The Polycomb repressor complex 2 (PRC2) is composed of the core subunits Ezh1/2, Suz12, and Eed, and it mediates all di- and tri-methylation of histone H3 at lysine 27 in higher eukaryotes. However, little is known about how the catalytic activity of PRC2 is regulated to demarcate H3K27me2 and H3K27me3 domains across the genome. To address this, we mapped the endogenous interactomes of Ezh2 and Suz12 in embryonic stem cells (ESCs), and we combined this with a functional screen for H3K27 methylation marks. We found that Nsd1-mediated H3K36me2 co-locates with H3K27me2, and its loss leads to genome-wide expansion of H3K27me3. These increases in H3K27me3 occurred at PRC2/PRC1 target genes and as de novo accumulation within what were previously broad H3K27me2 domains. Our data support a model in which Nsd1 is a key modulator of PRC2 function required for regulating the demarcation of genome-wide H3K27me2 and H3K27me3 domains in ESCs.

Chromatin modifications shape cell heterogeneity by activating and repressing defined sets of genes involved in cell proliferation, differentiation and development. Polycomb-repressive complexes (PRCs) act synergistically during development and differentiation by maintaining transcriptional repression of common genes. PRC2 exerts this activity by catalysing H3K27 trimethylation. Here, we show that in the intestinal epithelium PRC2 is required to sustain progenitor cell proliferation and the correct balance between secretory and absorptive lineage differentiation programs. Using genetic models, we show that PRC2 activity is largely dispensable for intestinal stem cell maintenance but is strictly required for radiation-induced regeneration by preventing Cdkn2a transcription. Combining these models with genomewide molecular analysis, we further demonstrate that preferential accumulation of secretory cells does not result from impaired proliferation of progenitor cells induced by Cdkn2a activation but rather from direct regulation of transcription factors responsible for secretory lineage commitment. Overall, our data uncover a dual role of PRC2 in intestinal homeostasis highlighting the importance of this repressive layer in controlling cell plasticity and lineage choices in adult tissues.

Sin3a is the central scaffold protein of the prototypical Hdac1/2 chromatin repressor complex, crucially required during early embryonic development for the growth of pluripotent cells of the inner cell mass. Here, we compare the composition of the Sin3a-Hdac complex between pluripotent embryonic stem (ES) and differentiated cells by establishing a method that couples two independent endogenous immunoprecipitations with quantitative mass spectrometry. We define the precise composition of the Sin3a complex in multiple cell types and identify the Fam60a subunit as a key defining feature of a variant Sin3a complex present in ES cells, which also contains Ogt and Tet1. Fam60a binds on H3K4me3-positive promoters in ES cells, together with Ogt, Tet1 and Sin3a, and is essential to maintain the complex on chromatin. Finally, we show that depletion of Fam60a phenocopies the loss of Sin3a, leading to reduced proliferation, an extended G1-phase and the deregulation of lineage genes. Taken together, Fam60a is an essential core subunit of a variant Sin3a complex in ES cells that is required to promote rapid proliferation and prevent unscheduled differentiation.

Heavy methyl Stable Isotope Labeling with Amino acids in Cell culture (hmSILAC) is a metabolic labeling strategy employed in proteomics to increase the confidence of global identification of methylated peptides by MS. However, to this day, the automatic and robust identification of heavy and light peak doublets from MS‐raw data of hmSILAC experiments is a challenging task, for which the choice of computational methods is very limited. Here, hmSEEKER, a software designed to work downstream of a MaxQuant analysis for in‐depth search of MS peak pairs that correspond to light and heavy methyl‐peptide within MaxQuant‐generated tables is described with good sensitivity and specificity. The software is written in Perl, and its code and user manual are freely available at Bitbucket (https://bit.ly/2scCT9u).

Background In epigenetic research, both the increasing ease of high-throughput sequencing and a greater interest in genome-wide studies have resulted in an exponential flooding of epigenetic-related data in public domain. This creates an opportunity for exploring data outside the limits of any specific query-centred study. Such data have to undergo standard primary analyses that are accessible with multiple well-stabilized programs. Further downstream analyses, such as genome-wide comparative, correlative and quantitative analyses, are critical in deciphering key biological features. However, these analyses are only accessible for computational researchers and completely lack platforms capable of handling, analysing and linking multiple interdisciplinary datasets with efficient analytical methods.ResultsHere, we present EpiMINE, a program for mining epigenomic data. It is a user-friendly, stand-alone computational program designed to support multiple datasets, for performing genome-wide correlative and quantitative analysis of ChIP-seq and RNA-seq data. Using data available from the ENCODE project, we illustrated several features of EpiMINE through different biological scenarios to show how easy some known observations can be verified. These results highlight how these approaches can be helpful in identifying novel biological features.ConclusionsEpiMINE performs different kinds of genome-wide quantitative and correlative analyses, using ChIP-seq- and RNA-seq-related datasets. Its framework enables it to be used by both experimental and computational researchers. EpiMINE can be downloaded from https://sourceforge.net/projects/epimine/.Electronic supplementary materialThe online version of this article (doi:10.1186/s13072-016-0095-z) contains supplementary material, which is available to authorized users.

Cancer remains a leading cause of morbidity and mortality worldwide. Its evolutionary nature and resultant complex interactions with the tumour micro-environment and the host immune system engender heterogeneity, make developing interventions difficult. Usually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths. Therefore early detection of cancer, combined with current therapies, would have a significant impact on survival and treatment of this insidious disease. Epigenetic changes such as DNA methylation are some of the early events in carcinogenesis. Here, we report on a machine learning model that can classify 13 types of cancer as well as non-cancer tissue samples using only DNA methylome data, with an accuracy of 98.2%. We utilise the features identified by this model to develop a robust deep neural network that can generalise to independent data sets. We also demonstrate that the methylation associated genomic loci detected by the classifier are associated with genes involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.