The main focus of our lab is to understand how different mechanisms contribute to gene regulation. We are focusing or transcription factors and their binding to DNA, 3D genome organisation and DNA methylation.
3D genome organisation
Our lab is interested in understanding the mechanism of of how the genome folds in 3D within the nucleus and what is the functional role of this 3D organisation. We are using Drosophila melanogaster to understand this process and combine both experimental methods (including Hi-C, RNA-seq and ChIP-seq) and computational ones (both using published tools and custom scripts, but also developing new tools). We are able to generate contact maps at subkilobase resolution (approximately 500 bp), which allows us to address questions about the mechanism and functional role of the 3D genome organisation.
Transcription factors (TFs) are proteins that bind to the DNA to regulate gene expression. Our lab is looking at the steady state behaviour of TF binding. In particular, we proposed a mathematical framework based on statistical thermodynamics, which is able to recapitulate binding profiles of TFs in eukaryotic systems (see paper).
In addition, we are also interested to determine the contribution of the TFs binding mechanisms to the DNA. TFs use a mechanism called facilitated diffusion when binding to their targets sites and this mechanisms assumes that the molecules perform both three-dimensional diffusion and one-dimensional random walk on the DNA.
We are also interested in understanding the mechanisms behind transgenerational inheritance of DNA methylation in plants and mammals. For this, we developed an R/Bioconductor package DMRcaller that can detect differentially methylated regions between two samples from bisulfite converted libraries. The tool implements several methods for the detection of DMRs (binning the data, using a noise filter kernel, calling differentially methylated cytosines or filtering predefined regions). This is a highly versatile tool that can compute differential methylation in a context dependent manner on the entire human genome in few hours.
Lastly, we are also interested in the relationship between environmental exposures and the epigenome. By using a combination of biomarker, genetic and epigenetic data we aim to examine DNA methylation responses to several environmental pollutants that have previously been linked to a variety of adverse health outcomes. We are also interested in how vulnerable populations may be adversely affected and therefore our lab is currently researching additional factors that may have an impact on an individual’s response to environmental exposures.