Clusters of enhancers, referred as to super-enhancers (SEs), control the expression of cell identity genes. The organisation of these clusters, and how they are remodelled upon developmental transitions remain poorly understood. Here, we report the existence of two types of enhancer units within SEs typified by distinctive CpG methylation dynamics in embryonic stem cells (ESCs). We find that these units are either prone for decommissioning or remain constitutively active in epiblast stem cells (EpiSCs), as further established in the peri-implantation epiblast in vivo. Mechanistically, we show a pivotal role for ESRRB in regulating the activity of ESC-specific enhancer units and propose that the developmentally regulated silencing of ESRRB triggers the selective inactivation of these units within SEs. Our study provides insights into the molecular events that follow the loss of ESRRB binding, and offers a mechanism by which the naive pluripotency transcriptional programme can be partially reset upon embryo implantation. Clusters of enhancers, called super-enhancers (SEs), control the expression of cell identity genes. Here, the authors identify two types of enhancer units within SEs in ESCs, characterised by distinctive CpG methylation dynamics, and find that ESRRB regulates the enhancer units decommissioned at exit from naive pluripotency. Alongside the core transcription factors (TFs) OCT4, SOX2 and NANOG (OSN), ESC identity is associated with the expression of an additional cohort of naive pluripotency factors, including ESRRB, KLF4, TBX3 and TFCP2L1. These proteins are highly expressed in ESCs and downregulated as primed pluripotency is established2. Conversely, OTX2, ZIC2 and OCT6 factors were identified as major transcriptional regulators of primed EpiSCs in the context of the continued expression of OSN5.Pluripotency, the ability to form all tissues in an adult organism, is under the control of complex mechanisms that enable cells to differentiate into the early somatic and germ cell lineages. Embryonic stem cells (ESCs) and epiblast stem cells (EpiSCs) are derived from mouse pre- and post-implantation embryos, respectively, and represent the naive and primed state of pluripotency8. Readily demarcated by enhancer-specific histone marking and protein binding at high density, SEs in ESCs preferentially recruit numerous naive TFs, and are predicted to be decommissioned as cells exit from naive pluripotency8. Given the shared expression of a large panel of genes in ESCs and EpiSCs10, it remains unclear how this is achieved. In particular, the fate of individual enhancer units across SEs has not been investigated during the transition from naive-to-primed pluripotency.Enhancers act as hubs of TF binding and promote gene expression. Recent studies suggest that large regions with clustered enhancer units, often described as super-enhancers (SEs), regulate the expression of key cell identity genesIn this study, we identify molecular and functional differences between enhancer units within SEs, revealing distinctive regulatory mechanisms. We find that enhancer units mapped in ESCs divide into two types based on whether or not they continue to function in EpiSCs, as further established in the post-implantation epiblast in vivo. Mechanistically, we demonstrate that ESC-specific enhancer units exhibit extensive cell-to-cell CpG methylation heterogeneity and are most specifically marked by ESRRB. As a result, these units are selectively destabilised at the exit from naive pluripotency, as recapitulated upon ESRRB depletion. Loss of ESRRB in ESCs promotes de novo methylation, reduces mediator and RNA polymerase II (POL2) binding and attenuates the expression of target genes and enhancer RNAs (eRNAs) over disruption of chromatin interactions. In contrast, ESRRB-independent units within SEs remain active and hypomethylated through steady binding of TFs and co-regulators in ESCs and EpiSCs. These units promote the expression of a core set of genes throughout the naive-to-primed transition, suggesting a crucial role for the upholding of pluripotency upon embryo implantation.8. Additionally, we noticed that SEs and TEs show a significant difference in GC content . Thus, we hypothesised that CpG methylation might contribute to the structural organisation of SEs. To test this idea, we used available bisulfite-sequencing (BS-seq) data collected from ESCs grown in the presence of serum and leukemia inhibitory factor (serum/LIF)11. We scored each CpG as methylated (mCpG) or unmethylated along SEs mapped in ESCs and other cell types8 using a Hidden Markov model . As anticipated, high CpG methylation levels were steadily detected at somatic (proB cell) SEs  SE Fig. . By comp SE Fig. .Fig. 1De16 and called significant promoter–SE interactions with the CHICAGO pipeline17  between the selected datasets  were considered  capture Hi–C libraries generated using 4 bp recognition restriction enzymes22. Using newly generated BS-seq data in EpiSCs (this study), we observed that SEs mapped in ESCs accumulate substantial levels of CpG methylation in the primed cells , specific units escaped methylation in other SEs . By comparing the methylation status of all SE units in ESCs and EpiSCs, we thus identified two types of units with different fates: persistently unmethylated  and differentially methylated 23, offering a transitional stage between naive and primed identities. In agreement with our data, PU compared to DM subregions appeared largely unmethylated in EpiLCs and EpiSCs  or decommissioned (DM) during the pre- to post-implantation transition.To validate the assignment of PU and DM subregions in independent ESC and EpiSC lines, we probed alternative BS-seq studiesSCs Fig. . DM subr–E4 Fig. , which c25. Expression changes were predicted to be inversely related to average CpG methylation levels and compared with expression dynamics measured by RNA-seq (see Methods section). Amongst most affected SE-interacting promoters, we identified well-established naive pluripotency factors, including Klf4, Esrrb, Prdm14, Tbx3, Tcfcp2l1 and Zfp42  and in vivo (from ICM E3.5 to epiblast E6.5)p = 4 × 10−7). Importantly, these contrasting gene expression dynamics were recapitulated in vivo from E3.5 to E6.5 , mirroring the segregation of PU (unmethylated) and DM (methylated) subregions in the peri-implantation epiblast 5 in EpiLCs and EpiSCs  analysis of selected candidates at different time points upon the conversion of ESCs into EpiSCs in vitro27 Fig. . As anti27 Fig. , indicatSCs Fig. , indicat29. Interestingly, cell state fluctuations also manifest at the epigenetic level with evidence of CpG methylation oscillations at enhancers33. To assess the level of cell-to-cell methylation heterogeneity at PU and DM, and its potential association with gene expression, we reanalysed parallel single-cell transcriptional (sc-RNA-seq) and bisulphite (sc-BS-seq) data from ESCs grown in serum/LIF and 2i/LIF30. Remarkably, substantial variation in CpG methylation levels was revealed at DM subregions in serum/LIF conditions  and class II (Esrrb) SEs in the two cell clusters , we identified two subpopulations in serum/LIF ESCs: “naive-like” cells showing hypomethylated DM subregions as seen in 2i/LIF, and “primed-like” cells harbouring higher methylation level and variance at the same regions Fig. . CpG meters Fig. , in agre35. We therefore compared the relative occupancy of DNMT3s at SE subregions, along with the antagonistic enzyme TET1, which is capable of removing CpG methylation in a multistep process36. As anticipated, PU subregions showed significantly lower DNMT3s occupancy and higher recruitment of TET1  and a set of individual features as quantitative predictor variables (see Methods section). All features tested apart from H3K27ac were significantly associated with DM/PU status  ESCs grown in serum/LIFins Fig. , pointin30 to ask whether sporadic induction of early differentiation/primed pluripotency genes in serum/LIF42 could play a part in the observed cell-to-cell epigenetic heterogeneity. Receiver operating characteristic (ROC) curves were generated to evaluate, on the basis of their normalised expression levels,  the ability of co-expressed or individual genes to separate “naive-like” from “primed-like” single-cell clusters defined in Fig. 43). Notably, three gene sets were tested encoding for naive, general or primed pluripotency markers  values > 0.92, p = 10−5) were identified as top genes whose increased expression best discriminates “naive-like” cells from “primed-like” cells  in either PU or DM subregions were attributed to at least one corresponding TF expressed in serum/LIF ESCs  including differentiation-associated TFs induced later in development . Approximately half of the statistically enriched motifs  adjusted p < 5 × 10−9), while the KLF/SP motif was found enriched in both DM (p < 3 × 10−7) and PU subregions (p < 2 × 10−27). These correspond to the pluripotency factors ESRRB/NR5A2 and KLF2, KLF4 or KLF5 that are highly expressed in naive pluripotency and downregulated as primed pluripotency is established  and SOX2. Collectively, our findings corroborate a possible role for naive pluripotency factors in regulating DM subregions, and furthermore suggest that PU might be maintained hypomethylated and accessible through hotspot binding of numerous TFs.Interestingly, the cognate motif for ESRRB/NR5A2 showed strong enrichment in DM subregions only  ESCs45, and Nanog−/− ESCs28 for comparison, relative to control populations. We found that the expression of class II SE-interacting promoters was uniquely sensitive to the depletion of ESRRB compared to class I candidates  enhanced the expression of almost all genes tested, with a more pronounced gain at class II compared to class I genes  Esrrb form where the ability of ESRRB to recruit co-activators at bound sites is abolished46. EsrrbMutAF-2 cells showed lowered class I and class II gene expression  and class II (DM-containing only) SE-associated gene candidates in tes Fig. . A similtes Fig. , an essenes Fig. . Esrrb−/Esrrb−/− ESCs, showing no induction of the early Otx2, Fgf5 and Dnmt3b differentiation markers and retained expression of Pou5f1, Sox2 and Nanog in serum/LIF . To corroborate whether the methylation state of DM subregions was also affected by the loss of ESRRB binding, we focussed on the Klf4 locus as an example of class II SE-interacting promoters  ESCs. As anticipated, methylation reached similarly high levels at DM and INT sites in c-EpiSCs, where Klf4 expression is extinguished  from −/− and control ESCs, implying that the constitutive depletion of ESRRB might destabilise but not fully inactivate DM-containing SEs in ESCs. This agrees with the maintenance of an undifferentiated state in  SE Fig. . Results52. To further elucidate the molecular consequences of ESRRB depletion, we investigated the binding of OCT4, P300 and MED1 at the Klf4-associated SE using ChIP-qPCR assays  assays in 2i/LIF ESCs (2i) and both control (f/f) and Esrrb−/− ESCs grown in serum/LIF (ser) with declining levels of MED1 binding . In contrast, ESRRB depletion did not significantly affect the H3K27ac levels of PU and DM subregions. As MED1 can be recruited by multiple TFs55, the relationship between ESRRB occupancy and MED1 loss was also examined in the context of OCT4 binding as control. Using a linear regression model , highlighting the specificity of ESRRB–MED1 association in our model system.To establish the specific dependency of MED1 occupancy on ESRRB binding across all PU and DM subregions, ChIP-seq of MED1 and H3K27ac as a control were performed in ESRRB-depleted and control ESCs (this study). In line with our ChIP-qPCR, strong loss of MED1 occupancy in ide Fig. . ImportaKlf4). In contrast, PU subregions retain MED1 occupancy in ESRRB-depleted cells. These regions are associated with genes that maintain or even gain expression in primed pluripotency  subregions that most specifically regulate ESC-specific pluripotency genes and become decommissioned in the primed state. These SE subregions are highly bound by ESRRB in naive ESCs and display strong loss of MED1 