Streptococcus pneumoniae TIGR4 by applying a combination of high-throughput RNA-sequencing techniques. We successfully map 1864 high confidence transcription termination sites (TTSs), 790 high confidence transcription start sites (TSSs) , and 1360 low confidence TSSs (74 secondary and 1286 primary) to yield a total of 2150 TSSs. Furthermore, our study reveals a complex transcriptome wherein environment-respondent alternate transcriptional units are observed within operons stemming from internal TSSs and TTSs. Additionally, we identify many putative cis-regulatory RNA elements and riboswitches within 5’-untranslated regions (5’-UTR). By integrating TSSs and TTSs with independently collected RNA-Seq datasets from a variety of conditions, we establish the response of these regulators to changes in growth conditions and validate several of them. Furthermore, to demonstrate the importance of ribo-regulation by 5’-UTR elements for in vivo virulence, we show that the pyrR regulatory element is essential for survival, successful colonization and infection in mice suggesting that such RNA elements are potential drug targets. Importantly, we show that our approach of combining high-throughput sequencing with in vivo experiments can reconstruct a global understanding of regulation, but also pave the way for discovery of compounds that target (ribo-)regulators to mitigate virulence and antibiotic resistance.Efficient and highly organized regulation of transcription is fundamental to an organism’s ability to survive, proliferate, and quickly respond to its environment. Therefore, precise mapping of transcriptional units and understanding their regulation is crucial to determining how pathogenic bacteria cause disease and how they may be inhibited. In this study, we map the transcriptional landscape of the bacterial pathogen  Streptococcus pneumoniae. We find that the majority of multi-gene operons have alternative start and stop sites enabling condition specific regulation of genes within the same operon. Furthermore, we identified many putative RNA regulators that are widespread in the S. pneumoniae pan-genome. Finally, we show that separately collected RNA-Seq data enables identification of conditional triggers for regulatory RNAs, and experimentally demonstrate that our approach may be used to identify drug-able RNA targets by establishing that pyrR RNA functionality is critical for successful S. pneumoniae mouse colonization and infection. Thus, our study not only uses genome-wide high-throughput approaches to identify putative RNA regulators, but also establishes the importance of such regulators in S. pneumoniae virulence.The canonical relationship between a bacterial operon and the mRNA transcript produced from the operon has become significantly more complex as numerous regulatory mechanisms that impact the stability, translational efficiency, and early termination rates for mRNA transcripts have been described. With the rise of antibiotic resistance, these mechanisms offer new potential targets for antibiotic development. In this study we used a combination of high-throughput sequencing technologies to assess genome-wide transcription start and stop sites, as well as determine condition specific global transcription patterns in the human pathogen  The transcriptional architecture of bacterial genomes is far more complex than originally proposed. The classical model of an operon describes a group of genes under the control of a regulatory protein where transcription results in a polycistronic mRNA with a single transcription start site (TSS) and a single transcription terminator site (TTS) . HoweverTrans–acting small RNAs (sRNAs), which are found in intergenic regions . M1. M1pyr mf” state  is indeef” state . M2 trigr operon  and M3 hr operon .S. pneumoniae is partially regulated by a two-component system (SP_2192–2193) and that genes in this pathway are important for growth 64] with RNA-Seq libraries were generated by using the RNAtag-Seq protocol ,44. Brie620 = 0.4) using the QiaShredder and RNeasy Kit (Qiagen) by following the kit instructions with the exception of the lysis step, which was performed by membrane disruption using zirconia/silica beads. After extraction, RNA was treated with DNA Free DNA removal kit (Thermo) to remove DNA. RNA-seq library was generated via ScriptSeq vs RNA-seq library kit (epicenter) using 1 ug RNA. The kit instructions were followed using AMPure beads (Agencourt) for cDNA and library purification. Each replicate was individually indexed using Failsafe PCR enzyme (Epicentre). Completed libraries were analyzed for insert size distribution on a 2100 BioAnalyzer High Sensitivity kit . Libraries were quantified using the Quant-iT PicoGreen ds DNA assay . One hundred cycle paired end sequencing was performed on an Illumina HiSeq 4000.RNA was extracted in triplicate from cells grown to mid-logarithmic phase (ODterm-seq libraries were generated as previously described  with fewS. pneumoniae TIGR4 (NC_003028.3) genome annotation was supplemented with all known structured ncRNAs and sRNAs. Homologs of known ncRNA families deposited in Rfam [S. pneumoniae [S. pneumoniae TIGR4 (NC_003028.3) genome using the in-house developed Aerobio pipeline. Aerobio runs the processing and mapping in two phases. Phase 0 employs bcl2fastq to convert BCL to fastq files, quality control and de-multiplexing and compilation of the reads based on the sample conditions. Phase 1 maps the de-multiplexed reads against the genome, under default parameters, using Bowtie2 [The  in Rfam ,66 were  in Rfam . sRNAs peumoniae ,18 were eumoniae . Coordin Bowtie2  and stre Bowtie2  to generPerl code from  was adaphttps://github.com/nikhilram/T4pipeline.Regions up to 100 nucleotides upstream of the predicted high confidence TSSs were split into three datasets—TSS-20; TSS-20-50; and TSS-100, and searched for enriched promoter motifs and transcription factor binding sites listed in  using MEPaired-end reads were mapped to the T4 genome using TopHat2 . The BAMOnce the TSSs were identified, 5’-UTR regions with a length of at least 70 nucleotides were scanned for mapped 3’ end sequencing reads with a minimum coverage of 2 to identify putative early terminators. 5’-UTR regions with a predicted early TTS were binned as candidate regulatory elements. The nucleotide sequence for each candidate element was obtained and folded using RNAFold . SecondaThe response of candidate regulatory elements to different media conditions were assessed by calculating the RNA-Seq coverage in both the regulatory element and the regulated gene. Read-through was calculated for each of the candidates as described previously . BrieflyS. pneumoniae strains available in Refseq 77 [-4.A local BLAST  databaseefseq 77  and 350 efseq 77 . Each ofRNA was isolated from cultures using the Qiagen RNeasy kit (Qiagen). DNase treated RNA was used to generate cDNA with iScript reverse transcriptase supermix for RT-qPCR (BioRad). Quantitative PCR was performed using a Bio-Rad MyiQ. Each sample was normalized against the 50S ribosomal gene, SP_2204 and were measured in biological replicate and technical triplicates. No-reverse transcriptase and no-template controls were included for all samples. Primers used in this study are listed in S. pneumoniae TIGR4 genomic DNA (GenBank accession number NC_003028.3). Mutations to the pyrR RNA element were obtained either by site-directed mutagenesis or by PCR assembly using appropriate primers . Growth assays were performed no less than three times.Wild type and pyrR RNA mutants of T4 were grown for 2 hours and diluted to an OD1 x 1 competition experiments were performed with pyrR RNA mutants (M1 to M3) that were competed against the wild-type strain after which bacterial fitness was calculated as previously described  with a fS1 FigA. Frequency distribution of the distance between the TSS position predicted for each gene between conditions. This also takes into account whether TSSs were predicted. Majority of the predictions match exactly or are very close to one another between the conditions providing confidence in the TSS predictions from the pooled data. B. Predicted TSSs show enrichment of the TATA box at the -10 and the TTGACA at the -35 positions respectively as identified by MEME [ by MEME .(TIF)Click here for additional data file.S2 Fig(A) StringTie reconstructed transcript grouping genes SP_0001 –SP_0014. Log transformed paired-end RNA-Seq coverage map across this region. Red and Green lines represent the predicted TSSs and TTSs (B) The pie chart describes the distribution of the types of operons present in T4 identified using single-end RNA-Seq analyzed with Rockhopper. A total of 474 multigene and 773 single gene operons were identified, which can be divided up in 63% simple operons , 29% complex operons , 4% traditional operons , 1% multiTSS operons (blue), and 3% multiTTS operons (red). (C) The clustered histogram describes the distribution of genes and transcriptional features in non-traditional operons described by Rockhopper, where gray represents the numbers of genes in the multigene operons, green represents the number of TSSs within operons, red represents the number of TTSs within operons. Two-gene operons are found most frequently in the non-traditional operons with one internal TSS and TTS.(TIFF)Click here for additional data file.S3 FigA. Frequency distribution of the candidates across the surveyed genomes. B. Conservation of the candidates as a measure of the mean p-Distance within each candidate cluster.(TIF)Click here for additional data file.S4 FiglacZ represents a β-galactosidase reporter construct without a regulatory region upstream, which was used as a negative control. Error bars represent standard error of the mean across three technical replicates.The activity of the wild type pyrR decreases in the presence of uracil, while M1 is insensitive to the ligand. Activity of mutant M2 results in increased Miller units even in the presence of uracil. Empty (TIF)Click here for additional data file.S1 TableThe 742 high confidence primary TSSs (sheet 1), the 48 putative secondary high confidence TSSs (sheet 2), 1286 low confidence primary TSSs identified (sheet 3), and the 74 low confidence secondary TSSs (sheet 4), their Processed/unprocessed ratios and coverages, and the lengths of the 5’-UTR thus formed are listed. Additionally, a description of whether the TSS is intergenic or within an upstream coding sequence is provided.(XLSX)Click here for additional data file.S2 TableThe positions of all 1864 enriched sense TTSs, and the coverage of each site pooled from all the samples are listed. Lengths of the 3’-UTRs are also listed for the sense TTSs. The predicted stem-loop structure and the number of uridines upstream of each TTS is also shown.(XLSX)Click here for additional data file.S3 TableA description of the 388 multi-gene operons identified by StringTie (sheet 1) and the 474 multi-gene operons identified by Rockhopper (sheet 2). Number and locus tags of genes that are in operons are listed.(XLSX)Click here for additional data file.S4 TableDetailed description of the candidate 5’-UTR regulatory elements for 141 genes. Position of the TSS and its Processed/unprocessed ratio, early TTS and average coverage for each are described. Additionally, length of the 5’-UTR as well as whether it is intergenic or within the upstream coding sequence is provided.(XLSX)Click here for additional data file.S5 TableList of adapters used for 5’ end-Seq. Highlighted in red are the barcode sequence (5’-3’).(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S1 Methods(DOCX)Click here for additional data file.


