Two proteins act as the IUF1 insulin gene enhancer binding factor

IUF1 is a pancreatic β cell-specific factor which binds to the sequence 5′-CPyCTAATG-3′ (CT box) within the human insulin gene enhancer. Here we show that IUF1 is composed of 2 binding activities that can be separated by DEAE ion exchange chromatography. South Western blot analysis indicates that these distinct binding activities have apparent molecular weights of 115 kDa and 46 kDa.     

Lipid ligand-activated transcription factors regulating lipid storage and release in human macrophages

Macrophages play a pivotal role in the development of atherosclerosis. After recruitment in the sub-endothelial space, monocytes differentiate into macrophages, accumulate lipids thus forming foam cells and secrete pro-inflammatory and matrix-degrading factors, thus playing a role in plaque development, inflammation and instability. Therefore, pharmacological modulation of macrophage functions represents an attractive strategy for the prevention and treatment of cardiovascular diseases caused by atherosclerosis.     

coMET: visualisation of regional epigenome-wide association scan results and DNA co-methylation patterns

Background

Epigenome-wide association scans (EWAS) are an increasingly powerful and widely-used approach to assess the role of epigenetic variation in human complex traits. However, this rapidly emerging field lacks dedicated visualisation tools that can display features specific to epigenetic datasets.

Result

We developed coMET, an R package and online tool for visualisation of EWAS results in a genomic region of interest. coMET generates a regional plot of epigenetic-phenotype association results and the estimated DNA methylation correlation between CpG sites (co-methylation), with further options to visualise genomic annotations based on ENCODE data, gene tracks, reference CpG-sites, and user-defined features. The tool can be used to display phenotype association signals and correlation patterns of microarray or sequencing-based DNA methylation data, such as Illumina Infinium 450k, WGBS, or MeDIP-seq, as well as other types of genomic data, such as gene expression profiles. The software is available as a user-friendly online tool from http://epigen.kcl.ac.uk/cometand as an R Bioconductor package. Source code, examples, and full documentation are also available from GitHub.

Conclusion

Our new software allows visualisation of EWAS results with functional genomic annotations and with estimation of co-methylation patterns. coMET is available to a wide audience as an online tool and R package, and can be a valuable resource to interpret results in the fast growing field of epigenetics. The software is designed for epigenetic data, but can also be applied to genomic and functional genomic datasets in any species.