occupancy and enhancer activity in ESRRB-depleted cells concomitant with the acquisition of CpG methylation , as previously suggested61. The focal unmethylated subregions of SEs coincide with the binding of TFs and co-regulators  where SE–promoter interactions preferentially assemble, in agreement with the concept of hub enhancers62. Moreover, we find that the prevalence of chromatin interactions and eRNA transcription at unmethylated over methylated regions is conserved under 2i/LIF conditions, which enforce a globally hypomethylated state in ESCs  that follow independent fates during the naive-to-primed pluripotency transition. While PU subregions remain hypomethylated, highly accessible and hotspots of protein binding in ESCs and EpiSCs, DM subregions are targeted by de novo methylation and loose their enhancer signatures  in the primed cells. Hence, while PU and DM enhancer units are both engaged in ESCs, they become constitutively active (PU) or decommissioned (DM) in EpiSCs, as further established in the peri-implantation epiblast in vivo.SEs are defined as clusters of enhancer units located within large domains of H3K27ac deposition and cell-type-specific TF binding. How these domains are organised and to which extent enhancer units within SEs are functionally equivalent is still the matter of debate68. Thus, PU-like subregions might similarly operate in other cell state transitions at different stages of development. Whether PU subregions mapped in pluripotent cells are subsequently inactivated upon gastrulation, as suggested by their methylated profiles in somatic cells  are enriched within INT (methylated) regions of SEs, pointing to the presence of “latent” lineage-specific enhancer units (or seed enhancers10). While these putative enhancer units are most likely inactive in pluripotent cells, they might become unveiled in a tissue-specific manner upon CpG demethylation in due course of development.Remarkably, we find that PU subregions are not detected across all ESC SEs and most specifically regulate the expression of a core set of genes shared by naive and primed cells. This evokes a pivotal role for PU subregions in the upholding of pluripotency during this key developmental transition. Of interest, hotspots of TF binding were also reported within lineage-specific SEs with a prevalent role at the onset of progenitor differentiation50. Focussing on the Klf4-associated (DM-containing) SE as a model locus, we demonstrate that ESRRB depletion in ESCs is sufficient to impede the loading of the mediator complexes at Klf4-associated SE, reduces the expression of its target gene, and promotes CpG methylation at DM subregions. This might involve methylation spreading from DNMT3B highly bound INT regions owing to the processive activity of DNMT3B enzymes71. ESRRB-dependent MED1 recruitment is further confirmed across all SEs genome wide, particularly at DM subregions, and is essential for maximal transcriptional activation by promoting class II SE–promoter interactions. Accordingly, we find that chromatin interactions at the Klf4 locus are destabilised upon ESRRB depletion, concomitant with a reduction in POL2 recruitment and eRNA production. These findings corroborate knowledge of nuclear receptor-mediated gene activation mechanisms74, and furthermore are supported by the ability of ESRRB to interact with mediator and POL2 complexes in ESCs51. Given the importance of ESRRB in stabilising the recruitment of these complexes, possibly via its co-activator NCOA3, it will be of interest to study the role of ESRRB-NCOA3 in the formation of phase-separated condensates recently identified as key activation domains, particularly at SEs55.In contrast to PUs, we show that DM subregions are demarcated by a high-level of ESRRB binding. Concurringly, ESRRB’s cognate binding motif is strongly enriched at DM relative to PU subregions in contrast to other pluripotency TF motifs. Functionally, we establish that DM enhancer units regulate the expression of pre-implantation gene modules, and are uniquely sensitive to the loss of ESRRB that underlies or triggers the exit from naive pluripotencyEsrrb, which in turn is under the control of a DM-containing (class II) SE and dynamically methylated in cells initiating differentiation . Given ESRRB’s ability to access ERRE binding sites within methylated regions75 and inhibit de novo CpG methylation upon binding (this study), we propose that a balance between DNMT3s and ESRRB activities instigates a metastable state at DM subregions prone for decommissioning upon exit from naive pluripotency . It is worth noting that the action of ESRRB is not restricted to SEs but most likely extends to a subpopulation of TEs that are targeted by hypermethylation in EpiSCs and similarly sensitive to the loss of ESRRB in ESCs , superseries GSE139189 . All other publicly available datasets used are specified in Supplementary Data Gene Expression Omnibus accession numbers for the sequencing data generated in this paper are 77) supplemented with 1 μM PD0325901, 3 μM CHIR99021 and LIF (2i/LIF conditions). c-EpiSCs and embryo-derived EpiSCs were cultured in N2B27 (ref. 26) or CDM25, respectively, both supplemented with 20 ng/mL activin A and 12 ng/mL fibroblast growth factor 2 (FGF2). Mouse Esrrb−/− ESCs45, Nanog−/− ESCs28 and matching control populations have been previously described. Ncoa3−/− and control ESCs were derived from mutant and WT B6/129 mice and kindly provided by Austin Cooney. For the generation of rescued Esrrb−/− ESCs, cDNA encoding Esrrb WT or AF-2 point-mutant46 form was cloned into the pPyCAGIP vector, and one million of cells transfected with Lipofectamine 2000 and 2 μg of either of these two vectors or an empty vector (control). Twenty-four hour post-transfection 1 μg/mL puromycin was added for selection and after 8–10 days of culture individual ESC clones were isolated and expanded indefinitely under selection.Mouse ESCs were routinely cultured on 0.1% gelatin coated plates and maintained in Glascow Minimum Essential Medium (GMEM) media supplemented with 10% fetal bovine serum (serum), MEM non-essential amino acids, beta-mercaptoethanol, L-glutamine, sodium pyruvate, sodium bicarbonate, penicillin/streptomycin, LIF (prepared in-house) and the appropriate drug selection (serum/LIF conditions). Where mentioned, ESCs were adapted into serum-free culture conditions using either N2B27 or chemically defined medium (CDM27. Passage was performed after 4–5 days using collagenase II treatment. Cells were considered as stably converted (c-EpiSCs) after at least three passages in the presence of FGF2 and activin A.R1-ESCs (ATCC) used for conversion were cultured in 2i/LIF or serum/LIF. To induce conversion into the primed EpiSC state, ESCs were trypsinized and replated into CDM supplemented with FGF2 (12 ng/ml) and activin A (20 ng/ml), on serum-coated cultures platesfre Kit (Invitrogen). Samples were reverse-transcribed using SuperScript III (Invitrogen) and random primers following the manufacturer’s instructions. For quantification, cDNA (or DNA) samples were amplified with SYBR Green PCR Mastermix (Sigma or Applied Biosystems), using a StepOne™ System (Applied Biosystems). Data were normalised using the geometric mean of Sdha and Pbgd for conversion experiments or S17, L19 and Gapdh for mutant, and matching control ESCs. Primers used in RT-qPCR assays are listed in Supplementary Data Total RNA was isolated and DNaseI-treated using the RNeasy mini kit (Qiagen). For eRNA detection, total RNA was isolated using Trizol (Invitrogen) and DNAse-treated was performed on purified RNA using TURBO DNA-78 with minor modifications outlined below. Chromatin was fixed with 1% formaldehyde (Sigma-Aldrich) for 10 min and sonicated on a bioruptor (Diagenode) to produce fragments of 100–500 bp, and ChIPs performed with Protein G-coupled magnetic Dynabeads (Invitrogen) and the following antibodies: 8 µg MED1 (A300-793A Bethyl Laboratories), 5 µg OCT4 (sc-8628 SantaCruz), 5 µg p300 (sc-585 SantaCruz), 5 µg POL2  and 5 µg H3K27ac (Ab4729 Abcam). The amounts of chromatin (protein) used in each ChIP were as follows: 400 µg (OCT4), 500 µg (P300), 500 µg (H3K27ac) and 800 µg (MED1 and POL2). Following washes of bound DNA–protein complexes, DNA was eluted in 1% sodium dodecyl sulfate (SDS) and treated with 40 ng/µl RNaseA following 0.2 µg/µl Proteinase K. After phenol/chloroform purification, DNA was then precipitated at −20 °C with 20–30 μg GlycoBlue carrier (Invitrogen), 1/10 volume of 3 M NaAc and 2 volumes of 100% ethanol. Resuspended pellets were used for qPCR or for generation of libraries for sequencing (MED1 and H3K27ac). Sequencing libraries were prepared using the NEBNext® Ultra™ DNA Library Prep Kit and Multiplex Oligos (New England Biolabs) from 5 ng of DNA. Following analysis on an Agilent Bioanalyzer libraries were pooled and sequenced on an Illumina Genome Analyzer II (Illumina). Quality of the sequenced reads was assessed using the FASTQC program (Babraham Bioinformatics). Primers used in ChIP-qPCR assays are listed in Supplementary Data ChIP was performed as previously describedGenomic DNA was extracted using the DNeasy kit (Qiagen). A total of 1 µg of eluted DNA was diluted in 17 µl of 20% TE buffer. For each set of enzyme reaction, 2 µl of appropriate restriction enzyme buffer was added to the diluted DNA. A volume of 9.5 µl of the mixture was then transferred to a separate tube, serving as undigested control. A volume of 0.5 µl (5U) of enzymes was added to the remaining DNA mixture. Both digestion reactions and undigested control were incubated overnight in 37 °C incubator. A volume of 95 µl of 20% TE buffer was then added to both digested and undigested samples, which were then proceeded with qPCR analysis. Enzymes (methylation sensitive) used in this study are BsaAI, Ssil, HpaII and Hin6I, and qPCR primer sequences are listed in Supplementary Data imagej.nih.gov/ij). Signals were normalised to H3 or Actin. Western blots were repeated at least three times. Antibodies used were: ESRRB , NCOA3 , NANOG , ACTIN  and H3 . Uncropped and unprocessed scans of blots are included in Supplementary Data Cells were lysed for 30 min on ice into RIPA buffer  in the presence of protease and phosphatase inhibitors (Pierce). Proteins were quantified using BCA assay (Pierce). A total of 10–15 µg of proteins were charged on pre-cast polyacrylamide gel 4–15% (Biorad) for 1 h run at 100 V. Transfer was then performed on Trans-Blot Turbo (Biorad) for 7 min on a PVDF membrane . After blocking in TBS-Tween20 0.01% (TBS-T) with either 4% non-fatty milk or 5% BSA, membranes were incubated overnight at 4 °C with primary antibodies. After washes in TBS-T, membranes were incubated with secondary antibodies for 1 h, washed and revealed with ECL2 western blotting substrate (Pierce). Chemiluminescent signals were captured using Chemidoc Touch imaging system (Biorad) and then analysed with ImageJ (Esrrbf/f and Esrrb−/− ESCs were cultured in serum/LIF or 2i/LIF (control cells only), and three biological replicates were employed per condition. 