Chromatin remodeling is linked to circadian regulation in mammals, but it is unknown whether cycles in chromatin accessibility are a general feature of clock-regulated genes throughout evolution. To assess this, we applied an ATAC-seq approach using Nematostella vectensis, grown under two separate light regimes (light:dark (LD) and constant darkness (DD)). Based on previously identified N. vectensis circadian genes, our results show the coupling of chromatin accessibility and circadian transcription rhythmicity under LD conditions. Out of 180 known circadian genes, we were able to list 139 gene promoters that were highly accessible compared to common promoters. Furthermore, under LD conditions, we identified 259 active enhancers as opposed to 333 active enhancers under DD conditions, with 171 enhancers shared between the two treatments. The development of a highly reproducible ATAC-seq protocol integrated with published RNA-seq and ChIP-seq databases revealed the enrichment of transcription factor binding sites , which have not been previously associated with circadian signaling in cnidarians. These results provide new insight into the regulation of cnidarian circadian machinery. Broadly speaking, this supports the notion that the association between chromatin remodeling and circadian regulation arose early in animal evolution as reflected in this non-bilaterian lineage.In animals, circadian rhythms are driven by oscillations in transcription, translation, and proteasomal degradation of highly conserved genes, resulting in diel cycles in the expression of numerous clock-regulated genes. Transcription is largely regulated through the binding of transcription factors to  Nematostella vectensis, model organism.The phenotype of an organism cannot be fully explained by its genome; it is now clear that other factors contribute to the ecology and evolution of animals. The DNA molecule is exceptionally long and has to fit into a small nucleus; therefore, the DNA is wound around proteins called histones in a unique architectural complex called a nucleosome (that further forms the chromatin). This DNA structure plays a crucial role in gene regulation, as nucleosomes alternate accessibility to regulatory sites, that in turn navigate expression patterns that allow gene expression or suppression. It is well known that chromatin remodeling is associated with circadian regulation in mammals, but it was uncertain whether cycles in chromatin accessibility are a conserved feature of clock-regulated genes throughout the animal kingdom. We used ATAC-seq and RNA-seq data to show that the association between chromatin remodeling and circadian regulation arose early in animal evolution as reflected in the sea anemone,  Drosophila . To. ToN. veMYB motif, enriched in LD-specific peaks, was shown to have an essential role in circadian rhythm maintenance in Arabidopsis [MYB motif was enriched around the promoters of differentially expressed (DE) genes under LD conditions, particularly within clusters 1, 2, and 5. Another example is the homeobox motifs, enriched in both the LD and DD treatments, as well as around genes from clusters 1, 3, 4 and 5. Homeobox factors, in particular members of the NK homeobox gene family, with motifs enriched in both treatments, have been shown to contribute to rhythmic regulatory processes in mammals [NvClock (a core component of the circadian clock machinery), contain the binding motif of C/EBP in their accessible promoter region , which overlaps our ATAC-seq results from CT13 that shows its promoter to be accessible  [NvClock, NvCry1, and NvCry2 are aligned with the correlation between chromatin accessibility measured by ATAC-seq and expression profiles measured by RNA-seq  , prior t RNA-seq . Further mammals .in vivo [N. vectensis during different early development stages, our analysis identified 259 LD-treated and 333 DD-treated enhancers that overlap with the H3K27ac histone mark, and a total of 174 enhancers shared between the two treatments  and median depth of 10 million high-quality unique reads that represent the accessible chromatin of N. vectensis genome, to uncover valuable information about the active CREs and the TFs that bind them, and further enable us to examine the relationship between CREs and gene expression. Among the two light regimens surveyed, 8366–36582 peaks with IDR cut-off of 0.05 were identified, enabling the prediction of TFs binding sites within the accessible genome, specifically around rhythmic genes. The overlaps of ATAC-seq libraries with rhythmic genes lead us to conclude that there is an association between gene expression and DNA accessibility in N. vectensis, as presented in our time point sampling.In this work, we aim at illuminating the landscape of accessible chromatin, within the N. vectensis gene set. Our results showed a strong enrichment of rhodopsin-like GPCRs, particularly in the DD treatment. The rhodopsin-like GPCRs represent a diverse protein family that includes hormone receptors, neurotransmitters and photoreceptors, all of which transduce extracellular signals through interactions with nucleotide-binding proteins [To identify groups of genes based on promoter accessibility, we conducted a GO enrichment analysis, comparing genes with accessible promoters to the full proteins . Remarkaproteins ,35. Therproteins . the incproteins .NvClock promoter , caudal and CDX2 . Interestingly, caudal has also been identified (in gene cluster 3) and previously reported as a synchronizer of locomotor activity in crayfish [NvCry genes have expression peaks that did not overlap with the CT13 sampling point; (NvCry1 expression peak is at CT4-CT11 and NvCry2 expression peak is at CT0-CT4). As cryptochromes are photoreceptors, their expression profile peaks at mid-day (light time) [NVcry1 becoms accessible at sampling point CT29, This could indicate that its proximate chromatin region is more packed or inhibited, and further investigation is needed to validate this conclusion.We found that gene expression rhythmicity corresponded with changes in DNA accessibility within promoters of 139 out of 180 previously-identified rhythmic genes. However, this general rule does not apply to all circadian genes, and there was a subset of genes that showed relatively high and continues accessibility rate throughout the experiment . This stpromoter  is imporredicted . Moreoveredicted  and actsredicted  includinht time) ,15, it iN. vectensis, we could identify enhancers that can serve as potential targets for rhythmic gene regulation , under natural light and at a constant temperature of 18°C. Between 50 and 100 individuals were kept in each container in a recirculating water system. Animals were fed 5 times a week with freshly hatched brine shrimp (Artemia nauplii).Adult N. vectensis were incubated under two different light regimens: LD (12h light:12h dark) or DD (constant darkness), for 45 hours at a constant temperature of 18°C. Biological duplicates were sampled every 8 hours at CT13, CT21, CT29, CT37 and CT45 from both conditions and were processed as described in the “ATAC-seq nuclear isolation and library preparation” section. We did not sequence sample DD CT29 due to technical problems. These time points were chosen based on gene enrichment data from RNA-seq experiments previously published. The ATAC-seq sampling interval was due to the timing requirements of the ATAC-seq protocol (approximately 6–7 hours).Female Nematostella that were incubated in different lighting treatments. From each sample, tissue was suspended in 500 μL PBS-NAC 2%  by pipetting in a 1.5 mL tube [Nuclei were isolated from adult  mL tube . The susN. vectensis were prepared using single-end 50bp reads from a single Illumina HiSeq run. Treatments were run on one lane of Illumina HiSeq2000. On average, ~50 million single-end reads were obtained for each sample.Samples of whole Nematostella vectensis genome using bowtie [Sequenced reads were aligned to the Nemve1 g bowtie . Only ung bowtie  with theg bowtie : findMotg bowtie .N.vectensis [et al., [N.vectensis [To compare chromatin accessibility with circadian patterns in gene expression, we evaluated a set of 180 transcripts that were previously shown to exhibit a diel expression pattern in ectensis . The gen[et al., . Pearsonectensis . Reads wectensis  with defectensis ,47. Geneectensis .S1 FigNematostella vectensis locomotor activity under a 12-hr light: 12-hr dark cycle (LD). (B) Nematostella vectensis locomotor activity under constant dark (DD). Yellow indicates light hours, grey indicates dark hours and pale blue indicates dark during subjective day. (C) 139 out of 180 genes that were found to be rhythmic in a previous RNA-seq experiment, clustered by expression groups. Above the heatmap is the aligned distance moved, as measured in LD. Data from Oren et al., [(A)  et al., .(EPS)Click here for additional data file.S2 FigThe red bars indicate the approximate place of the nearest next TSS .(EPS)Click here for additional data file.S3 FigN.vectensis. (C-D) We determined the overlap of ATAC-seq peaks with H3K4me2 peaks in LD-treated and DD-treated N.vectensis. (E-F) We determined the overlap of ATAC-seq peaks with H3K4me3 peaks in LD-treated and DD-treated N.vectensis. (G) Comparison of H3K27ac peak position to Planula and Gastrula enhancer list from previously published Chip-seq data  We determined the overlap of ATAC-seq peaks with H3K27ac ChIP-seq peaks in LD-treated and DD-treated  et al., ).(EPS)Click here for additional data file.S1 TableP-value < 0.01 for all results.(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 TableNematostella by Schwaiger et al., [Pie charts of genomic annotations showing histone acetylation marks identified from a previous ChIP-seq study, performed on adult female  et al.,  (left co(EPS)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.


