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

Intragenic sequences in the trophectoderm harbour the greatest proportion of methylation errors in day 17 bovine conceptuses generated using assisted reproductive technologies

Abstract Background Assisted reproductive technologies (ART) are widely used to treat fertility issues in humans and for the production of embryos in mammalian livestock. The use of these techniques, however, is not without consequence as they are often associated with inauspicious pre- and postnatal outcomes including premature birth, intrauterine growth restriction and increased incidence of epigenetic disorders in human and large offspring syndrome in cattle. Here, global DNA methylation profiles in the trophectoderm and embryonic discs of in vitro produced (IVP), superovulation-derived (SOV) and unstimulated, synchronised control day 17 bovine conceptuses (herein referred to as AI) were interrogated using the EmbryoGENE DNA Methylation Array (EDMA). Pyrosequencing was used to validate four loci identified as differentially methylated on the array and to assess the differentially methylated regions (DMRs) of six imprinted genes in these conceptuses. The impact of embryo-production induced DNA methylation aberrations was determined using Ingenuity Pathway Analysis, shedding light on the potential functional consequences of these differences. Results Of the total number of differentially methylated loci identified (3140) 77.3 and 22.7% were attributable to SOV and IVP, respectively. Differential methylation was most prominent at intragenic sequences within the trophectoderm of IVP and SOV-derived conceptuses, almost a third (30.8%) of the differentially methylated loci mapped to intragenic regions. Very few differentially methylated loci were detected in embryonic discs (ED); 0.16 and 4.9% of the differentially methylated loci were located in the ED of SOV-derived and IVP conceptuses, respectively. The overall effects of SOV and IVP on the direction of methylation changes were associated with increased methylation; 70.6% of the differentially methylated loci in SOV-derived conceptuses and 57.9% of the loci in IVP-derived conceptuses were more methylated compared to AI-conceptuses. Ontology analysis of probes associated with intragenic sequences suggests enrichment for terms associated with cancer, cell morphology and growth. Conclusion By examining (1) the effects of superovulation and (2) the effects of an in vitro system (oocyte maturation, fertilisation and embryo culture) we have identified that the assisted reproduction process of superovulation alone has the largest impact on the DNA methylome of subsequent embryos

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