4C-seq experiments were performed as previously50 with minor modifications. Briefly, 10 million cells were crosslinked for 13 min with 2% paraformaldehyde in ESC culture medium, quenched with glycine and lysed in 15 ml lysis buffer  for 30 min at 4 °C. Nuclei were then incubated with 0.25% SDS for 30 min in NEB buffer3 followed by Triton X-100 treatment for 30 min, both at 37 °C. Nuclei were then digested with 700 U DpnII enzyme (NEB) overnight at 37 °C. The enzyme was inactivated at 65 °C for 15 min followed by “in nuclei” ligation at 16 °C with 2000 U T4 ligase (NEB). Samples were then treated with protease K and RNAseA, and DNA was purified by phenol–chloroform (Sigma). DNA was further digested with 50 U BfaI, and purified with QIAquick PCR purification columns followed by a second ligation at 16 °C. Next, 1000 ng of 4C-seq library was amplified with bait-specific inverse primers50 and using the Expand Long template PCR system (Roche 11759060001) for 28 PCR cycles. PCR products were then purified and 50 ng DNA was used for library preparation using KAPA Hyperprep kit (Roche) and five PCR cycles. Libraries were sequenced on the Illumina NextSeq 500 (Illumina) to obtain paired-end sequences of 50 bp.GSM1904118 and GSM1904112 datasets). HMM analysis has been applied on each chromosome independently. Genome was split in segments containing CpGs spaced by <1 kb. Segments containing <10 CpGs have been filtered out. Parameters of HMM analysis on the remaining CpGs were initialised using viterbiEM function of tileHMM R-package; CpGs were considered to be in two different states: methylated or unmethylated. Corresponding HMM model has been applied for CpGs states call on the whole chromosome. Then, in ESC (serum/LIF) and EpiSC samples independently, segments of SEs containing at least four consecutive unmethylated CpGs were collected (with a coverage of at least 7 reads and distant of <1 kb). Intersection of EpiSC and ESC unmethylated segments were defined as PU regions. Segments containing at least four consecutive CpGs that were unmethylated in ESCs and methylated in EpiSCs were defined as DM regions. Segments of SE regions located between PU and DM segments were defined as INT segments. Coordinates and CpG methylation information on the different samples (in vitro and in vivo) can be found in Supplementary Data For each WGBS sample, CpG information have been filtered to keep only those for which we had a sufficient coverage of at least 7 reads (apart from All ChIP-seq datasets were processed from raw reads (Fastq files) to filtered, mapped and deduplicated reads (bam files) through a standardised pipeline. This pipeline involves: adaptor removal, low-quality read trimming and filtering using Trimmomatic; alignment to mm9 reference genome with Bowtie2; duplicate read marking and removal with Picard Tools. ChIP-seq coverage plots were produced as follows : for each of a set of genomic regions of interest, the region is split into a fixed number (1000) of windows of equal width, and the average coverage across each window is computed; the average of each window’s coverage is then computed across all regions of interest.q-value threshold of 0.01 and using whole cell extract (WCE) (input) as control. The number of ATAC-seq peaks intersecting SE subregions in 2i-ESCs, ser-ESCs or EpiSCs was computed using the summarizeOverlaps function from the GenomicRanges R-package. When multiple SE subregions overlapped the same ATAC-seq region, the SE subregion with highest overlap was assigned. The average chromatin accessibility level per SE subregion was computed with the featureCounts function in the Rsubread package and normalised to RPKM after subtracting the read counts from the WCE (input) data. Data are shown in Supplementary Data Chromatin accessibility measured by ATAC-seq in ESCs grown in 2i/LIF (2i), serum/LIF (ser), and in EpiSCs was downloaded from published data  were considered expressed.Gene expression values (average RPKM of at least two replicates) were taken from publicly available RNA-seq datasets (Supplementary Data distanceToNearest” function in the R-package GenomicRanges Only promoters of known genes were considered.Promoters were defined as the 5 kb window surrounding an annotated transcription start site. Gencode GRCm37 (version M1) gene annotation was used. The distance from a SE to the promoter of the closest expressed gene was determined with the “k = 7 bins. Differential analysis was done by DEseq2 using the local dispersion fit, after summing read counts in bins of 5 kb surrounding the viewpoint (up to 1 Mb up- and downstream). Bins with coverage <1000 RPKM were discarded for differential analysis. Data are shown in Supplementary Data 4C forward or reverse PCR primers from paired-end sequenced FASTQ files were trimmed with cutadapt allowing a 10% mismatch: cutadapt -g [primer_seq] -O [primer_length −2] -e 0.1 --discard-untrimmed. Trimmed reads were mapped to the mm9 reference genome using Bowtie2 with the option “very-sensitive” in single-end mode. Low-quality reads (MAPQ < 10) were discarded. The FourCSeq package was used to map reads from the forward and reverse PCR primer to valid restriction sites. Reads were normalised compared with DESeq2 discarding reads that mapped in trans. For visualisation, counts were smoothed using a running mean with 15 and NcoI data (Sahlen)16, four consecutive restriction fragments were merged into a pseudo-fragment to increase the read count and confidence per called interaction. We used the CHICAGO pipeline for CHiC17 to call significant interactions between these pseudo-fragments in the ser-ESC or 2i-ESC state with a default threshold (score > = 5). Additionally, interactions with coverage of <5 reads (geometric mean of the replicates) were discarded. CHICAGO takes the geometric mean of the pairwise interactions counts when multiple replicates are available. The much lower library depth of the second replicate (Supplementary Data High-resolution capture Hi–C (CHiC) studies  following an approach we used earlierCHiC interaction frequency between promoters and SE subregions was computed at the native 1 restriction fragment resolution (DpnII or NcoI) for all promoter–SE pairs that had a significant interaction. SE subregions smaller than 500 bp were discarded since they often have too few overlapping restriction fragments to enable a robust analysis. Statistical differences per subregion class  were assessed by a linear regression model that accounts for the two major confounders: the number of capture baits per subregion and the promoter–subregion distance (log2).X = log2 × (%CpG ESC − %CpG EpiSC). The analysis was restricted to PU and DM SE subregions.We hypothesised that changes in CpG methylation would mostly affect the gene expression of the strongest interacting promoters Fig. . In othep-Values were adjusted for multiple testing using Benjamini–Hochberg correction.We used Gimme motifs to find TF motifs that are statistically enriched in the PU, DM or INT SE subregions. Since the SE subregions are typically quite broad, we partitioned each SE subregion into equally spaced regions with a length of 291 bp; the median length of the ATAC-seq peaks. To determine a threshold for TF motif presence/absence, 50,000 regions of 291 bp were randomly sampled from the genome. For each motif, the 99% was used as a cut-off, leading to an empirical false discovery rate of 0.01 (gimme threshold). Next, we counted the number of present motifs in the PU, DM and INT subregions of SEs relative to the union of the regions and applied a hypergeometric test. 30 were obtained from the Gene Expression Omnibus (accession GSE74534). Segments of SE regions were mapped to mm9 coordinates using UCSC liftOver tool (https://genome.ucsc.edu/cgi-bin/hgLiftOver). For each SE subregion type , mCpG/total-CpG ratio was computed from all reads mapping to any CpG site within an SE segment of the corresponding type. Complete linkage hierarchical clustering was performed on all available ESCs, using the squared differences between the total DM mCpG/total-CpG averages of each cell. This clustering was used to define two clusters of ESCs: one cluster included all 2i-ESCs and a subset of the serum-ESCs (which we defined as “naive-like” ESCs); the other cluster contained only serum-ESCs (which we defined as “primed-like” ESCs). Differential gene expression analysis was performed using Limma to compute empirical Bayes moderated t-statistics from linear models fitted to RNA-seq read counts for serum-ESCs by the DM methylation cluster to which the corresponding cell had been assigned. DNA methylation profiles  and each of a set of features as quantitative predictor variables: average ChIP-seq enrichment for TET1, DNMT3A/B, H3K4me1, H3K4me3, H3K27ac, average ATAC-seq signals in serum/LIF ESCs and CpG density. Models were fitted using the generalised linear model function implementation ‘glm’ in R. Model coefficient estimates, standard errors, 2 fold change of MED1 ChIP-seq enrichment in Esrrb−/− relative to Esrrbfl/fl ESCs as a quantitative outcome variable, with serum/LIF ESC ESRRB ChIP enrichment, subregion class (DM vs PU) and OCT4 binding score as predictor variables. Model coefficients, t-statistics and p-values were obtained from the fitted linear model using the ‘summary.lm’ function in R, and are provided in Supplementary Data MACS2 was applied to call peaks from serum/LIF ESC OCT4 ChIP-seq study analysed for Fig. ATAC-seq and ChIP-seq bigwig tracks were prepared using deeptools “bamCoverage”, with parameters “binsize” = 10, “normalizedUsing” = RPKM and “extendReads” = 200 (or fragment size in the case of paired-end sequencing). Tracks where visualised on the Washu epigenome browser.Box plots: Centre lines show the medians; box limits indicate the 25th and 75th percentiles as determined by R software; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles, outliers are represented by dots.Violin plots: White dots show the medians; box limits indicate the 25th and 75th percentiles as determined by R software; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles; polygons represent density estimates of data and extend to extreme values.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Reporting Summary


Saccharomyces cerevisiae encodes a functional homolog of RECQL4 called Hrq1, which is more amenable to experimentation and has recently been shown to be involved in DNA inter-strand crosslink (ICL) repair and telomere maintenance. To expand our understanding of Hrq1 and the RecQ4 subfamily of helicases in general, we took a multi-omics approach to define the Hrq1 interactome in yeast. Using synthetic genetic array analysis, we found that mutations of genes involved in processes such as DNA repair, chromosome segregation, and transcription synthetically interact with deletion of HRQ1 and the catalytically inactive hrq1-K318A allele. Pull-down of tagged Hrq1 and mass spectrometry identification of interacting partners similarly underscored links to these processes and others. Focusing on transcription, we found that hrq1 mutant cells are sensitive to caffeine and that mutation of HRQ1 alters the expression levels of hundreds of genes. In the case of hrq1-K318A, several of the most highly upregulated genes encode proteins of unknown function whose expression levels are also increased by DNA ICL damage. Together, our results suggest a heretofore unrecognized role for Hrq1 in transcription, as well as novel members of the Hrq1 ICL repair pathway. These data expand our understanding of RecQ4 subfamily helicase biology and help to explain why mutations in human RECQL4 cause diseases of genomic instability.The human genome encodes five RecQ helicases  that participate in various processes underpinning genomic stability. Of these enzymes, the disease-associated RECQL4 is comparatively understudied due to a variety of technical challenges. However,  Here, hundreds of significant positive and negative interactions were identified, with gene ontology (GO) term enrichment for processes such as transcription and rRNA processing in addition to expected functions such as DNA repair. Mass spectrometry (MS) analysis of proteins that physically interact with Hrq1 returned similar results. Our initial characterization of the link between Hrq1 and transcription revealed that hrq1 mutant cells are sensitive to the transcription stressor caffeine and that the hrq1Δ and hrq1-K318A mutations affect the transcription of hundreds of genes, many of which are known or hypothesized to be related to transcription, DNA ICL repair, and the cytoskeleton.To gain a more comprehensive understanding of the roles of RecQ4 subfamily helicases in genome integrity, we sought to define the HRQ1 gene was deleted in Y8205 (HRQ1 locus (MB527 and MB528). The confirmed hrq1Δ strain was named MBY639. The hrq1-K318A allele was introduced into the Y8205 background in a similar manner. First, an hrq1-K318A(NatMX) cassette was PCR-amplified from the genomic DNA of strain MBY346  were sequenced using oligonucleotide MB932 to confirm the presence of the K318A mutation. The verified hrq1-K318A strain was named MBY644. Hrq1 was tagged with a 3xFLAG epitope in the YPH499 genetic