CNR1 gene) have been compromised by differences in patient response. Toward addressing the hypothesis that genetic changes within the regulatory regions controlling CNR1 expression contribute to these differences, we characterized the effects of disease‐associated allelic variation within a conserved regulatory sequence (ECR1) in CNR1 intron 2 that had previously been shown to modulate cannabinoid response, alcohol intake, and anxiety‐like behavior. We used primary cell analysis of reporters carrying different allelic variants of the human ECR1 and found that human‐specific C‐allele variants of ECR1 (ECR1(C)) drove higher levels of CNR1prom activity in primary hippocampal cells than did the ancestral T‐allele and demonstrated a differential response to CB1 agonism. We further demonstrate a role for the AP‐1 transcription factor in driving higher ECR1(C) activity and evidence that the ancestral t‐allele variant of ECR1 interacted with higher affinity with the insulator binding factor CTCF. The cell‐specific approaches used in our study represent an important step in gaining a mechanistic understanding of the roles of noncoding polymorphic variation in disease and in the increasingly important field of cannabinoid pharmacogenetics.Cannabinoid receptor‐1 (CB1) represents a potential drug target against conditions that include obesity and substance abuse. However, drug trials targeting CB1 (encoded by the  Cultures were transfected 4 days after plating with luciferase plasmids . Cells were dissociated using a combination of trypsin and papain solution followed by gentle agitation from pooled tissues to reduce variability and cultured in poly‐s Figure  for a fu2.4l‐glutamine (4 mM), and sodium pyruvate (1 mM). Medium was supplemented with 10% (v/v) heat‐inactivated fetal bovine serum (Gibco) and 1% (v/v) Penicillin–Streptomycin . The cells were cotransfected with luciferase reporter plasmids  or an expression vector expressing the c‐fos protein into the SH‐SY5Y neuroblastoma cell line. SH‐SY5Y cells  were cultured in Dulbecco's modified Eagle medium  containing low glucose (5.5 mM), s Figure a or a po2.5All luciferase reporter assays were performed either 24 (SH‐SY5Y cells) or 48 hr (primary cells) after transfection as per the manufacturer's instructions (Promega). Luciferase expression was quantified using Dual Luciferase Reporter assay system and a GloMax 96 microplate luminometer (Promega).6 cells in 10 cm plates), as described above, and incubated for 48 hr. Briefly, following cross linking with formaldehyde hippocampal chromatin was extracted and fragmented by restriction digestion (NcoI/BglII and MluI/AseI) or sonication as previously described  was performed on chromatin derived from primary hippocampal neurons transfected with either the ECR1(C) or ECR1(T) reporter constructs  for ChIP samples: quantitative PCR (qPCR) for ChIP samples was conducted using SYBR green reagents as per manufacturer's instructions (Roche) using the following primers to normalize transfection efficiencies from input DNA  and to assess immunoprecipitation of human ECR1T or ECR1C allelic variants following immunoprecipitation .2.6t test was used to examine the difference between two groups. Where there were more than two groups, statistical significance of data sets was analyzed using a one‐way analysis of variance with Bonferroni post hoc tests. All tests were carried out using GraphPad PRISM version 5.02 .Two‐tailed unpaired parametric Student's 33.1CNR1 promoter  to upregulate the activity of the CNR1 promoter. Bioinformatic analysis (RegSNP) of the human ECR1 locus predicted that the AP‐1 transcription factor (a dimer of the c‐FOS and c‐JUN proteins) would bind to ECR1(C) with high affinity but with reduced affinity to ECR1(T). The RegSNP program also suggested that the ECR1(T) allele would bind the CTCF transcription factor with higher affinity. This is intriguing as CTCF is a known marker for the function of regulatory sequences known as insulators that protect promoters against the effects of enhancer sequences ‐Luc or ECR1(T)‐Luc plasmids Figure a into pr3.4CNR1 in the hippocampus . To verify this observation, we also showed that expression of c‐FOS; one of the proteins that forms the AP‐1 complex, activates the ECR1(C) enhancer but not ECR1(T) in cotransfected cells. This observation was interesting as several studies have demonstrate that stimulation of CB1 induces binding of AP‐1 to DNA and expression of c‐FOS (Marie‐Claire et al., Our observations also touch on the evolution of the C‐allele which is only found in humans. We were able to predict binding of the CTCF transcription factor; a known marker of insulator function, with higher affinity to the T‐allele, a prediction that we also support using ChIP assays. These observations raise an interesting possibility that the ancestral T‐allele of ECR1 could act as a weak enhancer with insulator properties in the hippocampus of most vertebrates. However, the change to the C‐allele in humans, that increased binding affinity of AP‐1 and increased the enhancer like properties of ECR1 in specific tissues, may have proved advantageous to specific populations faced with new and stressful circumstances thus expanding the frequency of the C‐allele in these populations.CNR1 and a mechanism is proposed based on the evidence. Although much remains to be done to conclusively establish a role for these observations in alcohol abuse and drug effects in humans, our unique primary cell‐based ChIP transfection studies and reporter‐expression vector coexpression studies lay the foundation for future studies of the contribution of these genetic and epigenetic factors in the pharmacogenetics of the cannabinoids and the possible effects on human health and disease susceptibility. We believe that the current manuscript provides not only a platform for the further study of the effects of polymorphisms on the pharmacogenomics of the cannabinoids but also an important functional tool for understanding the role of noncoding polymorphisms in health and disease.Taken together, the evidence discussed above suggests a role for allelic variation within ECR1 in modulating the activity of CNR1prom and thus expression of


An array of oncogenic histone point mutations have been identified across a number of different cancer studies. It has been suggested that some of these mutant histones can exert their effects by inhibiting epigenetic writers. Here, we report that the H3.3 G34R (glycine to arginine) substitution mutation, found in paediatric gliomas, causes widespread changes in H3K9me3 and H3K36me3 by interfering with the KDM4 family of K9/K36 demethylases. Expression of a targeted single-copy of H3.3 G34R at endogenous levels induced chromatin alterations that were comparable to a KDM4 A/B/C triple-knockout. We find that H3.3 G34R preferentially binds KDM4 while simultaneously inhibiting its enzymatic activity, demonstrating that histone mutations can act through inhibition of epigenetic erasers. These results suggest that histone point mutations can exert their effects through interactions with a range of epigenetic readers, writers and erasers. Recent studies have identified a number of oncogenic histone point mutations in different cancers. Here the authors provide evidence that H3.3 G34R substitution mutation, which is found in paediatric gliomas, causes changes in H3K9me3 and H3K36me3 by interfering with the KDM4 family of K9/K36 demethylases. The steady state equilibrium of these modifications is dependent on the balanced activity of chromatin modifiers such as methyltransferases 5 and demethylases (e.g. KDM4 A/B/C)7. The continual action of chromatin modifiers is required to maintain appropriate chromatin states to facilitate all genomic processes. Any significant disruption of chromatin pathways leads to destabilisation of the genome by interfering with DNA replication, repair and transcription, and ultimately renders genomic DNA vulnerable to mutagenic events.Eukaryotic chromosomes are comprised of a repeated array of approximately 147 bp of DNA wrapped around a nucleosome. Each nucleosome consists of an octamer of four core histone proteins; two subunits each of H2A, H2B, H3, H4, or an equivalent variant. The DNA/nucleosome complex forms the basic unit of chromatin, a structure which confers genomic stability and protects against DNA damage. In addition to conferring structural integrity through direct interactions with DNA, nucleosomes can also be post-translationally modified (PTM) to facilitate dynamic and nuanced regulation of DNA-related processes. These PTMs include the trimethylation of key lysine residues on the N-terminal tail of histone H3 such as H3K9me3, H3K27me3 and H3K36me3, which are involved in promoter silencing and prevent spurious intragenic transcription8. In addition, some oncogenic mutations can alter chromatin states indirectly through the inhibition of chromatin modifiers. For example, point mutations in a metabolic enzyme, isocitrate dehydrogenase (IDH1/2) produces 2-hydroxyglutarate (2-HG)11, an oncometabolite that competitively inhibits 2-oxoglutarate (2-OG)-dependent dioxygenase histone and DNA demethylases13. The IDH1/2 mutations are frequent events in certain cancers such as lower grade gliomas17 where they also overlap with inactivating mutations in ATRX17, a chromatin remodeller which deposits histone H3.3 at heterochromatin20. H3.3 is a replication-independent histone variant, which replaces canonical histone H3.1/2 outside of S-phase21. Interestingly, point mutations in histone H3.3 are common events in certain cancers, indicating that the ATRX/H3.3 chromatin pathway is a key contributor to the molecular pathogenesis of particular cancers.This loss of genomic integrity is a hallmark of cancers and accordingly, mutations in chromatin-related genes have been identified as oncogenic drivers. Many cancers harbour at least one mutation in a chromatin-related gene including DNA methyltransferases, histone modifiers, chromatin readers and remodellers23, and H3/H3.3 K36M mutations which are common in chondroblastomas24 and head