Depletion of DNMT1 in differentiated human cells highlights key classes of sensitive genes and an interplay with polycomb repression

Additional file 3: Figure S2. Changes in methylation levels by genomic element. (A) Protein levels in knockdown lines by western blotting. As a control HCT116 colon cancer cells which are WT or have a homozygous mutation in DNMT1 (KO) are shown: the DNMT1-specific top band is indicated by the arrowhead at right. (B) Median levels of methylation are shown for each genomic element (listed at top). The positions of medians are also indicated at right (arrowheads). The differences between WT and KD medians were used to plot Fig. 1d. (C) Density distribution of methylation at the three main elements involved in gene regulation, shown by cell line. Demethylation seems most marked at gene bodies (Genes), indicated by increased density of probes at low methylation (β) values

The UHRF1 protein is a key regulator of retrotransposable elements and innate immune response to viral RNA in human cells

While epigenetic mechanisms such as DNA methylation and histone modification are known to be important for gene suppression, relatively little is still understood about the interplay between these systems. The UHRF1 protein can interact with both DNA methylation and repressive chromatin marks, but its primary function in humans has been unclear. To determine what that was, we first established stable UHRF1 knockdowns (KD) in normal, immortalized human fibroblasts using targeting shRNA, since CRISPR knockouts (KO) were lethal. Although these showed a loss of DNA methylation across the whole genome, transcriptional changes were dominated by the activation of genes involved in innate immune signalling, consistent with the presence of viral RNA from retrotransposable elements (REs). We confirmed using mechanistic approaches that 1) REs were demethylated and transcriptionally activated; 2) this was accompanied by activation of interferons and interferon-stimulated genes and 3) the pathway was conserved across other adult cell types. Restoring UHRF1 in either transient or stable KD systems could abrogate RE reactivation and the interferon response. Notably, UHRF1 itself could also re-impose RE suppression independent of DNA methylation, but not if the protein contained point mutations affecting histone 3 with trimethylated lysine 9 (H3K9me3) binding. Our results therefore show for the first time that UHRF1 can act as a key regulator of retrotransposon silencing independent of DNA methylation

A randomized controlled trial of folic acid intervention in pregnancy highlights a putative methylation-regulated control element at ZFP57

Table S1. Pyrosequencing and transcriptional primer sets used in this study. Pyroassay primers are given as bisulfite converted sequence. The same primers were used for both RT-PCR and RT-qPCR. (DOCX 15 kb

Supporting data for "CandiMeth: Powerful yet simple visualization and quantification of DNA methylation at candidate genes"

DNA methylation microarrays are widely used in clinical epigenetics and are often processed using R packages like ChAMP or RnBeads by trained bioinformaticians. However, looking at specific genes requires bespoke coding which wet-lab biologists or clinicians are not trained for. This leads to high demands on bioinformaticians, who in turn may lack insight into the specific biological problem. We therefore wished to develop a tool for mapping and quantification of methylation differences at candidate genomic features of interest, without using coding, to bridge this gap. We generated the workflow CandiMeth (CANDIdate METHylation) in the web-based environment Galaxy. CandiMeth takes as input any table listing differences in methylation generated by either of the popular R-based packages above and maps these to the human genome. A simple interface then allows the user to query the data using lists of gene names. CandiMeth generates 1)Tracks in the popular UCSC genome browser with an intuitive visual indicator of where differences in methylation occur between samples, or groups of samples 2) Tables containing quantitative data on the candidate regions, allowing interpretation of significance. In addition to genes and promoters, CandiMeth can analyse methylation differences at LINEs and SINEs. Cross-comparison to other open-resource genomic data at UCSC facilitates interpretation of the biological significance of the data and the design of wet lab assays to further explore methylation changes and their consequences for the candidate genes. CandiMeth (RRID:SCR_017974; Biotools:CandiMeth) allows rapid, quantitative analysis of methylation at user-specified features without the need for coding and is freely available, with extensive guidance, at CandiMeth GitHub repo

Detecting aberrant DNA methylation in Illumina DNA methylation arrays: a toolbox and recommendations for its use

In this study, our goal was to determine probe-specific thresholds for identifying aberrant, or outlying, DNA methylation and to provide guidance on the relative merits of using continuous or outlier methylation data. To construct a reference database, we downloaded Illumina Human 450K array data for more than 2,000 normal samples, characterized the distribution of DNA methylation and derived probe-specific thresholds for identifying aberrations. We made the decision to restrict our reference database to solid normal tissue and morphologically normal tissue found adjacent to solid tumours, excluding blood which has very distinctive patterns of DNA methylation. Next, we explored the utility of our outlier thresholds in several analyses that are commonly performed on DNA methylation data. Outliers are as effective as the full continuous dataset for simple tasks, like distinguishing tumour tissue from normal, but becomes less useful as the complexity of the problem increases. We developed an R package called OutlierMeth containing our thresholds, as well as functions for applying them to data

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Additional file 2: Figure S1. Variation between shRNA clonal lines. (A) Relative similarities between cell lines based on principal component analysis (PCA) of the 450K data; three independent cultures of each line were analysed. Note the clustering of lines d8R and d10R. The fraction of total variance explained by each component is indicated in brackets. (B) The 1000 sites most variably methylated between cell lines were used for hierarchical clustering. The location of sites with respect to CpG island is indicated at left. Beta values are depicted as shades from red (low) to blue (high)

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Additional file 6: Table S3. Details of the genes showing transcriptional changes in KD cell lines from Fig. 6c