Dysregulation of DNA methylation and mRNA alternative cleavage and polyadenylation (APA) are both prevalent in cancer and have been studied as independent processes. We discovered a DNA methylation-regulated APA mechanism when we compared genome-wide DNA methylation and polyadenylation site usage between DNA methylation-competent and DNA methylation-deficient cells. Here, we show that removal of DNA methylation enables CTCF binding and recruitment of the cohesin complex, which, in turn, form chromatin loops that promote proximal polyadenylation site usage. In this DNA demethylated context, either deletion of the CTCF binding site or depletion of RAD21 cohesin complex protein can recover distal polyadenylation site usage. Using data from The Cancer Genome Atlas, we authenticated the relationship between DNA methylation and mRNA polyadenylation isoform expression in vivo. This DNA methylation-regulated APA mechanism demonstrates how aberrant DNA methylation impacts transcriptome diversity and highlights the potential sequelae of global DNA methylation inhibition as a cancer treatment.

BackgroundA powerful way to identify genes for complex traits it to combine genetic and genomic methods. Many trait quantitative trait loci (QTLs) for complex traits are sex specific, but the reason for this is not well understood.Methodology/Principal FindingsRNA was prepared from bone marrow derived macrophages of 93 female and 114 male F2 mice derived from a strain intercross between apoE-deficient mice on the AKR and DBA/2 genetic backgrounds, and was subjected to transcriptome profiling using microarrays. A high density genome scan was performed using a mouse SNP chip, and expression QTLs (eQTLs) were located for expressed transcripts. Using suggestive and significant LOD score cutoffs of 3.0 and 4.3, respectively, thousands of eQTLs in the female and male cohorts were identified. At the suggestive LOD threshold the majority of the eQTLs were trans eQTLs, mapping unlinked to the position of the gene. Cis eQTLs, which mapped to the location of the gene, had much higher LOD scores than trans eQTLs, indicating their more direct effect on gene expression. The majority of cis eQTLs were common to both males and females, but only ∼1% of the trans eQTLs were shared by both sexes. At the significant LOD threshold, the majority of eQTLs were cis eQTLs, which were mostly sex-shared, while the trans eQTLs were overwhelmingly sex-specific. Pooling the male and female data, 31% of expressed transcripts were expressed at different levels in males vs. females after correction for multiple testing.Conclusions/SignificanceThese studies demonstrate a large sex effect on gene expression and trans regulation, under conditions where male and female derived cells were cultured ex vivo and thus without the influence of endogenous sex steroids. These data suggest that eQTL data from male and female mice should be analyzed separately, as many effects, such as trans regulation are sex specific.

Objective-Apolipoprotein (apo) E-deficient mice are hypercholesterolemic and develop atherosclerosis on low-fat chow diets; however, the genetic background strain has a large effect on atherosclerosis susceptibility. This study aimed to determine the genetic regions associated with strain effects on lesion area. Methods and Results-We performed a strain intercross between atherosclerosis sensitive DBA/2 and atherosclerosis resistant AKR apoE-deficient mice. Aortic root lesion area, total cholesterol, body weights, and complete blood counts were ascertained for 114 male and 95 female F 2 progeny. A high-density genome scan was performed using a mouse single nucleotide polymorphism chip yielding 1967 informative polymorphic markers. Quantitative trait locus (QTL) statistical analyses were performed. Novel loci associated with lesion or log lesion area were identified for the female and male F 2 cohorts. The atherosclerosis QTLs in female mice reside on chromosomes 15, 5, 3, and 13, and in male mice on chromosomes 17, 18, and 2. QTL were also identified for body weight, total cholesterol, and blood count parameters. Conclusions-Loci were identified for atherosclerosis susceptibility in a strain intercross study. The identity of the responsible genes at these loci remains to be determined.

Summary A critical need in understanding the biology of prostate cancer is characterizing the molecular differences between indolent and aggressive cases. Because DNA methylation can capture the regulatory state of tumors, we analyzed differential methylation patterns genome-wide among benign prostatic tissue, low grade, and high grade prostate cancer and found extensive, focal hypermethylation regions unique to high grade disease. These hypermethylation regions occurred not only in the promoters of genes, but also in gene bodies, and at intergenic regions that are enriched for DNA-protein binding sites. Integration with existing RNA-seq and survival data revealed regions where DNA methylation correlates with reduced gene expression associated with poor outcome. Regions specific to aggressive disease are proximal to genes with distinct functions from regions shared by indolent and aggressive disease. Our compendium of methylation changes reveals crucial molecular distinctions between indolent and aggressive prostate cancer.