and neck squamous cell carcinomas25. These lysine to methionine mutations have been demonstrated to act in a dominant-negative manner by inhibiting the histone lysine methyltransferases which normally methylate the cognate residues, resulting in genome-wide chromatin alterations27. In addition, a number of substitutions have been detected at the glycine residue at H3/H3.3 position 34 (G34)24, including an H3.3 G34R substitution which is commonly found in paediatric gliomas23. The functional consequences associated with G34 mutations are not well understood. H3.3 G34R has been reported to directly inhibit SETD2-mediated (SET domain containing 2) H3.3 K36me3 on the mutated histone26, though not in a dominant-negative manner which is expected from heterozygous single-copy mutations. Furthermore, oncogenic chromatin mutations generally trigger genome-wide chromatin alterations but the genomic changes associated with H3.3 G34R have not yet been established.The oncohistone mutations which have been discovered to date, are uniformly single-copy substitution mutations predominantly on H3.3, with rare occurrences on H3.1/2. Examples include the H3/H3.3 K27M mutation which is frequently detected in paediatric gliomasH3f3a G34R targeted mutation in mouse embryonic stem (ES) cells to faithfully recapitulate the mutations identified in paediatric gliomas. We find that this mutation triggers a gain in H3K36me3 across the genome, suggesting that H3.3 G34R inhibits the activity of a histone lysine demethylase. We identify the KDM4 A/B/C family of K9/K36 demethylases as likely candidates, and show that the H3K9me3 and H3K36me3 profile in H3.3 G34R mutants closely resemble a KDM4 A/B/C triple-knockout. Furthermore, we demonstrate that H3.3 G34R preferentially binds to KDM4 and inhibits its enzymatic activity. Studies of other oncohistone mutations have described a general mechanism where the mutated residues inhibit the activity of a chromatin remodeller . We describe a similar phenomenon for the H3.3 G34R mutations and identify the KDM4 histone lysine demethylases as the key chromatin modifiers, which are disrupted by this mutant histone. The IDH1/2 mutations are also known to inhibit the KDM4 demethylases, raising the possibility that H3.3 G34R and IDH1/2 mutations may drive oncogenesis through parallel pathways.We created a single-copy H3f3a and H3f3b) which encode identical proteins29. The G34R substitution in paediatric gliomas always occur as heterozygous mutations on H3f3a23, and tumours retain three normal copies of H3.3 in addition to the canonical H3.1/2 histones. Therefore, the oncogenic properties of H3.3 G34R emerge in a context where the mutant histone comprises a small fraction of total cellular H3. As such, modelling the H3.3 G34R mutation with an ectopic overexpression system could result in artefactual phenotypes that mask the underlying oncogenic mechanisms. Instead, we opted to create a targeted mutation on the mouse H3f3a locus to accurately reflect the full H3.3 genotype of paediatric gliomas.In mouse and humans, the H3.3 histone variant is expressed from two distinct genes (H3f3a promoter29. A targeting vector comprised of three cassettes (wild-type (WT) H3f3a, neo and H3f3a G34R) was substituted for exon 2 of H3f3a, creating a conditional allele which expressed a WT H3f3a minigene from the endogenous H3f3a promoter  demethylase) enzymes, KDM4-A, -B and -C32. This family of demethylases are structurally highly similar and all have dual substrate specificity towards both H3K36me3 and H3K9me334. The crystal structures of these enzymes indicate that a small amino acid is required at the −2 position (e.g. glycine at position 34) for full catalytic activity35. We therefore asked if the gains in H3K36me3 could be due to H3.3 G34R interference with KDM4 A/B/C.We therefore reasoned that the observed gains in H3K36me3 could be due to H3.3 G34R inhibition of a K36 demethylase. The demethylation of H3K36me3 is primarily catalysed by three closely related KDM4  of KDM4 binding sites were simultaneously enriched for all three KDM4 A/B/C  demethylation activity, this model predicts H3K9me3 profiles would also be altered in H3.3 G34R mutants. To test this, we performed H3K9me3 ChIP-seq in WT and G34R cells and isolated genic regions which were enriched for H3K9me3 in either cell type. Similar to H3K36me3, we observed moderate gains of H3K9me3 across all of these regions Fig. . We next36 to the H3.3 G34R profiles. We identified regions which gained H3K9me3 (>1.5-fold) in KDM4-tKO cells and observed comparable gains at these sites in the H3.3 G34R mutant ant Fig. . Genomicant Fig. . A similant Fig.  while siant Fig. . Exampleant Fig. , and gaiant Fig. .36. The microarray gene identifiers were cross-referenced against the UCSC database to retrieve mm9 gene coordinates; 944/1005 (94%) of KDM4-tKO downregulated genes and 1099/1107 (99%) of KDM4-tKO upregulated genes were identified. We assessed WT and G34R RNA-seq data and observed clear similarities between the G34R and KDM4-tKO mutants. Genes which were downregulated in the KDM4-tKO mutants tended to be downregulated in G34R cells relative to WT expression and compared against a sample of random genes  which had two peaks of KDM4B binding in WT cells; one centred on the promoter with relatively low levels of H3.3, and a second peak within the gene body with high levels of H3.3 , and an A7R substitution interfered with KDM4A demethylation of H3K9me334. To determine if the G34R substitution inhibited KDM4 demethylation of K36me3, recombinant KDM4-A, -B and -C were combined with either a H3.3 K36me3 or an H3.3 G34R K36me3 peptide 25, giant cell bone tumours (H3.3 G34W/L)24 and paediatric gliomas 23. The lysine to methionine substitutions are thought to bind and inhibit lysine methyltransferases to trigger genome-wide changes in chromatin states27. The mechanism which underlies the G34 substitutions have been less clear. Early studies indicated that an H3.3 G34R substitution prevented SETD2-mediated tri-methylation of the neighbouring K36 residue but this effect is limited to the mutated histone26, unlike the relationship between K27M/PRC2 and K36M/SETD2 which are able to act in trans across the genome27.Histone point mutations are a common feature of certain cancers including chondroblastomas (H3.3 K36M)22. The double mutation in H3.3 and its chaperone, means that it has not been possible to accurately determine the chromatin alterations which are directly attributable to H3.3 G34R from analyses of patient samples. We addressed this by recreating the H3f3a G34R single-copy mutation in mouse ES cells on an otherwise WT background. The initial analysis of this cell line showed that there were localised increases in H3K36me3 at specific regions across the genome. These observations do not directly contradict the previous reports that H3.3 G34R prevents SETD2-mediated trimethylation of H3.3 G34R K36, however our findings strongly suggest the H3.3 G34R is able to alter the genome in trans through a separate chromatin pathway.We sought to characterise and identify the chromatin modifications and pathways which are most affected by the H3.3 G34R mutation. In paediatric gliomas, the H3.3 G34R substitution is always found in conjunction with mutations in an H3.3 chaperone, ATRX7, which reportedly prefer a small amino acid (e.g. glycine) at position 34 of H3/H3.335. As these demethylases have dual activity towards both H3K36me3 and H3K9me3, we profiled H3K9me3 and found this modification was also altered in the G34R mutant. Furthermore, the chromatin and transcriptional profiles of the G34R mutant were highly similar to those observed in a KDM4-tKO. Therefore, expression of a single copy of H3.3 G34R at endogenous levels is sufficient to alter two independent histone modifications in a manner similar to a KDM4-tKO, strongly suggesting that H3.3 G34R is interfering with the KDM4 demethylases.We reasoned that these chromatin changes may be due to inhibition of the H3K36me3 demethylases, KDM4 A/B/C38, suggesting that the G34V mutations may also inhibit KDM4. We do not exclude the possibility that H3.3 G34R may affect additional chromatin pathways, but we nonetheless identify KDM4 as the major pathway which is disrupted by this mutation.Consistent with this, we were able to demonstrate that KDM4 shows binding preference for H3.3 G34R compared to WT H3.3, and this preference is sufficient to alter the distribution profile of KDM4B in an H3.3-dependent manner. It is important to note that a substantial fraction of H3.3 is incorporated into genomic repeats and our current assays were not able to fully assess the H3.3 G34R-mediated disruptions of KDM4 within these repeats. Further studies into H3.3 G34R and the other H3.3-specific oncohistones  should attempt to address this critical issue. Nonetheless, we were able to demonstrate that H3.3 G34R preferentially binds to KDM4 and also inhibits demethylase activity towards H3.3 G34R K36me3. The simultaneous binding and inhibition of KDM4 by H3.3 G34R, is reminiscent of the interactions between K27M/PRC2 or K36M/SETD2, and accounts for the similarities between the G34R and KDM4-tKO mutants. These findings are also consistent with observations of increased H3K36me3 in H3.3 G34V brain tumours7, including neomorphic mutations in IDH1/2 which produce an oncometabolite11 that inhibits 2-OG-dependent dioxygenase demethylases, including the KDM4 family of demethylases13. The IDH1/2 mutations are relatively rare in paediatric gliomas but occur at high frequency in adult lower grade gliomas. Mutations in IDH1/2 are a defining feature of diffuse astrocytomas and are almost always found in conjunction with inactivating mutations in ATRX and TP5317. This mutation signature is identical to H3.3 G34R mutated paediatric gliomas, which are similarly mutated for ATRX and TP5322. This raises the possibility that mutations in H3.3 G34R and IDH1/2 act through parallel pathways, and inhibition of KDM4 may be a common feature in ATRX mutated cancers.Many studies have indicated that defects in KDM4 contribute to oncogenesisIt is now