MethPat: a tool for the analysis and visualisation of complex methylation patterns obtained by massively parallel sequencing.

BACKGROUND: DNA methylation at a gene promoter region has the potential to regulate gene transcription. Patterns of methylation over multiple CpG sites in a region are often complex and cell type specific, with the region showing multiple allelic patterns in a sample. This complexity is commonly obscured when DNA methylation data is summarised as an average percentage value for each CpG site (or aggregated across CpG sites). True representation of methylation patterns can only be fully characterised by clonal analysis. Deep sequencing provides the ability to investigate clonal DNA methylation patterns in unprecedented detail and scale, enabling the proper characterisation of the heterogeneity of methylation patterns. However, the sheer amount and complexity of sequencing data requires new synoptic approaches to visualise the distribution of allelic patterns. RESULTS: We have developed a new analysis and visualisation software tool "Methpat", that extracts and displays clonal DNA methylation patterns from massively parallel sequencing data aligned using Bismark. Methpat was used to analyse multiplex bisulfite amplicon sequencing on a range of CpG island targets across a panel of human cell lines and primary tissues. Methpat was able to represent the clonal diversity of epialleles analysed at specific gene promoter regions. We also used Methpat to describe epiallelic DNA methylation within the mitochondrial genome. CONCLUSIONS: Methpat can summarise and visualise epiallelic DNA methylation results from targeted amplicon, massively parallel sequencing of bisulfite converted DNA in a compact and interpretable format. Unlike currently available tools, Methpat can visualise the diversity of epiallelic DNA methylation patterns in a sample.

The Impact of MiR-33a-5p Inhibition in Pro-Inflammatory Endothelial Cells.

Evidence suggests cholesterol accumulation in pro-inflammatory endothelial cells (EC) contributes to triggering atherogenesis and driving atherosclerosis progression. Therefore, inhibiting miR-33a-5p within inflamed endothelium may prevent and treat atherosclerosis by enhancing apoAI-mediated cholesterol efflux by upregulating ABCA1. However, it is not entirely elucidated whether inhibition of miR-33a-5p in pro-inflammatory EC is capable of increasing ABCA1-dependent cholesterol efflux. In our study, we initially transfected LPS-challenged, immortalized mouse aortic EC (iMAEC) with either pAntimiR33a5p plasmid DNA or the control plasmid, pScr. We detected significant increases in both ABCA1 protein expression and apoAI-mediated cholesterol efflux in iMAEC transfected with pAntimiR33a5p when compared to iMAEC transfected with pScr. We subsequently used polymersomes targeting inflamed endothelium to deliver either pAntimiR33a5p or pScr to cultured iMAEC and showed that the polymersomes were selective in targeting pro-inflammatory iMAEC. Moreover, when we exposed LPS-challenged iMAEC to these polymersomes, we observed a significant decrease in miR-33a-5p expression in iMAEC incubated with polymersomes containing pAntimR33a5p versus control iMAEC. We also detected non-significant increases in both ABCA1 protein and apoAI-mediated cholesterol in iMAEC exposed to polymersomes containing pAntimR33a5p when compared to control iMAEC. Based on our results, inhibiting miR-33a-5p in pro-inflammatory EC exhibits atheroprotective effects, and so precisely delivering anti-miR-33a-5p to these cells is a promising anti-atherogenic strategy.