increase in bone resorption [RANKL expression in bone tissues is upregulated, which leads to stronger bone resorption and decrease in bone mineral density [The Glucocorticoid induced osteoporosis (GIO) is the most frequent cause of secondary osteoporosis ,2,3. Patsorption . Osteopr density .TNFSF11 gene. It is found as membrane-bound and soluble forms, the latter of which is produced by alternative splicing or by proteolytic cleavage of membrane-bound RANKL [RANKL is expressed in cells involved in bone formation , immune cells, malignant cells, and other cell types [RANKL expression is regulated by parathyroid hormone (PTH), vitamin 1,25(OH)2D3, Runt-related transcription factor 2 (Runx2), interleukin (IL)-6-type cytokines [RANKL gene expression is also mediated by gender specific transcription factor SRY [RANKL belongs to the tumour necrosis factor (TNF) family of cytokines, and is encoded by nd RANKL ,14,15. Rnd RANKL ,17,18,19ll types . RANKL ill types ,23,24,25ytokines ,27, moleytokines ,33,34,35ytokines . We democtor SRY .NR3C1 gene, which is transcribed into two isoforms, GR-α and GR-β that modulate transcriptional regulation either directly or through transactivation with other transcription factors [Glucocorticoid receptor (GR) has been found in most of the human cells and can regulate transcription of hundreds of genes depending on the pathophysiological state of cell ,37. GR i factors . When gl factors . Besides factors ,40.RANKL level [RANKL expression varies between the cell types depending on their nature of origin [OPG [OPG and upregulated RANKL [OPG and RANKL expression is influenced by time and concentration of dexamethasone [OPG and increase RANKL expression level [RANKL gene expression in bone-marrow-derived osteoblastic/stromal cell line ST2, whereas OPG was strongly downregulated in this cell line [RANKL promoter region suggesting that GC can directly impact RANKL transcriptional regulation [RANKL expression in osteoblasts implying that GC upregulate RANKL expression through monomeric GR [RANKL expression is influenced by GCs that act through GR pathway. Whether GR activates RANKL promoter directly by DNA binding or by tethering with other transcription factors remains unknown.Glucocorticoid dexamethasone was shown to cause imbalance in RANK/RANKL/OPG pathway and bone resorption by reduction of osteoprotegerin (OPG) level and also by modifying KL level ,41,42. Tf origin . In calvgin [OPG . In humaed RANKL ,43. The ethasone . In humaon level ,45. Dexaell line . Kitazawgulation . The autgulation . Moreovemeric GR . All theRANKL expression. We demonstrate that GR increases human RANKL promoter activity in human A549, HeLa and HOS cells via at least two GRE sites in the human RANKL promotor region; −642, and +352 relative to transcription start site (TSS). We provide an evidence that GR activates RANKL promoter through GRE site at +352, since the mutated promoter was not affected by overexpression of GR. This is the first study demonstrating that GR can directly regulate human RANKL gene by binding to its proximal promoter region. We show that GR caused upregulation of RANKL in human adenocarcinoma A549 and human osteosarcoma HOS cells. Altogether, our results indicate that GR directly activates human RANKL promoter and regulates RANKL expression.Here, we explored whether GR directly upregulates human RANKL promoter activity and its transcriptional regulation. Since the expression of RANKL gene plays a crucial role in bone remodelling processes and it is modulated in various cancers [RANKL promoter, cells were co-transfected with plasmids encoding glucocorticoid receptor NR3C1 (pCMV-NR3C1) and pGL3 plasmid with 898 bp RANKL proximal promoter region (plasmid pGL3-RANKL-100 (region −789 to +100 relative to transcription start site [p = 0.0008) in comparison with an empty vector (p = 0.0063) increase in promoter activity in comparison with an empty vector . Promoty vector a. In HeLy vector b. In HOSnificant c. Overexnificant d. IncreaRANKL exon 1 region +342\+352 relative to transcription start site  (RANKL promoter. Promoter activity of plasmid pGL3-RANKL-100 (region −789 to +100 relative to TSS) increased 1.7 fold in comparison with an empty vector (p = 0.0376), pGL3-RANKL-183 (region −789 to +183 relative to TSS) 1.9 fold (p = 0.0024) and plasmid pGL3-RANKL-370 (region −789 to +370 relative to TSS) which includes novel GRE site 4.5 fold (p = 0.0224) in comparison with an empty vector (RANKL promoter region that includes putative GRE site at +352 (pGL3-RANKL370) was 2.3-fold higher than that of a shorter promoter region (−789 to +183) (pGL3RANKL-183), suggesting that putative GRE site at +352 is a functional GRE site and that overexpression of GR increases RANKL promoter activity by binding to this site. Promoter activity of RANKL promoter length mutations also increased in the presence of GR in HOS cells, yet changes were not significant  a. A549 ay vector b. Promotnificant c.RANKL promoter activity, we introduced mutation in the putative GRE site (+352 relative to TSS) (pGL3-RANKL370 GREmut plasmid) and performed luciferase assays in A549 and HOS cells. Overexpression of NR3C1 did not cause significant change in promoter activity of pGL3-RANKL370-GREmut in comparison with an empty vector in any of the tested cells confirming that GRE at +352 is important for induction of RANKL promoter activity by NR3C1. In fact, introduction of mutation at GRE site caused 7.9-fold (p < 0.0001) and 1.8-fold (p = 0.009) lower promoter activity in the presence of GR in A549 and HOS cells, respectively  