clear that mutations in chromatin pathways are a common feature in many cancers, and the majority of mutations trigger broad changes in chromatin states. We have demonstrated that H3.3 G34R also fits this general model and outline a mechanism which enables this point mutation to alter chromatin profiles across the genome. These findings broaden the framework for understanding how histone mutations may exert their effects and suggest that, much like histone modifications, these mutations act through interactions with epigenetic readers, writers and erasers.3 units/ml leukaemia inhibitory factor (Merck) and 0.1 mM β-mercaptoethanol. ‘WT’ cells indicate unmodified ES cells . ‘G34R’ cells refer to cells with a single H3f3a G34R mutation .Mouse ES cells were cultured in Dulbecco Modified Eagle Medium supplemented with 15% heat-inactivated foetal calf serum, 1029. The targeting vector was identical with the previous study, except that the yellow fluorescent protein coding sequence (CDS)29 was replaced with H3.3 G34R CDS. On exposure of targeted cells to Cre-recombinase, the WT H3.3 minigene CDS was excised, and the mutant G34R minigene CDS was brought under control of the endogenous H3f3a promoter. Mutant clones were selected by restoration of G418 sensitivity (as the neo cassette is excised together with the WT minigene), and confirmed by Southern blot. The presence of mutant H3.3 G34R RNA transcript was validated by reverse transcription-PCR amplification of mRNA followed by DNA sequencing analysis of the PCR products using transgene-specific sequencing primers . DNA was precipitated with ammonium acetate and isopropanol, and washed with 70% ethanol. DNA was resuspended in 50 μl of TE buffer. Genomic DNA was digested with a 5-fold excess of restriction enzyme and 5 μg was loaded. Blots were performed with Hybond XL membranes according to manufacturer’s protocols , except that the pre- and hybridisation solution was 5× SSPE, 5× Denhardt’s solution, 1% SDS. All gels were depurinated and washed twice with 0.2× SSC, 0.5% SDS. PCR primers for probe synthesis are shown in Supplementary Table Southern Blots were performed as previously describedAntibodies used were directed against H3 (Abcam ab1791), H4 (Merck Millipore), H3.3 (Merck Millipore 09-838), H3K36me3 (Abcam ab9050), H3K9me3 (Abcam ab8898), H4K20me3 (Abcam ab9053), Flag (Sigma Aldrich), HA (Roche) and Actin (Santa Cruz Biotechnology).3), decrosslinked overnight at 65 °C, incubated with Proteinase K, phenol chloroform extracted and ethanol precipitated.Five million cells were fixed in growth media with 0.4% formaldehyde for 10 min and quenched with 125 mM glycine. Chromatin was released by sequential lysis with cells lysis buffer  and nuclear lysis buffer . Chromatin was sheared to ≈300 bp with 12 rounds of sonication  on a Bioruptor (Diagenode) and incubated overnight at 4 °C with either 5 μg H3K9me3 antibody (Abcam ab8898) or 10 μg H3K36me3 antibody (Abcam ab9050). Samples were immunoprecipitated with Protein A Agarose beads  and washed sequentially with low salt , high salt  and LiCl buffers . Chromatin was eluted with elution buffer . After 24 h, transfected cells were cross-linked first with 2 mM EGS (ethylene glycol bis(succinimidyl succinate)) (Pierce) for 45 min then subsequently with 1% paraformaldehyde for 15 min. Excess formaldehyde was quenched with glycine at a final concentration of 0.25 M. Cell were then washed, lysed and subjected to sonication as described above. Resulted chromatin was diluted in dilution buffer and immunoprecipitated with 25 μl anti-Flag-bound magnetic beads (Sigma Aldrich) at 4 °C overnight. The immunoprecipitated chromatin was washed, eluted, decross-linked and purified as described above.Sample concentrations were determined by Qubit (ThermoFisher Scientific) and 20 ng of DNA was used as starting material. ChIP libraries were prepared with Nugen Ovation Ultralow System V2  with 10 cycles of amplification. Libraries quality was assessed by Qubit, Bioanalyzer (Agilent) and qPCR, and a single equimolar pool was made based on size-adjusted qPCR quantitation. Following denaturation, 12 pM of library pools were used for cBot hybridisation and cluster generation , and samples were sequenced on an Illumina HiSeq 1500 rapid mode .P-values were calculated using Student’s T-test. See Supplementary Table ChIP samples were resuspended in 1 ml. 5 μl of ChIP material was combined with 0.5 μM of primers and FastStart DNA Master SYBR Green  in a 15 μl reaction and amplified in a LightCycler (Roche). Samples were normalised for input and compared against a negative control point (β-actin promoter). Results and error bars are the mean average and standard deviations of three independent experiments. 40 with default settings except (m −5) to allow restricted multi-mapping to short repeats. Data were imported into Seqmonk (v 0.32.1) with PCR duplicates excluded. Reads were counted across 200 bp genomic bins and samples were normalised for total reads (per million reads). WT and H3.3 G34R datasets were normalised against each other using the ‘match distribution quantitation’ tool on Seqmonk.ChIP-seq of H3K36me3 and H3K9me3 from WT and H3.3 G34R mutants were aligned to the mouse genome (mm9) with Bowtie (v1.0.0)We selected bins which overlapped UCSC annotated genes and only analysed sites which were enriched for H3K36me3/H3K9me3 (>95th percentile) in either WT or G34R cells. All windows within 100 bp were merged using MergeBed (−d 100 −o mean) function in BedTools (v 2.20.1). Merged regions >500 bp and regions with extreme read counts (top 0.01%), typically associated with poorly annotated genomic regions, were excluded from further analysis. A total of 31,593 WT/G34R H3K36me3-enriched sites and 32,502 WT/G34R H3K9me3-enriched sites were used for downstream analyses.42 regions were excluded from analysis.H3K36me3- or H3K9me3-enriched regions which exceeded the 90th percentile for H3.3 binding or the 75th percentile for KDM4 A/B/C binding within each dataset were determined to be ‘enriched’. Regions which fell below the 25th percentile were defined as ‘depleted’. Comparison plots show normalised H3K9me3 or H3K36me3 ChIP-seq reads from WT and G34R cells under regions deemed to be enriched or depleted. Boxes represent the 25th, median and 75th percentiles; whiskers represent the 10th and 90th percentiles. Venn diagrams show overlaps between regions deemed to be enriched for binding, with depletion was used as exclusion criteria. Cumulative read profiles were generated in DeepTools using computeMatrix (v 2.5.0.0) and plotProfile (v 2.5.0.0) on Galaxy. Regions were fitted to 500 bp with an additional 1 kb up and downstream; blacklistedSee Supplementary Table 36 (Supplementary Table ChIP-seq of H3K9me3 and H3K36me3 from WT-KDM4 and KDM4-tKO cells were obtained from ref. Regions where H3K9me3 or H3K36me3 was 1.5-fold higher in KDM4-tKO relative to WT-KDM4 were deemed to have gained these modifications in KDM4-tKO cells. Regions where KDM4-tKO/WT-KDM4 ratios were between 0.8 and 1 were deemed to be ‘unchanged’. Heatmaps were created using DeepTools computeMatrix (v 2.5.0.0) and plotHeatmap (v 2.5.0.0) in Galaxy. Regions where H3K9me3 and H3K36me3 were gained or unchanged in KDM4-tKO cells were scaled to fit in 500 bp with an additional 1 kb up and downstream; WT-KDM/KDM4-tKO and WT/G34R H3K9me3 and H3K36me3 ChIP-seq were used as score files.RNA was extracted from 5 million cells with TRI Reagent  according to manufacturer’s instructions. Samples quality was assessed using a Bioanalyzer and quantified by Qubit. All samples were processed starting with 500 ng of total RNA according to the Illumina Stranded Total RNA protocol  except that 12 cycles of amplification was used to minimise amplification artefacts. Libraries were quantitated by Qubit and qPCR, and an equimolar pool were made based upon qPCR results. Following denaturation, 200 pM of the library pool was clustered in one lane of a HiSeq 3000  and 50 bp SR sequencing was carried out .43 using default parameters. Primary alignments were imported into Seqmonk as single-ended RNA-seq. The RNA-seq quantitation pipeline was used to estimate RPKM read counts with the following parameters: mRNA as transcript feature, strand-specific, merge transcript isoforms, apply transcript length correction and exclude probes with no counts. A total of 26,127 mRNA probes were quantitated.Stranded 50 bp single-end RNA-seq reads were aligned to mouse (mm9) with STAR (v.2.4.2a)36. The UCSC Table Browser was used to extract mm9 coordinates based on gene names. A total of 944 (of 1005) downregulated and 1107 (of 1121) upregulated genes had mapped coordinates on the mm9 genome. Genes which were up/downregulated in KDM4-tKO cells were overlapped with RNA quantitations for WT/G34R cells. Genes which failed to overlap were excluded from further analyses, yielding a total of 933 KDM4-tKO downregulated genes and 1099 upregulated genes with matched WT and G34R RNA-seq data. log2 (G34R/WT) RPKM values were calculated for KDM4-tKO up and downregulated genes and sorted by ascending values. Results were plotted in R Studio using the gplots package44.A list of genes which were either downregulated or upregulated in KDM4-tKO cells as determined by microarray, was obtained from ref. A full list of genes, which were up- or down-regulated in G34R cells relative to WT, are provided as Supplementary Data H3f3a (NM_008210.5) and H3f3b (NM_008211.3), using Bowtie (v1.0.0) (2) with default settings. Reads which spanned codon 34 were extracted and duplicates were removed. A total of 62 and 68 informative reads were identified in WT cells and G34R cells, respectively. Pie charts show the proportion of reads which were identified as originating from H3.3 G34R, WT H3f3a or WT H3f3b.RNA-seq reads from WT and G34R cells were aligned to a custom library, consisting of the CDS for H3f3a G34R transgene, Ddx28) which was unchanged between WT and G34R cells. Results and error bars are the mean average and standard deviations of three independent experiments. P-values were calculated using Student’s T-test. Primers are shown in Supplementary Table RNA extracted from WT and G34R cells were quantitated by Nanodrop and 1 μg of total RNA was reverse transcribed  according to manufacturer’s instructions. 