A distinct epigenetic program underlies the 1;7 translocation in myelodysplastic syndromes

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Abstract

The unbalanced translocation dic(1;7)(q10;p10) in myelodysplastic syndromes (MDS) is originated by centromeric juxtaposition resulting into 1q trisomy and 7q monosomy. More than half of cases arise after chemo/radio-therapy. To date, given the absence of genes within the centromeric regions, no specific molecular events have been identified in this cytogenetic subgroup. We performed the first comprehensive genetic and epigenetic analysis of MDS with dic(1;7)(q10;p10) compared to normal controls and therapy-related Myeloid Neoplasms (t-MNs). RNA-seq showed a unique downregulated signature in dic(1;7) cases, affecting more than 80% of differentially expressed genes. As revealed by pathway and gene ontology analyses, downregulation of ATP-binding cassette (ABC) transporters and lipid related genes, and upregulation of p53 signaling were the most relevant biological features of dic(1;7). Epigenetic supervised analysis revealed hypermethylation at intronic enhancers in the dicentric subgroup, in which low expression levels of enhancer putative target genes accounted for around 35% of the downregulated signature. Enrichment of Kruppel-like transcription factor binding sites emerged at enhancers. Furthermore, a specific hypermethylated pattern on 1q was found to underlie the hypo-expression of more than 50% of 1q-deregulated genes, despite trisomy. In summary, dic(1;7) in MDS establish a specific transcriptional program driven by a unique epigenomic signature.

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DNA methylation of distal regulatory sites characterizes dysregulation of cancer genes

Dvir Aran, Sivan Sabato, Asaf Hellman (2013)

Background Abnormal epigenetic marking is well documented in gene promoters of cancer cells, but the study of distal regulatory siteshas lagged behind.We performed a systematic analysis of DNA methylation sites connected with gene expression profilesacross normal and cancerous human genomes. Results Utilizing methylation and expression data in 58 cell types, we developed a model for methylation-expression relationships in gene promoters and extrapolated it to the genome. We mapped numerous sites at which DNA methylation was associated with expression of distal genes. These sites bind transcription factors in a methylation-dependent manner, and carry the chromatin marks of a particular class of transcriptional enhancers. In contrast to the traditional model of one enhancer site per cell type, we found that single enhancer sites may define gradients of expression levels across many different cell types. Strikingly, the identified sites were drastically altered in cancers: hypomethylated enhancer sites associated with upregulation of cancer-related genes and hypermethylated sites with downregulation. Moreover, the association between enhancer methylation and gene deregulation in cancerwas significantly stronger than the association of promoter methylationwith gene deregulation. Conclusions Methylation of distal regulatory sites is closely related to gene expression levels across the genome. Single enhancers may modulate ranges of cell-specific transcription levels, from constantlyopen promoters. In contrast to the remote relationships between promoter methylation and gene dysregulation in cancer, altered methylation of enhancer sites is closely related to gene expression profiles of transformed cells.

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MethylSig: a whole genome DNA methylation analysis pipeline.

Laura S Rozek, Fernando E Figueroa, Christopher Y. Park … (2014)

DNA methylation plays critical roles in gene regulation and cellular specification without altering DNA sequences. The wide application of reduced representation bisulfite sequencing (RRBS) and whole genome bisulfite sequencing (bis-seq) opens the door to study DNA methylation at single CpG site resolution. One challenging question is how best to test for significant methylation differences between groups of biological samples in order to minimize false positive findings. We present a statistical analysis package, methylSig, to analyse genome-wide methylation differences between samples from different treatments or disease groups. MethylSig takes into account both read coverage and biological variation by utilizing a beta-binomial approach across biological samples for a CpG site or region, and identifies relevant differences in CpG methylation. It can also incorporate local information to improve group methylation level and/or variance estimation for experiments with small sample size. A permutation study based on data from enhanced RRBS samples shows that methylSig maintains a well-calibrated type-I error when the number of samples is three or more per group. Our simulations show that methylSig has higher sensitivity compared with several alternative methods. The use of methylSig is illustrated with a comparison of different subtypes of acute leukemia and normal bone marrow samples. methylSig is available as an R package at http://sartorlab.ccmb.med.umich.edu/software. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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ChIP-Enrich: gene set enrichment testing for ChIP-seq data

Ryan Welch, Chee-Siong Lee, Paul Imbriano … (2014)

Gene set enrichment testing can enhance the biological interpretation of ChIP-seq data. Here, we develop a method, ChIP-Enrich, for this analysis which empirically adjusts for gene locus length (the length of the gene body and its surrounding non-coding sequence). Adjustment for gene locus length is necessary because it is often positively associated with the presence of one or more peaks and because many biologically defined gene sets have an excess of genes with longer or shorter gene locus lengths. Unlike alternative methods, ChIP-Enrich can account for the wide range of gene locus length-to-peak presence relationships (observed in ENCODE ChIP-seq data sets). We show that ChIP-Enrich has a well-calibrated type I error rate using permuted ENCODE ChIP-seq data sets; in contrast, two commonly used gene set enrichment methods, Fisher's exact test and the binomial test implemented in Genomic Regions Enrichment of Annotations Tool (GREAT), can have highly inflated type I error rates and biases in ranking. We identify DNA-binding proteins, including CTCF, JunD and glucocorticoid receptor α (GRα), that show different enrichment patterns for peaks closer to versus further from transcription start sites. We also identify known and potential new biological functions of GRα. ChIP-Enrich is available as a web interface (http://chip-enrich.med.umich.edu) and Bioconductor package.