background by transformation of a 3xFLAG(His3MX6) cassette that was PCR-amplified from the pFA6a-3xFLAG-His3MX6 plasmid (hrq1-K318A(NatMX). The confirmed Hrq1-3xFLAG strain was named MBY520.The in Y8205  by trans plasmid  using olhrq1Δ and hrq1-K318A alleles was performed at the University of Toronto using previously described methods .SGA analysis of the llection  and the hrq1Δ and hrq1-K318A from the single-gene deletion and TS arrays were reanalyzed by hand to confirm their phenotypes. Briefly, the SGA query strains MBY639 and MBY644 (NatR) were mated to MATa tester strains from the arrays (KanR), sporulated, and then analyzed by random spore analysis  of ∼1.5 in YPD medium at 30° with shaking. The cells were harvested by centrifugation at 4°, washed with 50 mL of sterile ice-cold H2O, and harvested as before. The cell pellet was then resuspended in 100 μL/g of cells resuspension buffer  supplemented with 10 μg/mL DNase I and protease inhibitor cocktail . This cell slurry was slowly dripped into liquid nitrogen to generate frozen yeast “popcorn”, which was stored at -80° until use. To cryo-lyse the cells, the popcorn was ground in a freezer mill with dry ice. The resultant powder was collected into 50-mL conical tubes that were loosely capped and stored at -80° overnight to allow the dry ice to sublimate away. To perform the Hrq1 pull down, the cell powder was resuspended in 2.5 g powder per 25 mL lysis buffer  with gentle agitation. Then, 100 U DNase I and 10 μL of 30 mg/mL heparin were added, and the sample was incubated for 10 min at room temperature with gentle agitation. Cellular debris was pelleted by centrifugation at 14,000 × g for 10 min at 4°. Then, 100 μL of anti-FLAG agarose slurry was washed and equilibrated with lysis buffer, and the clarified lysate and anti-FLAG resin were added to a fresh 50-mL conical tube. This suspension was incubated at 4° overnight on a nutator. The resin and lysate were subsequently placed in a 30-mL chromatography column, and the lysate was allowed to flow through the resin by gravity. The anti-FLAG agarose was washed with 30 mL lysis buffer, and the beads were then resuspended in 150 μL lysis buffer and transferred to a 1.5-mL microcentrifuge tube. At this point, the sample could be used for proteinase digestion and MS analysis, or proteins could be eluted from the resin and examined by SDS-PAGE and Coomassie staining. The untagged control strain (MBY4) was also processed as above to identify proteins that nonspecifically bound to the anti-FLAG agarose.To immunoprecipitate 4HCO3, pH 8.5) was used to resuspend the FLAG resin. To this slurry, 10 μL of 0.1 μg/μL Trypsin Gold (Promega) was added and allowed to incubate overnight at 37° with shaking. After digestion, the FLAG resin was separated from the digested peptides via spin columns and centrifugation. Formic acid  was added to the supernatant to quench the reaction. After digestion, the peptide mix was separated into three equal aliquots. Each replicate was then loaded onto a microcapillary column. Prior to sample loading, the microcapillary column was packed with three phases of chromatography resin: reverse phase resin, strong cation resin, and reverse phase resin, as previous described  with slight modifications. Briefly, 200 µL of each culture was placed in duplicate into wells in 96-well plates, and each well was overlaid with mineral oil to prevent evaporation. The plates were incubated (30° with shaking) in a Synergy 1H microplate reader (BioTek), which recorded OD660 measurements at 15-min intervals for 24 h. The mean of the OD660 readings for each strain was divided by the mean OD660 of the same strain grown in YPD.The sensitivity of https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/), and reads were mapped to the S. cerevisiae genome using bowtie2 on local mode . Sequencing libraries were prepared, and Illumina sequencing was performed by, Novogene Corporation. Data analysis was then performed by the Indiana University Center for Genomics and Bioinformatics. The sequences were trimmed using the Trim Galore script , we crossed the hrq1Δ and hrq1-K318A alleles to the single-gene deletion and TS allele collections to generate all possible double mutants and assessed the growth of the resulting spore clones to identify negative and positive genetic interactions. A discussion of the significant positive and negative genetic interactions can be found therein. Here, we used GO Term mapping to identify cellular processes enriched for hrq1 genetic interactors in those data. These processes were similar when considering the hrq1Δ and hrq1-K318A datasets alone or together, so we present the combined results for simplicity. For all of the negative genetic interactions with hrq1Δ and hrq1-K318A, the top 10 GO terms were transcription by RNA polymerase II, regulation of organelle organization, DNA repair, chromatin organization, mitotic cell cycle, peptidyl-amino acid modification, cytoskeleton organization, mitochondrion organization, organelle fission, and response to chemical . Briefly, this included synthetic interactions with genes encoding genome integrity factors  and mitochondrial proteins , consistent with the known roles of Hrq1 and human RECQL4 in genome maintenance . Arp8 is a chromatin remodeling factor . We hypothesize that this is due to the natively disordered N-terminus of Hrq1 . Next, we snap-froze and cryo-lysed cells to preserve macromolecular complexes in near-native states , 77 of which had a SAINT score > 0.75 and were thus considered significant . These 7Hrq1-interacting proteins that are subunits of larger macromolecular complexes involved in several of the GO Term processes listed above. For instance, among the rRNA processing and ribosomal small subunit biogenesis proteins (https://www.yeastgenome.org/complex/CPX-1604) are significant Hrq1 interactors. Many more such proteins had SAINT scores < 0.75, suggesting that they may be secondary interactors  and/or more weakly associated subunits of the processome. Similarly, the transcription by RNA polymerase I (https://www.yeastgenome.org/complex/CPX-1664) and RNA polymerase II (https://www.yeastgenome.org/complex/CPX-2662) complexes themselves. As with the hrq1 SGA data in our companion manuscript , these links to transcription are intriguing and reminiscent of the links of human RECQL5 to transcription  with Hrq1-K318A, we would start with focused experiments, for instance, treating cells with ICL damage to enrich for repair complexes or crosslinking cells, enriching for telomeric repeat DNA, and identifying the associated proteins. Indeed, such experiments are ongoing.Despite knowing some mechanistic details of how Hrq1 and transcription identified through SGA and IP-MS, we decided to determine if the S. cerevisiae transcriptome is altered by HRQ1 mutation. First, we tested the effects of the general transcription stressor caffeine  more sensitive than wild-type, but again, the hrq1-K318A mutant displayed greater sensitivity at a wider range of concentrations . Similarly, 301 and 124 genes were down- and upregulated, respectively, in hrq1-K318A cells compared to wild-type. Similar to the SGA and proteomic data sets, the GO Terms of these differentially expressed genes (DEGs) were enriched for processes such as response to chemical, meiotic cell cycle, mitotic cell cycle, rRNA processing, and chromosome segregation (Table S8).As a purine analog, caffeine does cause transcriptional stress, but it has pleiotropic effects on yeast cells, affecting multiple biological pathways  (HSP30) (ICY1) (STP4) (YER053C-A), a factor whose over-expression blocks cells in G1 phase (CIP1)  , but the latter are particularly tantalizing nonetheless considering the known function of Hrq1 in ICL repair (The largest number of outliers were the 10 upregulated DEGs in ) (ICY1) , a prediYLR297W, TDA6, and FMP48 gene products function in the Hrq1-Pso2 ICL repair pathway, and their levels must be elevated to compensate for the catalytically crippled Hrq1-K318A mutant. Alternatively, they may represent members of a back-up ICL repair pathway that is activated when the Hrq1-Pso2 pathway is ablated. In either case, it should be noted that the RNA-seq experiments were performed in the absence of exogenous ICL damage, but the hrq1-K318A cells appear already primed to deal with ICLs in the absence of functional Hrq1. The reasons for this are currently unknown, but our ongoing experiments are addressing this phenomenon. It should also be noted that these three genes were likewise upregulated in the hrq1Δ samples (File S4), though not to the same extent as in hrq1-K318A cells.Perhaps the S. cerevisiaeHrq1 interactome. The data reported here and in our companion manuscript  greatly expand the known genetic and physical interaction landscape of Hrq1 in yeast, including synthetic genetic interactions with and transcriptomic changes caused by the strong hrq1-K318A allele. Various links to the known and putative roles of Hrq1 and its homologs in DNA repair, telomere maintenance, and the mitochondria were found, as well as novel connections to the cytoskeleton and transcription.Here, we used a multi-omics approach to comprehensively determine the S. cerevisiae RecQ family helicase, Sgs1, is likewise involved in transcription . However, it is unclear if Hrq1 and Sgs1 act together during transcription or have distinct roles, and it is unknown what these roles are. Human RECQL5 physically interacts with RNA polymerase II, controlling transcription elongation (Hrq1 and/or Sgs1 function similarly and, in the case of Hrq1, perhaps in the transcription-coupled repair of DNA ICL lesions. Future work should address these hypotheses, as well as the others raised throughout this manuscript, to further characterize the roles of RecQ helicases in the maintenance of genome integrity. Similar to the mechanistic identification of the roles of Hrq1 in yeast (Our concurrent data also indicate that the second ongation . It is r


Replying to H. Rzepa.Nature Communications 10.1038/s41467-021-21433-8 (2021)2, which cites our recent paper describing the first chemical synthesis of C2 (ref. 1). Rzepa suggests on the basis of several in silico approaches that the formation of free C2 from alkynyl-λ3-iodane and fluoride ion would be prohibitively endo-energetic. He proposes three possible explanations of the apparent discrepancy between these theoretical calculations and our experimental findings, of which one is that some species other than C2 is actually formed.We are writing in response to Rzepa’s theoretical analysis of the generation of C2 and the first bottom-up chemical synthesis of nanocarbons from C2, we should like to respond to the latter point. We believe that the evidence presented in our paper for the generation of C2 itself is compelling1. In particular, the connected-flask, solvent-free experiment clearly supports the generation of free C2 gas, for the following reasons:13C-labeled 1b-13Cβ was used, a 1:1 mixture of O- ethynyl galvinoxyl 15-13Cα and 15-13Cβ was obtained  is calculated to be ca. 1:20 (per 1H).The relative rate of hydrogen abstraction between CH4.14 solely produced O-ethynyl galvinoxyl 15 (not 16).Galvinoxyl radical 5.15-13Cα and 15-13Cβ obtained by the reaction of 1b-13Cβ with 14 differed in solvents of different viscosities (η).The amount of the mixture of It is more difficult to establish conclusively whether free C2, in other words, a singlet biradical (charge-shift bonding) character, as suggested in a recent theoretical study2.These results indicated that the radical character of the intermediate is much milder than that of the common unstabilized alkynyl radical, which is consistent with the presence of an interaction between the radicals in C2. Our findings have attracted great interest, and we anticipate that independent experimental findings will emerge in the near future. We ourselves are working on the direct observation of chemically generated C2 by means of Raman spectroscopy, ESR spectroscopy, and other methods, and we hope that this work will provide definitive experimental evidence for the bond length and electronic state of C2 (ref. 3 and references therein).At present, we cannot explain the discrepancy between Rzepa’s theoretical evaluation and our experimental results. However, as discussed in our original paper, we believe that all our experimental observations can only be rationalized in terms of the generation of gaseous C