20 ng of cDNA was combined with 0.5 μM of primers and FastStart DNA Master SYBR Green  in a 15 μl reaction and amplified in a LightCycler (Roche). Expression was calculated relative to a gene . After 24 h, transfected cells were then lysed in cold RIPA buffer  and sonicated. After centrifugation, the supernatant was collected and subjected to immunoprecipitation overnight using streptavidin magnetic beads and anti-FLAG antibody . Beads were washed with RIPA buffer three times. Immunoprecipitants were eluted by addition of 2× SDS PAGE sample buffer containing beta mercaptoethanol and boiled at 95 °C for 5 min. The eluates were subjected to SDS/PAGE gel electrophoresis and Western blot with an anti-HA antibody . Bands were quantitated with ImageJ software and normalised to input. Results and error bars are the mean average and standard deviations of three independent experiments. 4)2Fe(SO4)2·6(H2O), 1 mM TCEP for 2 h at room temperature. Samples were co-spotted onto an MTP anchorChip 800/384 TF MALDI target plate with Matrix solution of 10 mg/ml a-cyano-4-hydroxycinnamic acid  in 50% acetonitrile 0.1% TFA. Samples were analysed on a Bruker Daltonics  ULTRAFLEX MALDI TOF/TOF in reflector mode with an m/z range of 1200–3500 Da, using Smartbeam parameter set 4, and detector gain 2.5× for 1000 shots. The data was processed using Flexanalysis . The spectra were externally calibrated against a peptide mix including angiotensin (1296 m/z), Glu-Fibrinopeptide B (1570 m/z), ACTH17 (2093 m/z), ACTH39 (2465 m/z) and ACTH38 (3657 m/z) which were spotted on adjacent calibration wells.250 ng of either H3.3 WT K36me3 or H3.3 G34R K36me3 peptides  were incubated with 50 nM KDM4 A/B/C recombinant protein in reaction buffer (50 μl) containing 50 mM Tris–HCl pH 7.5, 0.02% Triton X-100, 200 μM 2-OG, 200 μM ascorbate, 50 μM (NH40 ng of biotinylated H3.3 K36me3 and H3.3 G34R K36me3 peptides (GLC Biochem) were bound to 20 μl of streptavidin magnetic beads (Thermo Scientific) in 200 μl of PBS for 5 h at 4 °C. Beads were washed twice with PBS then blocked with 1% BSA in PBS. Excess BSA was removed with a final PBS wash. 200 ng of recombinant Flag-tagged KDM4A or KDM4C (Active Motif) were incubated with peptide-bound beads in PBS overnight at 4 °C. A fraction of the input protein and the supernatant after binding to beads (flowthrough) was retained for gel analysis. Beads were washed with PBS. Input, flowthrough and bead fractions were run on a 10% SDS-PAGE gel and analysed by western blotting with an anti-FLAG antibody .Datasets used in this study are listed in Supplementary Table Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1


Vast repertoires of unique antigen receptors are created in developing lymphocytes. The antigen receptor loci contain many variable (V), diversity (D), and joining (J) gene segments that are arrayed across very large genomic expanses and are joined to form variable-region exons. This process creates the potential for an organism to respond to large numbers of different pathogens. Here, we consider the underlying molecular mechanisms that favor some V genes for recombination prior to selection of the final antigen receptor repertoire. We discuss chromatin structures that form in antigen receptor loci to permit spatial proximity among the V, D, and J gene segments and how these relate to the generation of antigen receptor diversity. H-to-JH recombination occurs first followed by VH-to-DHJH rearrangement. This process depends on the lymphocyte-specific V(D)J recombinase, RAG1/2, which recognizes recombination signal sequences (RSSs) that flank all V, D, and J gene segments3. During V(D)J recombination, two RSSs adjacent to V, D, or J gene segments partner such that cleavage and rejoining occur. RAG1 contains endonuclease activity and targets the RSS, and RAG2 is recruited to the epigenetically modified histone 3 when it is trimethylated on lysine 43. Within each antigen receptor locus, the RAG recombinase concentrates in the recombination center (RC) that is focused to the J segment–containing domain. Double-strand DNA breaks are generated at RSSs by RAG1/2, and the V, D, and J exons are joined together through non-homologous end joining4.In vertebrates, the adaptive immune response is capable of recognizing pathogens using antigen-specific receptors expressed on B and T lymphocytes. The B-cell receptor (BCR) is composed of two identical immunoglobulin (Ig) heavy chains (IgH) and two identical light chains (Igκ or Igλ). There are two lineages of T cells that are distinguished by the type of T-cell receptor (TCR) expressed. TCRαβ is encoded by the Tcra and Tcrb loci, whereas TCRγδ is encoded by the Tcrg and Tcrd loci. Antigen receptors are composed of variable (V) and constant (C) regions. The organization of the Igh and Igκ loci are schematically depicted . Igh var6. The composition and complexity of antigen receptor repertoires depend on the number of V, D, and J gene segments and the degree to which those segments are available for rearrangement. However, V gene usage in the pre-selected Igh repertoire is only quasi-random since it has been shown that V genes rearrange at very different intrinsic frequencies14. No one factor, or combination of factors, could fully account for unequal V gene usage in studies considering V germline transcript levels, transcription factor (TF) binding, RSS quality, and the distribution of a variety of epigenetic marks15. Hence, the mechanisms underlying V gene rearrangement frequencies remain to be determined.Antigen receptor gene rearrangement is tightly regulated during lymphocyte development; in turn, lymphocyte development is strictly dependent on V(D)J recombination6. Developmental stage-specific contraction of Ig and TCR loci promotes proximity of J-distal V genes with (D-)J segments and generally is thought to facilitate recombination but this has not been formally proven19. Two currently unresolved questions in the formation of the antigen receptor repertoires are (1) what is the molecular basis for locus contraction that is hypothesized to support V->DJ recombination over exceptionally long genomic distances and (2) what underlies the unequal rearrangement potential of individual V genes? Here, we focus on the murine Igh and Igκ loci to address these questions. The molecular principles resulting from these studies may be generally applicable to all antigen receptor loci.Antigen receptor loci are quite large spanning 0.67 Mb- 3.0 Mb and containing up to 100 functional V genes. Chromatin conformational changes in antigen receptor loci are important determinants for long-distance V(D)J recombination events23. Additionally, the Igh locus undergoes large-scale locus contraction during development that is detected by using three-dimensional (3D) DNA fluorescencein situ hybridization (FISH) methods25.Developmental activation of the Igh locus is a stepwise process that features acquisition of epigenetic modifications, DNase I hypersensitive sites, and the onset of sense and anti-sense transcription24. First, the Igh locus is located at the nuclear periphery in non-B cells and relocates to the nuclear center at the pro-B cell stage24 through a process that requires active dislocation from the nuclear lamina26. Second, the Igh locus is in an extended conformation in non-B cells and lymphoid progenitors, whereas both Igh alleles are contracted in pro-B cells, a developmental stage coincident with V(D)J recombination25. These pioneering studies have led to the recognition that all of the large antigen receptor loci undergo developmentally regulated conformational changes before rearrangement at that locus30. The contracted Igh locus in pro-B cells undergoes decontraction at the pre-B cell stage of development to prevent a second round of VH-DHJH rearrangement on the second Igh allele, presumably aiding allelic exclusion28. Degrees of locus compaction have been inferred from the relationship of inter-probe nuclear distances derived from 3D DNA FISH versus genomic distances and these measurements have limited resolution (100–1000 nm). Consequently, it has been difficult to identify DNA elements that mediate locus contraction.The early observations by Kosak revealed two fundamental findings regarding the disposition of the Igh locus in the nucleus32. Loss of Igh locus compaction is correlated with preferential usage of the most DH-proximal VH genes33, indicating that spatial access to the more distally positioned VH gene segments has been lost. Although depletion of any of these TFs reduces distal VH rearrangement, chromatin accessibility remains unchanged33. Low-level transcription over VH genes and intergenic regions occurs as the locus is preparing to undergo rearrangement37. The highest level of non-coding RNA (ncRNA) in the Igh locus is found at elements called Pax5-activated intergenic repeats (PAIRs) and these ncRNAs are dependent upon the presence of Pax5 and YY138. Although the function of TFs in locus contraction remains speculative, PAIR elements have been suggested to induce long-range chromatin looping by relocating to transcription factories where they associate with the 3′ proximal Eμ-JH-DH domain37. However, the molecular mechanism that mediates locus contraction remains unclear.Igh locus contraction depends on the TFs Pax5, Ikaros, and YY147. For example, insulators and enhancers often engage in physical interactions with their target promoters51, indicating that regulatory elements can control distant gene expression through direct long-range molecular contact. However, not all long-range chromatin interactions are directed toward regulating gene expression. For example, intra-chromosomal interactions are required to regulate V(D)J recombination and Ig class-switch recombination (CSR)52. In CSR, the constant (CH)-region exons encoding IgM are substituted with a downstream CH gene such that IgM is no longer produced and instead IgG, IgE, or IgA is