Highlights d ChIP-Rx of PRC2.1 and PRC2.2 reveals co-occupancy at most target sites in ESCs d Loss of either PRC2.1 or PRC2.2 is insufficient to deplete H3K27me3 at target sites d JARID2-PRC2.2 chromatin association is specifically dependent on PRC1 d Combined loss of Polycomb-like proteins and JARID2 leads to mislocalization of PRC2

Driver histone H3-K27M mutations are frequent in pediatric midline brain tumors. However, the precise mechanisms by which H3-K27M causes tumor initiation remain unclear. Here, we use human hindbrain neural stem cells to model the consequences of H3.3-K27M on the epigenomic landscape in a relevant developmental context.Genome-wide mapping of epitope-tagged histone H3.3 reveals that both wildtype and K27M-mutant incorporate abundantly at pre-existing active enhancers and promoters, and to a lesser extent at PRC2-bound regions. At active enhancers, H3.3-K27M leads to focal H3K27ac loss, decreased chromatin accessibility, and reduced transcriptional expression of nearby neurodevelopmental genes. In addition, H3.3-K27M deposition at a subset of PRC2 target genes leads to increased PRC2 and PRC1 binding and augmented transcriptional repression that can be partially reversed by PRC2 inhibitors. Our work suggests that rather than imposing de novo transcriptional circuits, H3.3-K27M drives tumorigenesis by locking initiating cells in their pre-existing, immature epigenomic state, via disruption of PRC2 and enhancer functions.

Recent sequencing of the Chinese hamster ovary (CHO) cell and Chinese hamster genomes has dramatically advanced our ability to understand the biology of these mammalian cell factories. In this study, we focus on the powerhouse of the CHO cell, the mitochondrion. Utilizing a high-resolution next generation sequencing approach we sequenced the Chinese hamster mitochondrial genome for the first time and surveyed the mutational landscape of CHO cell mitochondrial DNA (mtDNA). Depths of coverage ranging from ~3,319X to 8,056X enabled accurate identification of low frequency mutations (>1%), revealing that mtDNA heteroplasmy is widespread in CHO cells. A total of 197 variants at 130 individual nucleotide positions were identified across a panel of 22 cell lines with 81% of variants occurring at an allele frequency of between 1% and 99%. 89% of the heteroplasmic mutations identified were cell line specific with the majority of shared heteroplasmic SNPs and INDELs detected in clones from 2 cell line development projects originating from the same host cell line. The frequency of common predicted loss of function mutations varied significantly amongst the clones indicating that heteroplasmic mtDNA variation could lead to a continuous range of phenotypes and play a role in cell to cell, production run to production run and indeed clone to clone variation in CHO cell metabolism. Experiments that integrate mtDNA sequencing with metabolic flux analysis and metabolomics have the potential to improve cell line selection and enhance CHO cell metabolic phenotypes for biopharmaceutical manufacturing through rational mitochondrial genome engineering.

High throughput, cost effective next generation sequencing (NGS) has enabled the publication of genome sequences for Cricetulus griseus and several Chinese hamster ovary (CHO) cell lines. RNA-Seq, the utilization of NGS technology to study the transcriptome, is expanding our understanding of the CHO cell biological system in areas ranging from the analysis of transcription start sites to the discovery of small noncoding RNAs. The analysis of RNA-Seq data, often comprised of several million short reads, presents a considerable challenge. If the CHO cell biology field is to fully exploit the potential of RNA-Seq, the development of robust data analysis pipelines is critical. In this manuscript, we outline bioinformatics approaches for the stages of a typical RNA-Seq expression profiling experiment including quality control, pre-processing, alignment and de novo transcriptome assembly. Algorithms for the analysis of mRNA and microRNA (miRNA) expression as well as methods for the detection of alternative splicing from RNA-Seq data are also presented. At this relatively early stage of Cricetulus griseus genome assembly and annotation, it is likely that a combination of isoform deconvolution and raw count based methods will provide the most complete picture of transcript expression patterns in CHO cell RNA-Seq experiments.