TERT promoter mutations and BRAFV600E mutation are well-established oncogenic alterations in human cancers. Coexistence of BRAFV600E and TERT promoter mutations is frequently found in multiple cancer types, and is strongly associated with poor patient prognosis. Although the BRAFV600E-elicited activation of ERK has been demonstrated to contribute to TERT reactivation by maintaining an active chromatin state, it still remains to be addressed how activated ERK is selectively recruited to mutant TERT promoter. Here, we report that transcription factor GABPA mediates the regulation of BRAFV600E/MAPK signaling on TERT reactivation by selectively recruiting activated ERK to mutant TERT promoter, where activated ERK can phosphorylate Sp1, thereby resulting in HDAC1 dissociation and an active chromatin state. Meanwhile, phosphorylated Sp1 further enhances the binding of GABPA to mutant TERT promoter. Taken together, our data indicate that GABPA and Sp1 synergistically activate mutant TERT promoter, contributing to tumorigenesis and cancer progression, particularly in the BRAFV600E-driven human cancers. Thus, our findings identify a direct mechanism that bridges two frequent oncogenic alterations together in TERT reactivation.The activating  Since telomeres shorten with every cell division, telomerase re-expression is required for maintaining stable telomeres and permissive for the indefinite cell growth in most advanced malignancies3. Telomerase reverse transcriptase (TERT) encodes the catalytic subunit of telomerase, and TERT reactivation is a key step in telomerase re-expression. Recently, there are many studies showing that TERT potentiates the oncogenesis not only by catalyzing the elongation of telomeres, which is the primary function of telomerase, but also by conferring proliferation advantages through directly regulating MYC stability and tRNA transcription5, suggesting its critical role in tumorigenesis and tumor progression.Telomeres are special DNA-protein structures located at both ends of eukaryotic chromosomes, protecting genomic material and sheltering chromosome ends from DNA damage response machineryTERT promoter mutations are frequently found in multiple cancer types and predict poor patient prognosis7. In particular, two hotspot somatic mutations precisely located at positions −124 or −146 bp upstream of the TERT transcription start site  were documented each to create a de novo ETS binding site (EBS), and enhanced transcriptional activity of TERT promoter7. Further studies demonstrated that these hotspot mutations selectively recruited transcription factor GABPA to promote TERT transcription8. In addition, mutant TERT promoter exhibited active chromatin marks, while its wild-type allele remained an inactive chromatin state9, indicating that these mutations initiated an epigenetic switch and the mono-allelic expression of TERT. There is also a study reporting that binding of GABPA to mutant TERT promoter can mediate the long-range chromatin interactions, facilitating the acquisition of an active chromatin state of TERT promoter and enhancing TERT transcription10. Collectively, these studies have highlighted the importance of TERT promoter mutations as a gate-keeper for TERT reactivation and tumor progression.Since TERT reactivation has played a pivotal role in maintaining the telomeres and thereby supporting infinite cell division in cancer cells, many researchers have sought to determine the genetic basis for TERT reactivation in different types of cancer. It should be noted that BRAFV600E mutation is another frequent genetic alteration that drives tumorigenesis and tumor progression, particularly in thyroid cancers and melanomas12. This mutation results in constitutively activating BRAF kinase, contributing to phosphorylation of the downstream mitogen-activated protein kinases (MAPK)13. The abnormal activation of BRAF signals regulates various pathways and has a vital role in tumorigenesis, and recent studies demonstrated that inhibitors targeting BRAF pathway showed promising clinical activities15. Co-occurrence of BRAFV600E and TERT promoter mutations has been found in 7.1–9.8% of papillary thyroid cancers (PTCs) and 13.6–20.7% melanomas17, which is closely correlated with worse prognosis and more aggressive pathological characteristics, suggesting a cooperative role between BRAFV600E signaling and TERT promoter mutations in cancer initiation and progression. A recent study has demonstrated that GABPB1L, a catalytic subunit of GABP, positively regulates TERT expression in a TERT promoter mutation-dependent manner18. Moreover, there is evidence indicating that the BRAFV600E/MAPK cascade enhances GABPB transcription by phosphorylating transcription factor Fos, thereby activating mutant TERT promoter through forming a hetero-tetrameric complex with GAPBA19. Another study has also revealed that the constitutively activated BRAFV600E/ERK signal can selectively regulate an active chromatin state on mutant TERT promoter by phosphorylating Sp1 and facilitating the dissociation of HDAC120. These observations indicate a direct link between BRAFV600E and TERT promoter mutations in activating TERT transcription. However, questions have been raised on how activated ERK selectively binds to mutant TERT promoter, while not its wild-type allele.TERT promoter by pioneer transcription factors. We demonstrate that transcription factor GABPA is required for recruiting activated ERK to mutant TERT promoter, thereby resulting in Sp1 phosphorylation, the consequent dissociation of HDAC1 and TERT activation. Our data also show that activated ERK signaling can enhance the binding of GABPA to mutant TERT promoter in an Sp1-dependent manner. Altogether, our results reveal that GABPA bridges the BRAFV600E and TERT promoter mutations together in TERT reactivation, and demonstrate that GABPA and Sp1 synergistically transactivate mutant TERT promoter.In this study, we attempt to fill this gap by testing the hypothesis that activated ERK may be recruited to mutant TERT promoter mutation8, and GABPA is a direct downstream target of MAPK/ERK signaling21. Besides, there is evidence showing that ERK can be recruited to specific target sequences and exert its function by its downstream target genes22. These observations motivated us to assume that GABPA may recruit activated ERK  to mutant TERT promoter, leading to the activation of TERT promoter. To prove this, we first determined the effect of GABPA depletion on protein expression and phosphorylation of ERK in four cancer cell lines harboring both BRAFV600E and TERT promoter mutations, including melanoma cell line A375, thyroid cancer cell lines BCPAP and 8305C, and breast cancer cell line MDA-MB-231. As shown in Fig. TERT promoter  only includes the DNA-binding domain (DBD), while the beta subunit of GABP (GABPB) contains the transcription activation domain (TAD)ERK Fig. , while cter Fig. , indication Fig. , suggestV600E-mediated MAPK/ERK signaling can affect the binding capacity of GABP to mutant TERT promoter. To address this, we treated A375, 8305C, BCPAP, and MDA-MB-231 cells with siRNA targeting BRAF (si-BRAF) or 100 nM MEK inhibitor trametinib for 6 or 24 h to block the ERK cascade. The results showed that treatment with si-BRAF or trametinib markedly inhibited ERK phosphorylation, while almost did not change GABPA expression , while markedly decreased the levels of phosphorylated Sp1 (p-Sp1)  Fig. . Correspter Fig. , indicatTERT promoter in the indicated cells, and this effect could be abolished by trametinib treatment  and threonines 453 and 739 (T453 and T739)33. T453 residue in the BQ region is a transactivating domain at the N-terminus of Sp1, while T739 residue is located in the D region, mediating the interaction between Sp1 and other transcription factors35. In addition, phosphorylation at T739 residue has been supposed to contribute to further Sp1 phosphorylation36.Given that the ERK-elicited phosphorylation of Sp1 is crucial for GABPA binding at TERT promoter compared to Sp1-WT  was required for ERK signal-mediated GABPA binding and the consequent activation of mutant TERT promoter.Considering that the binding of HDAC1 to Sp1 may potentially pose a steric hindrance for the interaction between GABPA and Sp1, thus we speculate that activated ERK may promote the interaction of Sp1 with GABPA, and the consequent recruitment of GABPA to mutant TERT promoter is crucial for TERT promoter activity. It has been reported that Sp1 and Sp3 can recruit HDAC1 to TERT promoter in normal human somatic cells, thereby repressing TERT transcription44, while Sp1 overexpression has been demonstrated to transactivate TERT in cancer cells45. As supported, our data showed that both Sp1 and GABP were involved in the BRAFV600E/ERK-elicited epigenetic regulation on mutant TERT promoter, which is crucial for TERT reactivation. In addition, we also found that Sp1 and GABP could both physiologically and functionally interact with each other to activate mutant TERT promoter in a synergistic manner.Binding of Sp1 to V600E/ERK signaling on mutant TERT promoter through recruiting p-ERK. In turn, activated ERK facilitates the interaction between Sp1 and GABPA by promoting Sp1 phosphorylation and the consequent dissociation of HDAC1, thereby stabilizing the binding of GABPA to mutant TERT promoter. Sp1 and GABP cooperatively maintain an active chromatin state in mutant TERT promoter. In conclusion, the present study reveals a novel mechanism underlying synergistic effect of BRAFV600E and TERT promoter mutations, two hotspot genetic altercations in human cancers, on TERT reactivation.Our study supports a model  was purchased from Selleck Chemicals LLC , and was dissolved in dimethyl sulfoxide (DMSO) with a stock concentration of 10 mM. The stock solution was diluted to 200 nM and stored at −80 °C before use.BRAFV600E and TERT promoter mutations in all cell lines have been analyzed by Sanger sequencing. All cell lines were mycoplasma free.Human thyroid cancer cell lines 8305C and BCPAP were kindly provided by Dr. Haixia Guan . Human melanoma cell line A375, gastric cell line AGS, colon cancer cell line RKO and mouse MC38 and B16F10 cells were obtained from the American Type Culture Collection (ATCC) . Human breast cancer cell line MDA-MB-231 was obtained from Shanghai Cell Bank, Chinese Academy of Sciences . A375, AGS, RKO, MC38, and B16F10 cells were cultured in DMEM medium with 10% FBS . BCPAP, 8305C, and MDA-MB-231 cells were maintained in RPMI-1640 medium with 10% FBS. The siRNAs for target genes or negative control were obtained from GenePharma  or RiboBio Co., Ltd. , and the sequences were presented in Supplementary Table 46. In brief, total RNA from harvested cells were extracted with Trizol reagent (Takara) according to the manufacturer’s protocol. First-strand cDNA was prepared with 500 ng total RNA by Revert Aid First Strand cDNA Synthesis Kit (ThermoScientific). q-RTPCRs were performed on a CFX96 real-time PCR-detection System (Bio-Rad Laboratories) using KAPA SYBR FAST qPCR Master Mix (Sigma). The mRNA expression of the indicated genes was normalized to β-actin. The primer sequences were listed in Supplementary Table Total RNA isolation, cDNA synthesis, and qRT-PCR were performed as previously described47. The harvested cells were lysed in ice-cold RIPA buffer with protease inhibitors. The protein lysates were separated on SDS–PAGE and then transferred to PVDF membranes (Roche Diagnostics). After blocked for 2 h in 5% bovine serum albumin (BSA) in 1 × TBS-T (0.5% Tween-20), the membranes were then incubated at 4 °C overnight with the indicated primary antibodies. Horseradish peroxidase (HRP)-conjugated secondary antibodies along with an ECL kit  were used to detect protein signals. Multiple exposures were taken using the Western Bright ECL