made in conjunction with the original recombined variable-region exons. CSR is dependent on 3D chromatin architecture mediated by long-range intra-chromosomal interactions between distantly located transcriptional elements56. During V(D)J recombination, antigen receptor genes undergo ordered rearrangement with DH-to-JH joining preceding VH-to-DHJH recombination4. To produce a fully representative Ig repertoire, it is essential that the distal VH genes achieve spatial proximity with the RC and DHJH domain. Murre and colleagues have shown that Igh locus topology is best described as a series of three large chromatin loops joined by linkers in pre-pro-B cells but that these loops have intermingled and provide equal access of the DH-distal and -proximal VH gene segments with rearranged 3′ DHJH in pro-B cells57. The time interval for DHJH to gain proximity with a VH gene segment is on the order of minutes, and spatial confinement of topological domains largely regulates first-passage times for chromatin interactionsin vivo58. Although it is clear that Igh locus conformation is structured, the DNA elements that anchor chromatin looping in support of V(D)J recombination remain largely undefined.Eukaryotic chromosomes are organized into higher-order spatial configurations of multiple-length scales as determined by using high-resolution chromosome conformation capture (3C)-based approaches and microscopy-based methods, including 3D DNA FISH and live cell imaging60. The organization of interphase chromatin is largely conserved between cell types, especially with regard to TAD boundaries61. Strikingly, the Igh locus is contained within a 2.9-Mb TAD in pro-B cells1.Topologically associating domains (TADs) are megabase sized and represent regions of high-frequency self-interacting chromatin contacts as defined in 3C-based studiesH-proximal and DH-distal VH gene families, respectively—and flank a less structured sub-TAD B that includes the intermediate VH gene segments1  (an insulator which will be discussed in detail in sections below), and the proximal VH genes J recombination.The structure of sub-TAD A is worthy of additional consideration as it contains the Eμ and 3′Eα enhancers, the RC located in the JH genes . Sub-TADI to Eμ . In pro-I to Eμ . The higH-proximal VH genes are regulated differently from the rest of the VH genes. Although distal VH gene recombination is reduced in Pax5-, YY1-, and Ezh2-deficient pro-B cells, DH-proximal VH genes recombine normally33. Thus, the localization of the DH-proximal VH genes within the same conformational sub-TAD as the RC/DH/JH region distinguishes them from distal VH genes that lie within sub-TADs B and C.Several earlier observations have shown that DHJ558 gene family. Site III within sub-TAD C fails to associate with sites I and II in Pax5-deficient pro-B cells, thus providing a possible explanation for reduced VHJ558 rearrangements in Pax5-deficient pro-B cells1. Notably, 14 PAIR elements that were proposed to mediate locus compaction via Pax5 are all situated within sub-TAD C, and PAIR motifs 10 and 11 overlap with site III38. PAIR elements are bound by the TFs Pax5, E2A, and CTCF (CCCTC-binding factor) in pro-B cells38. It is not known whether transcriptional activity at PAIR elements regulates chromatin looping. Our studies provide a potential molecular definition of locus contraction by identifying loop anchor sites that are key mediators of this process.Pax5 organizes sub-TAD C that spans the distal V63. CTCF is a ubiquitously expressed zinc-finger protein that binds DNA, functions as an insulator in vertebrates69, and plays a key role in chromatin looping71. There is an observed inward or convergent orientation of CBEs flanking TADs73. Insulators were originally defined as genomic elements that act as a barrier to position effects caused by the spreading of chromatin marks and they block enhancer activity75. Although loci situated within TADs are relatively insulated from loci outside the domain, these same elements readily interact with other loci within the same domain. CRISPR/Cas9-mediated rearrangements of TAD boundaries and regulatory elements facilitate or prevent looping interactions with distal regulatory elements78. Acute depletion of CTCF leads to loss of loop domains and impaired regulation of nearby genes through loss of enhancer insulation79.TAD boundaries are frequently enriched for CTCF binding and CTCF-binding elements (CBEs)in situ Hi-C studies demonstrated that mammalian genomes are partitioned into contact domains45. Contact domains with end points that anchor a loop are referred to as loop domains70. TADs are most frequently loop domains but not all loop domains are TADs. In the context of V(D)J recombination, RAG recombinase activity was shown to be confined to loop domains that are defined by convergent CTCF-bound elements. RAG primarily initiates double-stranded breaks (DSBs) at RSSs within the antigen receptor loci. However, RAG can also initiate low-frequency DSBs at off-target sites that have sequence similarity to RSSs and cause chromosomal rearrangements and translocations82. Notably, when RAG was experimentally directed to chromosomal domains outside of antigen receptor loci, off-target DSBs were confined within loop domains and deletion of convergent CBEs extended the range of RAG activity83.High-resolution85. The cohesin complex is thought to form a ring around two CTCF proteins bound to DNA86. Different combinations of architectural proteins may mediate context-specific genomic organization87. Promoter–enhancer interactions are disrupted in embryonic stem cells88 and in thymocytes89 when cohesin is depleted. There is a rich CTCF-cohesin landscape in the Igh locus. One hundred thirty-two sites are bound by CTCF and cohesin and the majority of these are located at a distance of 1 to 32 kb from VH gene segments in the Igh locus91. Strikingly, all of the rearranging DH-proximal VH genes are closely paired with CBEs that are located within 68 base pairs (bp) of the RSS  and silencer in the intervening sequence (Sis), are located between the V and J gene segments, and many CBEs are found throughout the Vκ domain  have more complex patterns with the bound CTCF sites in the V gene portion of each locus found in both orientations86, with convergent CBEs in a large subset of cases101. It has been proposed that TADs can be formed by the loop extrusion activity of cohesin J recombination99. The Igh sub-TAD A contains several important looping contacts, including Eμ-IGCR looping interactions in pro-B cells J genes, and both are involved in mediating chromatin looping. Cer also functions as a transcriptional insulator108.Interestingly, the Igκ locus contains the Cer/Sis CBEs in the V-J intervening region . DeletioH5-1 gene segment would be highly used in VH-to-DHJH rearrangements since it is most proximal to the RC/DHJH domain (H5-1 is not used in V(D)J recombination. In contrast, VH81X (VH5-2), the next VH gene segment along the genome, is the most frequently used in V(D)J recombination. The question of why VH81X and not VH5-1 is used is long-standing. Two groups have explored the relationship between CTCF-mediated chromatin looping and proximal VH gene usage during V(D)J recombination107.To begin, one might expect that the V domain . HoweverH-proximal VH genes are found within 68 bp downstream of the RSSs90. Mutagenesis analyses have revealed that proximal VH CBEs dramatically influence the frequency of V(D)J rearrangement of that VH gene107. Mutation of the CBE associated with VH81X (VH5-2) J recombination.CBEs adjacent to the functional DVH5-2)  greatly ene106 . Thus, a91. Although it is clear that TAD boundaries are usually formed between convergent CBEs72, relatively little is known regarding the CBE orientation dependence in anchoring chromosome loops within the V domains of Ig loci. All of the bound CBEs in the VH domain are oriented toward the 3′ regulatory region (3′RR) and a single CBE within IGCR1 J rearrangement. However, when the VH81X CBE was inverted, usage of VH81X in V(D)J rearrangement was only modestly decreased106, indicating that the orientation specificity inside the VH sub-TADs is not strictly required.The antigen receptor loci have a much higher density of CTCF sites than the genome overall, making CTCF/cohesin a candidate for forming multiple long-range loops within these locin IGCR1 . If CTCFH gene CBE’s quality determines looping efficiency with IGCR1 and determines that VH gene’s recombination efficiency. It is noteworthy that most VH and all Vκ genes do not have any CTCF sites in close proximity, in contrast with the location of CBEs for the proximal VH genes91. Thus, for the majority of V genes, CBE-mediated looping with IGCR1 may have a less straightforward impact on VH gene rearrangement frequency.Together, these studies demonstrate that the proximal V2. We identified a novel enhancer element, E88, which is located close to the boundary separating sub-TADs C and D and which becomes active at the pro-B cell stage prior to V-J rearrangement J recombination. These studies revealed that the conformational organization of the Igh, Igκ, and TCRα/δ loci has significant implications for locus contraction and likely influences skewed V gene usage that together affects the composition of the pre-selected repertoires. Going forward, studies focused on the relationship of CTCF- and promoter-enhancer-mediated chromatin looping with locus contraction are likely to provide new insights. Studies designed to clarify the relationship of CTCF-dependent looping and Din situ hybridization; Ig, immunoglobulin; IGCR1, intergenic control region 1; IgH, immunoglobulin heavy chain; J, joining; ncRNA, non-coding RNA; PAIR, Pax5-activated intergenic repeat; RC, recombination center; RSS, recombination signal sequence; Sis, silencer in the intervening sequence; TAD, topologically associating domain; TCR, T-cell receptor; TF, transcription factor; V, variable3C, chromosome conformation capture; 3D, three-dimensional; bp, base pairs; C, constant; CBE, CTCF-binding element; Cer, contracting element for recombination; CSR, class-switch recombination; CTCF, CCCTC-binding factor; D, diversity; DSB, double-stranded break; FISH, fluorescence