BackgroundFibrosis of the intestine is a common and poorly understood complication of Crohn’s disease (CD) characterized by excessive deposition of extracellular matrix and accompanied by narrowing and obstruction of the gut lumen. Defining the molecular characteristics of this fibrotic disorder is a vital step in the development of specific prediction, prevention, and treatment strategies. Previous epigenetic studies indicate that alterations in DNA methylation could explain the mechanism by which mesenchymal cells adopt the requisite pro-fibrotic phenotype that promotes fibrosis progression. However, to date, genome-wide analysis of the DNA methylome of any type of human fibrosis is lacking. We employed an unbiased approach using deep sequencing to define the DNA methylome and transcriptome of purified fibrotic human intestinal fibroblasts (HIF) from the colons of patients with fibrostenotic CD.ResultsWhen compared with normal fibroblasts, we found that the majority of differential DNA methylation was within introns and intergenic regions and not associated with CpG islands. Only a low percentage occurred in the promoters and exons of genes. Integration of the DNA methylome and transcriptome identified regions in three genes that inversely correlated with gene expression: wingless-type mouse mammary tumor virus integration site family, member 2B (WNT2B) and two eicosanoid synthesis pathway enzymes (prostacyclin synthase and prostaglandin D2 synthase). These findings were independently validated by RT-PCR and bisulfite sequencing. Network analysis of the data also identified candidate molecular interactions relevant to fibrosis pathology.ConclusionsOur definition of a genome-wide fibrosis-specific DNA methylome provides new gene networks and epigenetic states by which to understand mechanisms of pathological gene expression that lead to fibrosis. Our data also provide a basis for development of new fibrosis-specific therapies, as genes dysregulated in fibrotic Crohn’s disease, following functional validation, can serve as new therapeutic targets.Electronic supplementary materialThe online version of this article (doi:10.1186/s13148-016-0193-6) contains supplementary material, which is available to authorized users.

Bioinformatic analysis often produces large sets of genomic ranges that can be difficult to interpret in the absence of genomic context. Goldmine annotates genomic ranges from any source with gene model and feature contexts to facilitate global descriptions and candidate loci discovery. We demonstrate the value of genomic context by using Goldmine to elucidate context dynamics in transcription factor binding and to reveal differentially methylated regions (DMRs) with context-specific functional correlations. The open source R package and documentation for Goldmine are available at http://jeffbhasin.github.io/goldmine.

Microbiome studies continue to provide tremendous insight into the importance of microorganism populations to the macroscopic world. High-throughput DNA sequencing technology (i.e., Next-generation Sequencing) has enabled the costeffective, rapid assessment of microbial populations when combined with bioinformatic tools capable of identifying microbial taxa and calculating the diversity and composition of biological and environmental samples. Ribosomal RNA gene sequencing, where 16S and 18S rRNA gene sequences are used to identify prokaryotic and eukaryotic species, respectively, is one of the most widely-used techniques currently employed in microbiome analysis. Prior to bioinformatic analysis of these sequences, trimming parameters must be set so that post-trimming sequence information is maximized while expected errors in the sequences themselves are minimized. In this application note, we present FIGARO: a Python-based application designed to maximize read retention after trimming and filtering for quality. FIGARO was designed specifically to increase reproducibility and minimize trial-and-error in trimming parameter selection for a DADA2-based pipeline and will likely be useful for optimizing trimming parameters and minimizing sequence errors in other pipelines as well where paired-end overlap is required. Availability and implementation:The FIGARO application is freely available as source code at https://github.com/Zymo-Research/figaro.

DNA methylation differences capture substantial information about the molecular and gene-regulatory states among biological subtypes. Enrichment-based next generation sequencing methods such as MBD-isolated genome sequencing (MiGS) and MeDIP-seq are appealing for studying DNA methylation genome-wide in order to distinguish between biological subtypes. However, current analytic tools do not provide optimal features for analyzing three-group or larger study designs. MethylAction addresses this need by detecting all possible patterns of statistically significant hyper- and hypo- methylation in comparisons involving any number of groups. Crucially, significance is established at the level of differentially methylated regions (DMRs), and bootstrapping determines false discovery rates (FDRs) associated with each pattern. We demonstrate this functionality in a four-group comparison among benign prostate and three clinical subtypes of prostate cancer and show that the bootstrap FDRs are highly useful in selecting the most robust patterns of DMRs. Compared to existing tools that are limited to two-group comparisons, MethylAction detects more DMRs with strong differential methylation measurements confirmed by whole genome bisulfite sequencing and offers a better balance between precision and recall in cross-cohort comparisons. MethylAction is available as an R package at http://jeffbhasin.github.io/methylaction.