detection system  to select images within the dynamic range of the film . Signals were normalized to GAPDH bands. The dilution ratio of antibodies for western blot analysis were shown in Supplementary Table The detailed procedure was carried out as previously described48. Luciferase activity analysis was performed according to standard procedures. Briefly, cells were plated to 70% confluency and transfected with pRL-TK and C228T-TERT-Luc or C250T-TERT-Luc plasmids using X-tremeGENE HP DNA transfection reagent (Roche) in a 12-well plate. For siRNA treatment, cells were transfected with different siRNAs for 24 h before transfection with luciferase plasmids. For trametinib treatment, cells were treated with 100 nM trametinib or DMSO for 24 h after transfection with luciferase plasmids. For overexpression experiments, cells were co-transfected with Sp1-WT or -T739A expression plasmid and luciferase plasmids. The above cells were collected 48 h post-treatment, and luminescence intensity was detected on EnSpire Multimode Plate Reader (PerkinElmer) using the dual-luciferase reporter assay system (Promega) according to the manufacturer’s instructions. The data were normalized against pRL-TK luciferase activity. Each assay was repeated in triplicate.The C228T/C250T-mutant and wild-type TERT-pGL4.10 luciferase reporter plasmids were generated and kindly provided by Prof. Levi A. Garraway TERT promoter using the Pierce Magnetic ChIP Kit (Pierce Biotechnology). The detailed procedure was described as previously49. Briefly, the harvested cells (about 1–2 × 107cells) were cross-linked using formaldehyde  for 10 min at room temperature, followed by quenching with glycine  for 5 min at room temperature. The cells were lysed with membrane extraction buffer and MNase digestion buffer for 10 min, and the whole-cell lysates were then sonicated with VCX-130PB  to fragment the chromatin to an average size of 300–500 bp. 10% of total chromatin from each lysate was used as input control, and the remaining 90% of chromatin was incubated overnight with 5 μg of indicated antibodies respectively in ChIP Buffer. Non-specific IgG was used as control. Immunoprecipitated protein DNA complex was then incubated with ChIP Grade Protein A/G Magnetic Beads for 2 h at 4 °C. Chromatin was eluted in ChIP Elution Buffer, and the proteins were removed with the addition of 200 mM NaCl and proteinase K (200 μg/mL) at 65 °C for 2 h. DNA was purified and used as templates for further analysis. Primer sequences for ChIP-qPCR analysis were listed in Supplementary Table The ChIP assay was performed to evaluate the recruitment of phosphorylated ERK (p-ERK), GABPA, and Sp1 to mutant or wild-type Cells with different treatments were harvested with the RIPA buffer. Whole lysates were then incubated with 2 μg of indicated primary antibodies for 1 h at 4 °C on a rotating wheel, followed by incubation with 20 μL of Protein A/G Agarose (ThermoScientific) overnight. After four washes and boiling of the beads, bead-bound proteins in the supernatants were analyzed by western blot analysis.50 according to the manufacturer’s guidelines. In brief, 48 h after transfection with the indicated siRNAs, the harvested 5 × 103 cells were collected and lysed in Lysis Buffer containing RNase inhibitor and incubated for 30 min on ice. The cell extracts were then centrifugation at 12,000 rpm for 10 min at 4 °C. The supernatants were stored at −80 °C for further analysis. The telomerase activities were then tested by SYBR Green RQ-TRAP assay. Briefly, the samples were incubated with TRAP master mix for 20 min at room temperature and amplified in 35 PCR cycles with 30 s at 95 °C and 90 s at 60 °C. The threshold cycle values were determined by standard curves. Each test was run in triplicate.The TRAP assay was performed as previously describedThe wild-type human Sp1 expression plasmid was obtained from Cyagen Biosciences . Site-directed mutagenesis of Sp1 Thr739 was performed using the QuikChange Lightning Site-Directed Mutagenesis Kit (Agilent Technologies) according to the manufacturer’s guide. The mutagenesis primer sequences were listed in Supplementary Table TERT promoter were predicted by using the transcription factor motif finder database (Jaspar), and the luciferase constructs with mutant Sp1 binding sites were obtained from TsingKe Biosciences .The potential Sp1 binding sites within the P-values were generated by two-tailed Student’s t-tests. P < 0.05 was considered significant.Data management and analysis were performed using the SPSS 22 and GraphPad Prism 6.0. All data in this study were presented as the mean ± SD of at least three independent replicates, and the Further information on research design is available in the supplemental materialReporting Summary Checklist


During neuronal differentiation, the transcriptional profile and the epigenetic context of neural committed cells is subject to significant rearrangements, but a systematic quantification of global histone modification changes is still missing. Here, we show that H3K79me2 increases and H3K27ac decreases globally during in-vitro neuronal differentiation of murine embryonic stem cells. DOT1L mediates all three degrees of methylation of H3K79 and its enzymatic activity is critical to modulate cellular differentiation and reprogramming. In this context, we find that inhibition of DOT1L in neural progenitor cells biases the transcriptional state towards neuronal differentiation, resulting in transcriptional upregulation of genes marked with H3K27me3 on the promoter region. We further show that DOT1L inhibition affects accessibility of SOX2-bound enhancers and impairs SOX2 binding in neural progenitors. Our work provides evidence that DOT1L activity gates differentiation of progenitors by allowing SOX2-dependent transcription of stemness programs. Neuronal differentiation requires rearrangement of the transcriptional and chromatin landscapes of neural cells. Here, the authors study in-vitro neuronal differentiation of murine embryonic stem cells (ESCs) to show that this process is modulated by DOT1L activity, which regulates H3K79me2 accumulation, and preserves accessibility of SOX2-bound enhancers. The process is accompanied by major rearrangements of the epigenetic and transcriptional profile, which are driven by the synergistic effects of epigenetic enzymes and transcription factors3, but few efforts have been directed towards a comprehensive description of global histone modification dynamics of neural committed cells4. Previous investigations were limited by the use of semi-quantitative and low-throughput methods . Recent developments in quantitative chromatin immunoprecipitation followed by sequencing (ChIP-seq) have overcome these technical limitations and they now allow detection of genome-wide global changes in histone modifications across conditions in a high-throughput manner7.Epigenetic and transcriptional changes driving neuronal differentiation have been characterized8. Among these, Disruptor of Telomeric silencing 1 Like (DOT1L) has been recently identified as a critical player in the differentiation process12. DOT1L is a highly conserved histone methyltransferase that catalyzes the mono-, di- and trimethylation of lysine 79 of histone H3 (H3K79me1/me2/me3)13. Since its first characterization in yeast as a disruptor of telomeric silencing upon gain or loss of function15, the protein has been recognized to be involved in a variety of biological processes, such as cell cycle control16, DNA repair13, gene expression17, differentiation, and reprogramming18. DOT1L regulates cardiomyocyte differentiation and maturation20 and chondrocyte differentiation21, while the modulation of its enzymatic activity was shown to be critical for cellular reprogramming efficiency9. Within the neural lineage, DOT1L prevents premature cell cycle exit and depletion of the neural progenitor pool and it is necessary for proper neuronal differentiation23.Various epigenetic enzymes are important for the orchestration of neuronal differentiationDot1l knock-down results in the establishment of repressive chromatin states around Myeloid/Lymphoid Or Mixed-Lineage Leukemia (MLL) target genes. This evidence suggests that the presence of H3K79 methylation may be critical to prevent deacetylation through the activity of SIRT1-complexes24. Loss of DOT1L activity also results in decreased acetylation and reduced frequency of promoter-enhancer interactions at H3K79me2-marked enhancers25. Currently, it is not clear whether the molecular perturbations described in leukemia are relevant for the differentiation phenotypes described in other model systems, and whether DOT1L activity targets enhancers in physiological developmental settings.DOT1L plays a prominent role in certain forms of leukemia. Interestingly, some studies in this field identified specific perturbations of the chromatin context that manifest upon blocking of DOT1L and indicate crosstalk between H3K79me2 and histone acetylation. Chen et al. show that 26. For both mESC and NPC48h, we further investigate whether competitive inhibition of DOT1L with Pinometostat (EPZ5676) affects the establishment of chromatin states and cell-type-specific transcriptional programs.In this work, we use mouse embryonic stem cells (mESC) and their in vitro derived neural progeny (NPC48h) to systematically characterize the global dynamics of the epigenetic landscape during neuronal differentiationWe show that the global levels of H3K79me2 increase genome-wide during the differentiation process. Our data indicate that DOT1L inactivation causes the onset of a transcriptional program which primes mESC-derived NPC towards neuronal differentiation. We further show that acute DOT1L inhibition is associated with reduced accessibility of intronic and intergenic enhancers that are bound in vivo by the stemness-conferring transcription factor SOX2.5 , transcriptomes and chromatin accessibility profiles of mESC and NPC48h treated with dimethyl sulfoxide (DMSO) or Pinometostat . To assess quantitative epigenetic changes, we used RELACS, a chromatin barcoding strategy for multiplexed and quantitative ChIP-seq5 Fig. .Fig. 1Exp-value < 0.01] to be prevalently upregulated in NPC48h compared to mESC   and transcriptional termination sites (TTS). . As expected, chromatin changes correlated with transcriptional changes 29  mediated in vitro neuronal differentiation  and that the interaction between H3K27ac and H3K79me2 is relevant to predict expression changes of genes driving neuronal differentiation.5.The computation of histone modification changes in a classic ChIP-seq experiment imposes a per-sample normalization that prevents the detection of global shifts. In contrast, the RELACS barcoding strategy we employed in this work allowed for quantitative estimations of genome-wide global epigenetic changes between samples. To assess the global dynamics of each histone PTM during the differentiation process, we estimated global scaling factors from sequencing data of control samples by computing pairwise ratios of input normalized read counts allocated to each sample after demultiplexingWe observed that H3K4me3, H3K4me1, and H3K27me3 levels in NPC48h did not deviate globally from mESC reference levels. H3K36me3 and H3K9me3 showed a mild global increase. Strong global changes were instead observed for H3K27ac and H3K79me2 during neuronal differentiation, with the former decreasing by a factor of ~2 (2.3 ± 0.12) and the latter increasing by a factor of ~4 (3.9 ± 0.05) Fig. .Fig. 2H3Global shifts of histone PTM levels can be caused by two possible mechanisms. (1) Histone marks can accumulate on specific loci, resulting in local enrichment compared to flanking regions . A global shift can occur if the number and magnitude of histone PTM local enrichment changes across conditions. In this work, we refer to this mechanism as a locally driven global change . Results indicated that H3K4me3 levels were unaffected upon differentiation in both background and locally enriched regions. H3K36me3 levels did not change globally in background regions but showed a mild increase in locally enriched regions. This