is important for GR activation of RANKL transcription in HOS and A549 cells. Moreover, endogenous transcription factors are responsible for activation of RANKL promoter (in the region +183 to +370), and this effect is potentiated by overexpressed GR.Next, to examine whether GRE site directly influences the ectively . InteresRANKL proximal promoter region, we performed electrophoretic mobility shift assay. For that purpose, HEK293T cells were transfected with pCMV-NR3C1 plasmid and nuclear extracts were isolated. Nuclear extracts with overexpressed GR or prepared from untransfected cells were incubated with biotinylated probe with GRE sequence at position +352. In parallel, incubations with unlabelled competitive probe (the same sequence as biotinylated probe) and with mutated probe (with mutations at the GRE +352 site) were performed. In case a protein binds to the biotinylated probe, a shift on the gel appears. In the presence of nuclear extract from cells with overexpressed GR, a shift of biotinylated oligonucleotides appeared indicating direct binding of a protein from the nuclear extract to the biotinylated probe  and 1.6-fold (p = 0.0163) upregulation of RANKL in A549 and HOS cells, respectively  in the presence of NR3C1 in comparison with an empty plasmid  change in NR3C1 level was observed, which caused 0.38 fold change in RANKL expression  and putative GRE site at +352 relative to transcription start site. Addition of GR caused higher increase in promoter activity of the region overlapping putative GRE site at +352 in comparison with the region overlapping only putative GRE site at −679. Therefore, our results confirm the results obtained with a mouse RANKL promoter [RANKL promoter. Moreover, our results demonstrate that GRE residing in the untranslated region of RANKL promoter is more important for activation of RANKL promotor activity with GR than GRE at −679 site. Namely, +352 region was more inducible by GR than previously reported putative GRE site at −679. Indeed, promoter activity of the region from −789 to +370 in the presence of NR3C1 was higher in comparison with a shorter region from −789 to +100 suggesting that GRE in the untranslated region plays an important role in transcriptional regulation of RANKL. Using EMSA, we demonstrate that GR from the nuclear extract directly binds to this +352 region of the RANKL promoter confirming the importance of the newly identified GRE in the regulation of RANKL gene. In one fifth of the genes that bear GREs, GR binding sites have been reported to reside upstream from transcription start site, yet small portion of GREs have been also identified in the first intron and exon [RANKL gene. Moreover, several GREs in a single gene have also been identified in other genes, which probably enhances the responsiveness to GC [RANKL gene in A549 cells treated with 100 nM dexamethasone [RANKL expression is very complex. There are at least two regions in the proximal RANKL promoter region that can be directly affected by GR. This provides important knowledge in the understanding the complexity of glucocorticoid induced osteoporosis.Given that GR affects promoter  and idenand exon , which wss to GC . Our resethasone . Our resRANKL mRNA level which is similar to previously reported results obtained on dexamethasone treated human primary osteoblast [RANKL in stromal ST2 cells. Dexamethasone was also shown to act synergistically with vitamin D3 to enhance osteoclast formation by increasing RANKL mRNA expression in bone marrow stromal primary and transformed cells [RANKL expression differ depending on the investigated cell types and experimental setups. Comparison between activation of RANKL promoter in three different cell types revealed that there is significant difference in transactivation of RANKL promoter in the presence of GR between cell systems and that fold of activation differs between cells. Our results indicate that transcription machinery of particular cell system affects RANKL promoter activity and corroborates with previous results that GC differently affect RANKL expression in different cell types. Besides, different transcriptional machinery, the existence of various mechanism by which GR can transactivate promoters, contribute to those differences.In our experimental setup, in human osteosarcoma and adenocarcinoma cells, addition of GR caused an increase in teoblast  and mousteoblast ,47. It wed cells . ReportsRANKL expression 6 days after treatment, and decrease in OPG mRNA level and was also confirmed at the protein level [RANKL indicating that dexamethasone and vitamin D3 upregulate RANKL transcription via GR transcriptional activation. Interestingly, in mice where dimerization of GR was hindered, dexamethasone and vitamin D3 still caused upregulation of RANKL implying that monomeric GR suffice for RANKL upregulation and enhanced osteoclastogenesis [RANKL promoter with one conserved GRE site (CAGCGTCGCCCTGTTCTTC), which mutation alleviates promoter activation, probably due to hindered binding of GR to DNA. Therefore, we propose that one of the mechanisms by which GR upregulates RANKL expression is via direct binding of monomeric GR unit to the +352 GRE site of the RANKL promoter. Further studies to elucidate whether other transcription factors are involved in this transactivation are required.In vivo relevance of our results was provided by mouse model study, where mice treated with vitamin D3 and dexamethasone showed potentiated osteoclast formation . Treatmein level . The levogenesis . Here, wRANKL level. We demonstrate that overexpression