What do we do?
At the Laboratory of Systems Biology and Genetics (LSBG), we are using high-throughput sequencing, single cell genomics, microfluidics, and computational approaches 1) to decipher the regulatory code in Drosophila and Mammals with a specific focus on mesenchymal stem cell function, adipose biology and gut immunity, and 2) to examine how variations in this code affect molecular and organismal diversity. In addition to our research interests, we are actively pursuing the development of new research tools and computational pipelines that enable a better characterization of gene regulatory networks.
Understanding the DNA binding behavior of transcription factors (TFs) is critical for elucidating the transcriptional logic in a cell and for uncovering how genomic variation affects gene regulatory processes. Despite tremendous efforts to define the DNA binding specificities of TFs, only less than half of all human TFs have so far been experimentally characterized, and this situation is even worse when considering obligate or facultative heterodimers. To address this data lacuna, we developed a novel digital microtechnology (SMiLE-seq) aimed at deriving quantitative DNA binding models of single and dimeric TFs that belong to different structural families. Furthermore, using a comparable microfluidic platform and in collaboration with the Hatzimanikatis Lab (EPFL), we performed a comprehensive study to quantify cooperativity between TFs that form heterodimers. The resulting data allowed us to build a mechanistic model that accounted for all possible intermediate and final complexes that can occur between two TFs and DNA. One of the main findings that emerged using this model is that the nucleotide composition of the heterodimer binding site has an important impact on the extent of DNA binding cooperativity.
Main publications 2016
- Deplancke et al. The genetics of transcription factor DNA binding variation. Cell 2016;166:538.
- Isakova et al. Quantification of cooperativity in heterodimer-DNA binding improves the accuracy of binding specificity models. J Biol Chem 2016;291:10293.
- Isakova et al. SMiLE-seq identifies binding motifs of single and dimeric transcription factors. Nature Methods 2016, in press.