indicates that H3K36me3 global change was mostly locally driven. In contrast, loss of H3K27ac and gain of H3K79me2 affected background and locally enriched loci to an almost equal extent. This observation indicates that the global changes measured for these two marks were genome-wide driven Fig. .In summary, our analyses show that H3K27ac and H3K79me2 levels change globally during in vitro neuronal differentiation, with opposite trends and through genome-wide acting mechanisms.30, but the functional relevance of the global H3K79me2 increase during neuronal differentiation remains to be clarified, particularly for transcription. Therefore, we asked whether differential H3K79me2 local enrichment, and/or the global H3K79me2 increase, associated with transcriptional dynamics.It has been reported that H3K79me2 local enrichment is the best linear predictor of gene expression levelsTo address this question, we stratified protein-coding genes in five clusters using standard log2-ratio scores between sequencing-depth normalized H3K79me2 and input, individually for each cell type. It should be noted that this approach corresponded to a traditional normalization absorbing all global changes. We quantified scores on a 3 kb window downstream of TSS of mESC and NPC48h Fig. . We obseTogether, these data show that, in our system, dynamic local enrichment of H3K79me2 associates with transcriptional activation of genes critical for neuronal development. Global accumulation of H3K79me2 did not associate with transcriptional changes.13. Yet, little is known about the causal relevance of DOT1L methyltransferase activity for the transcriptional process. To investigate the cell-type-specific causal contribution of DOT1L enzymatic function for the genome-wide transcriptional activity and for the overall epigenetic context, we inhibited the enzyme in mESC and NPC by treating cells for 48 h with the S-adenosyl methionine (SAM) competitor Pinometostat . Subsequently, we quantified transcriptional and epigenetic changes compared to cells treated with DMSO as control.In yeast, fly, mouse, and human, H3K79me2 is generally associated with transcriptional activity. The mark localizes to euchromatic regions and its enrichment strongly correlates with gene expression levelEPZ treatment successfully inhibited DOT1L methyltransferase activity, as shown by the reduction of all three degrees of H3K79 methylation  and sample clustering on normalized count data   identified significant pathways sharing an immunological and stress-induced pro-apoptotic molecular signature , and vanishingly small in NPC48h (β = 0.004)   were directly linked to transcriptional changes, as indicated by the small effect size and the specific association with deregulated genes.34. We used the chromatin segmentations of the control samples from mESC and NPC48h to measure the fraction of each chromatin state overlapping the promoter and the gene body of protein-coding genes genome-wide. We applied t-distributed stochastic neighbor embedding (tSNE) to visualize the distribution of genes in a reduced space35. Mapping of the DEG set revealed a clear separation between upregulated and downregulated genes, which was consistent across cell types  regulating the subset of transcriptionally affected genes.25 reported that accessibility of H3K79me2-marked enhancer and enhancer-associated H3K27ac decrease as a consequence of DOT1L inhibition in leukemia models. Therefore, we analysed how the accessibility profile was affected on enhancer regions genome-wide as a consequence of EPZ treatment during neural differentiation.To explore the hypothesis that DOT1L inactivation may affect specific DNA-binding TF, resulting in targeted gene expression changes, we profiled chromatin accessibility via ATAC-seq in mESC and NPC48h treated with DMSO or EPZ. Godfrey et al.To study if the accessibility of enhancer regions was perturbed in mESC and NPC48h upon DOT1L inhibition, we performed differential analysis of open chromatin regions between treatment groups. Similar to previous assays, we observed minor alterations of the accessibility landscape, with very few regions reaching statistical significance. In mESC, DOT1L inhibition only results in minor changes in the accessible chromatin landscape, as indicated by the small fraction of total variance accounted for by the second principal component of ATAC-seq data  were enriched for motifs of pluripotency factors, such as NANOG and POU5F1 for mESC, and a range of POU/SOX core motifs for NPC48h Fig. .37 , but we observed a significant difference between intronic and intergenic enhancers .Godfrey et al. reported on the association between affected enhancers upon EPZ treatment and loss of H3K79me2 and H3K27acNext, we restricted our analysis to introns of protein-coding genes. We observed that 62% of protein-coding genes having at least one ATAC peak with decreased accessibility were marked with H3K79me2, while only 25 and 34% of protein-coding genes associated with ATAC-Up and background-ATAC regions were marked with H3K79me2  are decreased in differentiated cells compared to the embryonic precursors4.Various studies have documented a progressive chromatin condensation during mESC differentiationIn contrast to these previous studies, here we use a quantitative ChIP-seq protocol (RELACS) to estimate global histone modification changes. Our main finding is that only H3K27ac and H3K79me2 levels change globally, in opposite directions, during in vitro neuronal differentiation, through genome-wide acting mechanisms.44. Loss of H3K27ac is consistent with chromatin condensation1, as H3K27ac is a mark associated with loose chromatin packaging and is known to be highly abundant in mESC45.Biologically, these results show that the progressive chromatin condensation observed during in vitro neuronal differentiation mostly follows from a genome-wide deacetylation process, while a smaller contribution may come from the global accumulation of repressive histone modifications 46 have recently shown that DOT1L localization and activity is controlled by CDK1 in mESC, causing global epigenetic effects during differentiation. The proposed mechanism is consistent with the global H3K79me2 increase that we observe during neuronal differentiation. Developmental gain of local enrichment of H3K79me2 associates with transcriptional activation of genes critical for neuronal differentiation, although the accumulation may be a byproduct of transcription. Previous reports have shown that the ectopic localization of DOT1L to the promoter region of few target genes, and the consequent accumulation of H3K79me2, is not sufficient to initiate transcription47.Secondly, our data show that H3K79me2 increases globally during neuronal differentiation in vitro. Michowski et al.24. In NPC48h, DOT1L inhibition seems to prime the transcriptome of neural progenitors towards neuronal differentiation. Although the transcriptional deregulation is moderate in effect size, epigenetic changes are associated with expression dynamics, suggesting that the altered transcriptional state induced by DOT1L inactivation might be epigenetically memorized and preserved through mitotic divisions.As a consequence of H3K79me2 global increase, we investigate the relevance of DOT1L methyltransferase activity for the establishment of chromatin and transcriptional states in mESC and their differentiated progeny, NPC48h. The third main result of this study shows that DOT1L inactivation affects gene expression in a targeted manner, despite the genome-wide depletion of H3K79me2. Our results also indicate that the presence of H3K79me2 is neither critical for the deposition of other histone modifications, nor necessary for sustaining the expression levels of most genes. We observe depletion of H3K27ac upon EPZ treatment, but this does not follow the global decrease in H3K79me2. Locally, however, loss of H3K27ac on enhancers and promoters alike correlates with transcriptional downregulation, and it is mirrored by a corresponding decrease in H3K4me3 on promoters. These data argue against the hypothesis that H3K79me2 is generally critical to preserve H3K27ac from deacetylase complexes in our modelJarid2, Zfp462)51.Interestingly, transcriptionally deregulated genes present a coherent chromatin signature. Our data indicate that DOT1L inactivation associates with upregulation of genes with a repressed, poised or null promoter state, and downregulation of highly expressed genes marked with active histone modifications. Based on the data presented in this work, it is tempting to hypothesize that the transcriptional upregulation upon DOT1L inhibition observed in the mammalian system may result from impaired targeting of repressive complexes. The cause of this may either reside in the altered H3K79me distribution, as in the yeast model, or it may indirectly follow from the selective downregulation of highly transcribed genes coding for repressive proteins  may result in the derepression of silent genes localized on facultative heterochromatic regions.In conclusion, our findings agree with the model proposed by Godfrey et al. in that DOT1L inhibition results in decreased accessibility of some H3K79me2-positive enhancers. In our system, though, we observe a specific response that pertains to a subset of regulatory regions bound by sequence-specific transcription factors . The closure of these enhancers may be responsible for the transcriptional decrease of highly expressed genes conferring stemness to progenitors. In addition, here we present evidence that could explain transcriptional increase upon DOT1L inhibition. We hypothesize that decreased expression of cell-type-specific transcripts coding for proteins with repressive functions  were cultured on inactivated MEF for 3 passages (p3) and from p4 onward on gelatin-coated plates , 15% FBS (Thermo Fisher), 1% Glutamax (Thermo Fisher), 1% PSN (Thermo Fisher), 1% NEAA (Thermo Fisher) + LIF (Sigma) (dilution = 1/1000) + β-Mercapto-EtOH (Thermo Fisher) (dilution = 1/500)). Feeder-free mESC were treated with either EPZ5676 (Hycultec) (10 μM), or DMSO (Thermo Fisher) (dilution = 1/1000) for 48 h.mESC 26. In brief, feeder-free mESC were trypsinized and dissociated to create a single-cell suspension. Cells were used to form cell aggregates (CA) on non-adherent (Grunier) plate ). Four days after CA formation, CA were exposed to RA (7.5 µM) for 4 days. CA were dissociated into single cells and seeded on PORN/LAMININ coated 6-well plates and grown in N2 medium for neuronal differentiation. At this stage, cells were treated either with EPZ5676 (10 μM) or DMSO (1/1000) for 48 h. At treatment completion, NPC48h were collected for downstream processing.mESC were differentiated in vitro towards NPC48h according to Bibel et al.5. Cells were fixed in 1% formaldehyde for 15 min. The reaction was quenched with 125 mM glycine for 5 min, followed by two washings with DPBS + proteinase inhibitor cocktail. Cell nuclei were isolated following Nexson protocol53 and permeabilized with 0.5% SDS. Chromatin was digested in situ using restriction enzyme CviKI-1  and barcoded using RELACS custom barcodes . Libraries were generated using the NEBNext Ultra RNA Library Prep Kit, following manual’s instructions. Libraries were sequenced on a HiSeq 3000 Illumina machine .54. In brief, ~50,000 cells were washed in ice-cold PBS and incubated in the transposition reaction mix (Nextera DNA Sample Preparation Kit). Transposed DNA was purified  and PCR amplified for 5 cycles. We determined the number of additional PCR cycles via qPCR according to54. Libraries were sequenced on a HiSeq 3000 Illumina machine .ATAC-seq libraries were generated according to55. The code used for the analysis of densitometric data is available at https://github.com/FrancescoFerrari88/code_DOT1L_paper/blob/master/figure_3/NOTEBOOKS/3a-3b_EPZ_vs_DMSO_H3K79me2_GLOBAL_BULCK_%26_LOCUS-SPECIFIC_CHANGES.ipynb.mESC or NPC48h were lysed in RIPA buffer . Cells were centrifuged 10 min, 13,000 rpm) and the supernatant collected. Protein concentrations were determined with Bradford reagent (Bio-Rad). Fifteen micrograms of