PTEN causes cancer predisposition. Numerous studies have demonstrated that PTEN regulates the expression of hundreds of genes, however, no mechanism is known so far. PTEN is a dual specificity phosphatase, using both lipid and protein as substrates. In the present study, we demonstrate that PTEN interacts with the RNA Pol II and that PTEN expression is inversely correlated with global phosphorylation of Pol II CTD. Furthermore, PTEN dephosphorylates Pol II CTD in vitro with a significant specificity for Ser5p. Interestingly, ChIP-seq data analysis revealed that PTEN globally binds to promoter proximal regions, and PTEN loss increases genome-wide Pol II Ser5p occupancy, suggest that PTEN is a Pol II CTD phosphatase. Our observations demonstrate an unexplored function of PTEN with the potential of global transcriptional regulation, adding a new dimension to somatic carcinogenesis and germline cancer predisposition.Gene transcription is a highly complex and strictly regulated process. RNA polymerase II (Pol II) C-terminal domain (CTD) undergoes massive cycles of phosphorylation and dephosphorylation during the process of gene transcription. These post-translational modifications of CTD provide an interactive platform for various factors required for transcription initiation, elongation, termination, and co-transcriptional RNA processing. Pol II CTD kinases and phosphatases are key regulators and any deviation may cause genome-wide transcriptional dysregulation leading to various pathological conditions including cancer. PTEN, a well known tumor suppressor, is one of the most commonly somatically altered in diverse malignancies. When mutated in the germline,  PTEN (phosphatase and tensin homolog) alterations are one of the most commonly observed in diverse malignancies. Germline PTEN mutations cause Cowden and related syndromes, conferring a high risk of breast, thyroid and other cancers. PTEN, a well-studied tumor suppressor, regulates cell proliferation, growth and survival by antagonizing the PI3K-AKT-mTOR pathway [Somatic  pathway , 2. PTEN pathway , 5. PTEN pathway –8 and is pathway –13. Nume pathway –16.Gene transcription and its regulation are one of the most intricate biological process involving a wide variety of factors interacting with RNA Polymerase II (Pol II) complex. Pol II is comprised of 12 highly conserved subunits. RPB1, the largest subunit of RNA Pol II complex, contains a C-terminal domain (CTD) comprising 52 tandemly repeated YSPTSPS heptad sequences. The CTD undergoes substantial phosphorylation and dephosphorylation cycles during gene transcription. The dynamic phosphorylation of serine residues at positions 2, 5, and 7 in CTD of RPB1 provide an interactive platform for various factors that regulate transcription initiation, elongation, termination as well as RNA processing . A serieVery recently, we reported that PTEN helps establish genome-wide Pol II pausing and re-distributes Pol II occupancy across the genome. Furthermore, it may impact Pol II pause duration, release, and elongation rate to enable precise gene regulation . AnotherPTEN gene in MCF7 (PTEN-wildtype) breast cancer cells using shRNA  breast cancer cells . We founng shRNA . Interesectively . These oin vitro pull-down assay to re-confirm our findings. GST-tagged Pol II CTD peptide was phosphorylated using CDK9/CyclinT1 in a kinase reaction that resulted in Ser2, Ser5, and Ser7 phosphorylation of GST-CTD  and Pol II Ser5p ChIP-seq  datasets from HeLa cells (GEO accession number GSE120478). We used our gene list  curated from GRCh37/hg19 gene list  to generate heatmap and metagene plots. Our analysis shows PTEN occupancy at promoter proximal regions in thousands of genes as shown by metagene and heatmap plots –4C. Firsposition .PTEN overexpression and knock-down or knock-out resulting in thousands of differentially expressed genes have been known without much mechanism resolution. Until very recently, we and others have shown its role in maintaining Pol II pausing, a highly conserved phenomenon finetuning the transcriptional process , and PTEhttps://thebiogrid.org) show that PTEN could have several hundred interacting partners, and its interaction with various nuclear factors has already been reported [Biological General Repository for Interaction Datasets . As Pol II complex elongates after pause release, Pol II Ser5 phosphorylation is gradually removed. PTEN dephosphorylating Pol II CTD specifically at Ser5p  and the In conclusion, our data indicate that PTEN is a Pol II Ser5 phosphatase that directly binds with Pol II complex. Corroboratively, ChIP-seq analysis further provides evidence that PTEN dephosphorylates Pol II Ser5p at promoter proximal regions and has the ability to modulate global gene regulation. However, more work is needed to further build on this founding study specifically to understand how PTEN co-operates with other factors at promoter proximal regions to decide the fate of the Pol II complex, whether to terminate or to undergo productive elongation. The further understanding of PTEN-Pol II interactions will add a new dimension to understanding somatic and heritable PTEN-related carcinogenesis.2. To prepare PTEN expressing stable line, BT-549 (PTEN null) cells were transfected with pTet-Off  using Fugene® 6  according to the manufacturer’s protocols. Multiple stable clones were isolated using neomycin selection and tested with pTRE2Hyg-Luc according to manufacturer’s protocols for optimal expression and tetracycline regulation. A clone was transfected using Fugene® 6 with a pTRE2Hyg (Clontech Takara) backbone containing 3X N-terminal FLAG-tagged PTEN or empty vector control. Both stable PTEN expressing and stable control clones were selected by hygromycin treatment. To prepare a PTEN knock-down stable line, MCF7 cells were transfected using Lipofectamine® 2000  with PTEN-targeting MISSION® TRC shRNA TRCN0000002746 shRNA or the MISSION Non-targeting control SHC002 . Stable clones were selected by puromycin treatment and clones with the best knockdown were used for the experiments. All the experiments were performed when cells were at 70–80% confluency.BT-549 (HTB-122™) and MCF7 (HTB-22™) breast cancer cell lines were purchased from American Type Culture Collection  and molecularly authenticated by STRS analyses. Cells with early passage range 3–6 were used for the experiments. Both cell lines were cultured in DMEM/F-12 medium  supplemented with 10% FBS  at 37° C with 5% CO2, 0.1 mM DTT and 10% Glycerol) containing 3X FLAG peptide followed by clean-up of elution mix by using PierceTM centrifuge columns (Thermo Scientific Pierce).3xFLAG-tagged PTEN construct was designed with stable overexpression system generated as discussed above. BT-549 cells overexpressing 3x FLAG-tagged PTEN were grown in 5 × 15 cm dishes to ~90% confluency each, and cell pellets were collected. Protein lysate was prepared using the M-PER (Thermo Scientific) supplemented with protease inhibitor (Sigma-Aldrich). Cell lysates were incubated with anti-FLAG M2 agarose beads for 1 hour at 4° C followed by 5–6 times washing with pre-chilled TBS buffer containing 0.5M Tris-HCl (pH 7.5) and 1.5 M NaCl. PTEN protein was eluted by using elution buffer  supplemented with protease and phosphatase inhibitors (Sigma-Aldrich) and quantified by using the BCA protein assay (Thermo Scientific). Lysates were separated by SDS-PAGE and transferred onto nitrocellulose membranes followed by probing with appropriate antibodies . Blots were scanned digitally using the GE Amersham Imager 600 . For immunoprecipitation, protein lysates were prepared using non-denaturing lysis buffer containing 20 mM Tris HCl pH 8, 137 mM NaCl, 1% NP-40, and 2 mM EDTA, supplemented with protease and phosphatase inhibitors (Sigma-Aldrich). Lysates were pre-cleared with Protein A/G Dynabeads™ (Thermo Scientific) followed by immunoprecipitation using appropriate antibodies. Immunoprecipitated proteins were immunoblotted and detected using the method described above.200 ng of unphosphorylated or phosphorylated GST-CTD (prepared using CDK9/CyclinT1 as described above) were incubated with glutathione beads at 4° C for 3 hours followed by washing at room temperature to remove unincorporated GST-CTD. Beads were then incubated with purified PTEN at 4° C for 2 hours followed by stringent washings at room temperature. Beads were incubated at 100° C in the 2× Laemmli buffer for 5 minutes followed by immunoblotting.200 ng of GST-CTD  was incubated with CDK9/CyclinT1  for 2 hours at 30° C in kinase buffer containing 40 mM HEPES, pH 7.5, 10 mM MgCl2, 5 mM dithiothreitol, and 500 μM ATP (Invitrogen). Reaction was stopped by removing unincorporated ATP through a MicroSpin Column  according to the manufacturer’s protocol. PTEN phosphatase activity was tested by incubating phosphorylated GST-CTD with PTEN and Ssu72 (a positive control) in phosphatase buffer containing 50mM Tris-HCl, pH 6.5, 10 mM MgCl2, 20 mM KCl, and 5 mM DTT. After 2 hours of incubation at 30° C, reactions were stopped by adding 2× Laemmli buffer and incubated at 100° C for 5 minutes before immunoblotting.ChIP-seq raw data files were downloaded from Gene Expression Omnibus  for PTEN ChIP-seq  and Pol II Ser5p ChIP-seq . Reads were aligned against hg19 genome by bowtie2 using sensitive mode parameter . SAM files generated from Bowtie2 were converted into BAM files. BAM files were normalized by depth after removal of PCR duplicates and blacklisted regions. Heatmaps and metagene plots were generated in ngs.plot.r program using our curated gene list. GRCh37/hg19 gene list was curated after removing non-canonical, mitochondrial, genes shorter than 2 kb and overlapping genes on the same strand, which resulted in 21,396 genes. Pol II Ser5p ChIP-seq signals at promoter proximal regions (−50 bp to +200 bp form TSS) were counted by using bedcov function of SAMtools. The resultant gene list was sorted based on Pol II Ser5p promoter proximal signals and used for generating heatmaps.