H3K27me3 is deposited at promoters by the preferential association of Polycomb repressive complex 2 (PRC2) with CpG-rich DNA elements regulating development by repressing gene transcription. H3K27 is also present in mono- and dimethylated states; however, the functional roles of H3K27me1 and H3K27me2 deposition remain poorly characterized. Here, we show that PRC2 activity is not only associated with H3K27me3 but also regulates all forms of H3K27 methylation in a spatially defined manner, contributing to different genomic functions in mouse embryonic stem cells. H3K27me1 accumulates within transcribed genes, promotes transcription, and is regulated by Setd2-dependent H3K36me3 deposition. Contrarily, H3K27me2 is present on approximately 70% of total histone H3 and is distributed in large chromatin domains, exerting protective functions by preventing firing of non-cell-type-specific enhancers. Considering that only 5%-10% of deregulated genes in PRC2-deficient cells are direct H3K27me3 targets, our data support an active role for all H3K27 methylated forms in regulating transcription and determining cell identity.

O-linked N-acetylglucosamine (O-GlcNAc) transferase (Ogt) activity is essential for embryonic stem cell (ESC) viability and mouse development. Ogt is present both in the cytoplasm and the nucleus of different cell types and catalyzes serine and threonine glycosylation. We have characterized the biochemical features of nuclear Ogt and identified the ten-eleven translocation (TET) proteins Tet1 and Tet2 as stable partners of Ogt in the nucleus of ESCs. We show at a genome-wide level that Ogt preferentially associates with Tet1 to genes promoters in close proximity of CpG-rich transcription start sites. These regions are characterized by low levels of DNA modification, suggesting a link between Tet1 and Ogt activities in regulating CpG island methylation. Finally, we show that Tet1 is required for binding of Ogt to chromatin affecting Tet1 activity. Taken together, our data characterize how O-GlcNAcylation is recruited to chromatin and interacts with the activity of 5-methylcytosine hydroxylases.

Data availability. The WGS and RNA expression data can be found at the European Genome-phenome Archive (EGA) under accessions EGAD00001004417 and EGAD00001004423, respectively. Code availability. Code associated with the analysis is available upon request. Ethics. The study was registered (UKCRNID 8880), approved by the Institutional Ethics Committees (REC 07/H0305/52 and 10/ H0305/1), and all subjects gave individual informed consent. Reporting summary. Additional information is included in the Life Sciences Reporting Summary, which details exact software and biological materials used and efforts made to ensure reproducibility of results. Author contributions RCF and AMF conceived the overall study. AMF and SJ analyzed the genomic data and performed statistical analyses. RCF, AMF and XL designed the experiments. AMF, XL and JM performed the experiments. GC contributed to the structural variant analysis and data visualization. SK helped compile the clinical data and aided statistical analyses. JP and SA produced and QC'ed the RNA-seq data. EO aided the whole genome sequencing of EAC cell lines. SM and NG coordinated the clinical centres and were responsible for sample collections. ME benchmarked our mutation calling pipelines. MO led the pathological sample QC for sequencing. LB and GD constructed and managed the sequencing alignment and variant calling pipelines. RCF and ST supervised the research. RCF and ST obtained funding. AMF and RCF wrote the manuscript. All authors approved the manuscript.

14Esophageal Adenocarcinoma (EAC) is a poor prognosis cancer type with rapidly rising incidence. Our 15 understanding of genetic events which drive EAC development is limited and there are few molecular 16 biomarkers for prognostication or therapeutics. We have accumulated a cohort of 551 genomically 17 characterised EACs (73% WGS and 27% WES) with clinical annotation and matched RNA-seq. Using a 18 variety of driver gene detection methods we discover 65 EAC drivers (66% novel) and describe 19 mutation and CNV types with specific functional impact. We identify a mean of 3.7 driver events per 20 case derived almost equally from copy number events and mutations. We compare driver mutation 21 rates to the exome-wide mutational excess calculated using Non-synonymous vs Synonymous 22 mutation rates (dNdS). We see mutual exclusivity or co-occurrence of events within and between a 23 number of EAC pathways (GATA factors, Core Cell cycle genes, TP53 regulators and the SWI/SNF 24 complex) suggestive of important functional relationships. These driver variants correlate with tumour 25 differentiation, sex and prognosis. Poor prognostic indicators (SMAD4, GATA4) are verified in 26 independent cohorts with significant predictive value. Over 50% of EACs contain sensitising events for 27 CDK4/6 inhibitors which are highly correlated with clinically relevant sensitivity in a panel EAC cell 28 lines. 29 30 simplest of these features is the tendency of a mutation to co-occur with other mutations in the 48 same gene at a high frequency, as detected by MutsigCV 9 . MutsigCV has been applied on several 49 occasions to EAC cohorts 6,10,11 and has identified ten known cancer genes as high confidence EAC 50 drivers (TP53, CDKN2A, SMAD4, ARID1A, ERBB2, KRAS, PIK3CA, SMARCA4, CTNNB1 and FBXW7). 51 However these analyses leave most EAC cases with only one known driver mutation, usually TP53, 52 due to the low frequency at which other drivers occur. Equivalent analyses in other cancer types 53 have identified three or four drivers per case 12,13 . Similarly, detection of copy number driver events 54 in EAC has relied on identifying regions of the genome recurrently deleted or amplified, as detected 55 by GISTIC 10,14-17 . However, GISTIC identifies relatively large regions of the genome, containing 56 hundreds of genes, with little indication of which specific gene-copy number aberrations (CNAs) may 57 actually confer a selective advantage. There are also several non-selection based mechanisms which 58 can cause recurrent CNAs, such as fragile sites where a low density of DNA replication origins causes 59 frequent structural events at a particular loci. These have not been differentiated properly from 60 selection based recurrent CNAs 18 . 61Without proper annotation of the genomic variants which drive the biology of EAC tumours 62we are left with a very large number of events, most of which are likely to be inconsequential, 63 making it extremely difficult to detect statistical associations between genomic variants and various 64 biologi...