protein extract were loaded with 5x Laemmli buffer on Mini Protean TGX gels (Bio-Rad) and run at 100 V for 1.5 h. Proteins were transferred to PVDF membranes (Trans-blot Turbo Transfer Pack) using the Trans-blot Turbo Transfer System (both from Bio-Rad) following manufacturer’s instructions. Membranes were blocked with 5% BSA in TBS-T (blocking buffer) for 1 h and incubated overnight with primary antibodies (diluted in blocking buffer). Membranes were washed, incubated with secondary antibodies for 1 h and detected using ECL or Femto substrates (Thermo Scientific) and LAS ImageQuant System . Antibodies details are listed in Supplementary Table ,000 rpm 23 using following deviating buffer recipes: lysis buffer  sodium dodecyl sulfate (SDS), and 1× Protease inhibitor) dilution buffer  Triton X-100, 0.1% (w v−1) SDS, and 1× Protease inhibitor). NPC48 cells grown on 1 × 10 cm dish (~6 Mio cells) treated for 48 h with 10 μM EPZ5676 or DMSO were used for one SOX2- and control-ChIP. Two micrograms of the following antibodies were used per ChIP: anti-SOX2 , mouse IgG . Significance of SOX2 binding to the specified loci between EPZ5676 treatment and control was calculated with a one-sided paired student t-tests. For that, four independent biological replicates were compared. The following primers were used for detection:ChIP-qPCR was performed as described previouslyJarid2_1 , Jarid2_2 ,Fgfr2 ,Msi2 ,Pantr1 ,Schip1 .56. Relevant parameters used for each experiment and summary QC are available at https://github.com/FrancescoFerrari88/code_DOT1L_paper/tree/master/multiQC_ConfigParameters. Mapping was performed on mouse genome build mm10 (GRCm38). For ChIP-Seq and ATAC-seq, high quality and uniquely mapping reads were retained (mapq > 5). RELACS custom barcodes were designed with integrated UMI, so duplicate removal was performed using UMITools57, while a standard deduplication was applied for ATAC-seq reads. We use gencode M18 as a reference gene model throughout all analysis. For ChIP-seq and ATAC-seq data, snakePipes also provided candidate peak regions using MACS2 (v. 2.2.6) using default parameters.All sequencing data were processed with snakePipes (v. 1.1.1)58 on count matrices output from snakePipes . We used a linear model controlling for batch effects  and we applied apeglm log2 fold-change shrinkage59.Differential analysis for RNA-seq was carried out using DESeq2 (v. 1.22.1)We estimate fold-changes for each histone modification on annotated genomic features known to associate with local histone PTM enrichment ; H3K79me2, H3K27me3, H3K9me3: extended promoter ; H3K36me3: transcription termination site ).60 and differential regions identified via DESeq2. We eventually applied normal log2 fold-change shrinkage. Peaks were annotated using Homer (v. 4.10)61 and UROPA (v. 3.1.0)62. We use GimmeMotifs (v. 0.13.1) for motif enrichment and differential motif analysis63. Metaprofile of ChIP-seq and ATAC-seq signals were generated with deeptools (v. 3.1.2)60 and deepStats (v. 0.3.1)64. The heatmap combining ChIP-seq and RNA-seq data was generated with Ultraheatmap (v. 1.3.0)65.Global differential ChIP-seq analysis was carried out after applying RELACS specific normalization by computing empirical log-fold changes across conditions . Traditional differential ChIP-seq and ATAC analysis was performed on consensus peak sets, coverage was computed using deepTools’ multiBamSummary66. The algorithm was run on a matrix of shape 4 (samples) × 3500 (features). As features, we included the top 500 most variable 2 kb loci for each of the seven histone modifications (feature groups), selected after applying variance stabilizing transformation to the counts matrix. We used scikit-learn (Python module) (v. 0.19.1) for PCA and tSNE, while linear modeling was performed using sklearn and statsmodels (v. 0.9.0). GO enrichment analysis and pathway analysis were performed using clusterProfiler (v. 3.10.1)67.Multiple factor analysis was done using FactoMineR (v. 1.41)33 with default parameters for chromatin segmentation. We trained two independent models for each cell type on the DMSO treated samples. We then used these models to perform the segmentation in the respective cell types for both treatments (EPZ and DMSO).We used ChromHMM (v. 1.15)35. For each gene, we identify potentially used transcripts by intersecting annotated TSS with H3K4me3 peaks. If a gene does not overlap with any H3K4me3 peak, we consider the full gene annotation. For each candidate gene, we then compute the fraction of overlap between each chromatin state segment in the control sample with the promoter region  and with the full gene body. In this way, each gene is identified by a vector of length 30 (15 states for the promoter + 15 states for the gene body). A matrix of shape g (number of genes per cell) × 30 is eventually used for dimensionality reduction by applying tSNE68.We compute the chromatin state signature of protein-coding genes in mESC and NPC48h according to69. The expected log2 fold-change for each group of genes (i) identified by the most represented chromatin state present in the promoter regions was identified by fitting the following hierarchical linear model:Bayesian linear modeling was performed using pymc3 (v. 3.6)All visualizations were generated in Python (v. 3.6) and R (v. 3.5).To estimate global histone modification changes, first we demultiplexed fastq files on RELACS custom barcodes. Then, for each sample, we divided the number of uniquely and high-quality mapped read-pairs (mapq > 5) coming from a ChIP of interest by the total number of read-pairs coming from the respective input. For estimating global histone modification changes, we considered the total number of mapped reads genome-wide. Pairwise quantitative comparisons between samples were computed as log2 ratio between input-normalized total mapped read counts.Local changes were estimated in the same way, by repeating this procedure for each individual bin of interest.All sequencing experiments presented in this paper have been conducted once, with the appropriate biological replicates included in the experimental run. Immunoblotting was replicated at least twice, with each replicate including all biological replicates shown in the study. SOX2 ChIP followed by qPCR was performed once.Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Most histones, also known as canonical histones, are synthesized and deposited onto chromatin only during S phase when DNA is being replicated. There are also other histones, known as histone variants, that are expressed and synthesized in a cell cycle-independent manner. Incorporation of histone variants into nucleosomes provides a means to regulate the chromatin architecture in addition to other epigenetic mechanisms. Histone variants differ from canonical histones in just a few amino acids or large fractions of polypeptides. One of the histone variants, H3.3, has only four- or five-amino acid residue difference from canonical H3, namely H3.1 and H3.2, respectively. Three amino acids are located in the histone core domain important for binding to H3.3-specific histone chaperones. The only different residue at the N-terminus of the protein is at position 31, which is an Alanine in H3.1 and H3.2 but replaced by highly conserved Serine in H3.3. Phosphorylation of H3.3S31 was first reported 15 years ago,1 however, the function of this modification remains largely unknown.Phosphorylation of histone proteins is involved in multiple cellular functions such as mitosis, DNA damage response, and transcriptional regulation. For example, phosphorylation of H3.3 Ser31 (H3.3S31ph) is known as a mitotic epigenetic modification.By mass spectrometric analysis of purified histones from primary mouse bone marrow-derived macrophages (BMDMs), Armache et al. detected an increase of H3.3S31ph in lipopolysaccharide (LPS)-stimulated cells. Using a homemade H3.3S31ph specific antibody, they further corroborated that H3.3S31ph is a stimulation-associated histone modification in four additional cell types responding to different physiological stimuli. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) analysis revealed an enrichment of H3.3S31ph specifically in LPS stimulation-induced genes. The enrichment of H3.3S31ph in gene bodies was abolished or significantly reduced by treating the cells with P-TEFb inhibitor flavopiridol or TOP1 inhibitor camptothecin, both of which block the elongation of RNA polymerase II (Pol II), suggesting a direct link between H3.3S31ph and transcription elongation. Previous studies described that several kinases, such as IKKα, CHK1, and aurora B, are able to phosphorylate H3.3S31. A series of genetic perturbation using short hairpin RNA (shRNA) and pharmacological inhibition experiments suggested that IKKα is likely responsible for the deposition of co-transcriptional H3.3S31ph at inflammatory genes.The distribution pattern of H3.3S31ph in gene body is reminiscent of that of another transcription elongation-associated histone modification, H3K36me3. H3K36me3 is mediated by a single histone methyltransferase, SETD2. Given this link between H3.3S31ph and H3K36me3 as well as their physical proximity on the H3.3 tail, Armache et al. hypothesized that H3.3S31ph may provide stimulation-induced genes with the capacity for augmented transcription, in part through the stimulation of H3K36me3. Indeed, the crystal structure of the human SETD2 catalytic domain bound to a H3.3 peptide bearing S31ph and related biochemical data supported this hypothesis. Two basic residues of SETD2, K1600, and K1673, provide positive charge recognizing the phosphorylated H3.3S31, thus promoting the engagement of H3.3K36 at the catalytic site for methylation. Consistent with the structural observation, SETD2 showed higher catalytic activity on the phosphomimic H3.3S31E-containing nucleosomes over unmodified nucleosomes; however, the stimulatory effect was lost by mutating any of the two basic lysine residues to glutamine (K1600E or K1673E). Together, these data suggest that IKKα stimulates target gene expression by H3.3S31ph-medaited augmentation of SETD2 activity.3 In addition to K36me3, unmodified S31 also contributes to the binding. Structural and biochemical data suggested that a phosphate at Ser31 impedes ZMYND11 binding to H3.3K36me3. Consistent with these in vitro data, inflammatory genes tend to have pre-existing H3.3K36me3 with coincident ZMYND11 occupancy in resting BMDMs; whereas upon stimulation, ZMYND11 is rejected from these genes. This is likely a consequence of IKKα-mediated H3.3S31ph, as ZMYND11 is retained at inflammatory genes in cells treated with an IKK inhibitor before LPS stimulation. Finally, to directly define the function of H3.3S31ph in inflammatory gene induction, Armache et al. generated H3f3a H3f3b double knockout (DKO) RAW264.7 macrophage cell lines using CRISPR-Cas9 and rescued the DKO cells with the wild-type, H3.3S31A (loss of function) and H3.3S31E  transgenes. Overall, LPS-induced gene expression in H3.3 DKO macrophages was increased by wild-type H3.3 or H3.3S31E transduction, but not by the H3.3S31A mutant, suggesting an essential role of H3.3S31ph in promoting rapid stimulation-induced transcription in macrophage-like cell lines.Previously, we and others identified ZMYND11 as an H3.3K36me3 specific reader that functions as a transcriptional corepressor and tumor suppressor.1 its function in interphase remained unknown. Studies from two laboratories recently showed that H3.3S31ph stimulates the catalytic activity of histone acetyltransferase p300, thus maintaining enhancers at an open, acetylated chromatin state permissive to the embryonic development program.5 In the current study, Armache et al.2 discovered a novel role of H3.3S31ph in transcription elongation. It not only augments the catalytic activity of SETD2, but also ejects negative regulator ZMYND11. Thus, together with H3.3K36me3, H3.3S31ph provides an epigenetic signature in gene bodies promoting rapid and high-level expression of stimulation-induced genes (Fig. Despite that H3.3S31ph was first reported as a mitotic epigenetic modification 15 years ago,