of GR caused activation of RANKL promoter and an increase in RANKL expression in human lung cancer and osteosarcoma cells; probably through direct binding to RANKL promoter at the region +352. We provide a link between upregulation of RANKL in patients treated with GC and activation of RANKL promoter. Our results imply that GC directly influences RANKL expression via GR induced activation of RANKL transcription.In glucocorticoid induced osteoporosis, GC cause bone loss through decrease in osteoblast formation and stimulated osteoclastogenesis. For both processes, balanced ratio in RANK/RANKL/OPG is essential. Here, we focused on the deciphering mechanism behind increase in RANKL promoter region (from −798 bp to +370 bp relatively to the transcription start site) was PCR amplified from the human genomic DNA with Q5® High-Fidelity DNA Polymerase  using RANKL370 f and RANKL370 rev primers  using primers listed in the Human proximal  primers  and clon® CRL-1543™), human cervical cancer HeLa cells (ATCC® CCL-2) and human lung cancer A549 cells (ATCC®CCL-185™) were cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% FBS, 1% glutamine, 1% antimycotic/antibiotic  at 37 °C, 5% CO2 and subcultured according to manufacturer’s procedure.Human bone osteosarcoma HOS cells (ATCC® Reporter (DLR™) Assay System  according to the manufacturer’s procedure. Cells were seeded at the density 5 × 104 cells/well in 24 well plates in antibiotic free growth media 24 h prior transfection. Next day, 500 ng of respective pGL3 reporter plasmid harbouring RANKL promoter , its mutant (pGL3-RANKL370 GREMut), positive control (pGL3-SV40) and negative control  and together with 15 ng of pRL Renilla plasmid were transfected using 1.5 µL PolyJet™ In Vitro DNA Transfection Reagent  according to manufacturer’s instructions. Luciferase assays were performed 24 h after transfection. The Firefly (Firefly luciferase is encoded in pGL3 vectors) and Renilla luciferase (Renilla is encoded in pRL vector) activities were measured for each sample. Relative luciferase activity was calculated by normalizing Firefly luciferase activity with the Renilla luciferase. All the experiments were performed at least three times independently.Luciferase activity was measured with the Dual-LuciferaseRANKL was analyzed using qPCR with RNA samples obtained in gain-of-function and loss-of-function experiments. The RNA was extracted from cells and the complementary DNAs (cDNAs) were synthesized using High-Capacity cDNA Reverse Transcription kits , with gene expression analyses performed as described below. Expressions of RANKL, NR3C1 were also analyzed using qPCR assays [Expression of R assays . For qPC2 dishes and transfected with empty pCMV-FLAG expression vector or pCMV-NR3C1 expression vector. The cells were lysed after 24 h and resolved on SDS-PAGE. The proteins were transferred to nitrocellulose membranes, which were later blocked in 5% milk/TTBS, and incubated overnight with the primary antibodies against FLAG . After the incubation in the primary antibodies, the membranes were washed in TTBS and incubated in the secondary antibodies bound to HRP, for 1 h. Substrate was then added and the proteins were visualized using imaging platform . Antibodies against beta-actin  were used for loading controls.Human bone osteosarcoma cells were seeded in 10-cmNR3C1 (sc-35505) and a negative control (sc-37007) were purchased from Santa Cruz Biotechnology, Dallas, TX, USA. Here, 50 pmol was transfected using Oligofectamine reagent . Then, 72 h after transfection, the RNA was isolated using PeqGold RNA extraction kits , reverse transcribed, and subjected to q-PCR for measurements of expressions of the genes of interest. Three biological replicates were analyzed.siRNAs for silencing of TGTTCTTCTATTTCAGAG′3) were prepared by hybridization in a C1000 thermal cycler by stepwise 0.5 °C decreases in the temperature, ranging from 95 °C to 35 °C, over 120 cycles, each 30 s long. Control reaction was performed without nuclear extract. The specificity of the DNA-protein complexes was verified by binding with competitive non-biotinylated DNA or mutated non-biotinylated DNA. The biotinylated oligonucleotides listed in Electrophoretic mobility shift assays were performed using LightShift Chemiluminescent EMSA kits , according to the manufacturer instructions and as previously described . 293T nup < 0.05. In case of the luciferase assays, the results of firefly luminescence were normalized with Renilla luminescence values. The significance of the data was analysed by student t-test (two tailed) or ANOVA as implemented in Graphpad Prism 8.0. Data are presented as means with standard errors of the means.All experiments were performed in triplicates. Data were analysed using the GraphPad Prism 8 software . All of the data are presented as mean relative target gene expression after normalization with GAPDH expression. The results in the graphs are depicted as the means and standard error of the means of all three biological repeats. Statistical analyses were carried out by Student’s t-test. The level of significance was taken to be TNFSF11 expression by binding to it proximal promoter region. We identified a novel glucocorticoid response element at the position +352 of the human TNFSF11 gene and show that this site is responsible for GR dependent activation of the human TNFSF11 promoter. Our study provides a novel link between glucocorticoid administration and onset of osteoporosis.In this study we demonstrate for the first time that glucocorticoid receptor can directly regulate human