P02: DNA recognition-element binding by GRHL/CP2-family and NKX2-1 transcription factors
Open postions: 1 Postdoctoral researcher for 2 years and 1 PhD student
Principal investigators: Prof. Dr. Heinemann and Prof. Dr. Schmidt-Ott
Transcriptional gene regulation depends on the transcription factors’ (TFs) ability to faithfully recognize DNA target sequences in promoters or enhancers. These molecular recognition processes usually tolerate small variations within the target sequence with only moderate loss in binding affinity. In stark contrast to the wealth of structural data available on the binding geometries of TFs to their consensus target sequences, comparatively little is known about the structural and energetic interactions these TFs have with sequence variants of their binding sites. Data of this kind have considerable value for assessing genome-wide TF DNA binding patterns and for predicting consequences of mutational events by bioinformatics tools. We propose to study two distinct types of TFs: the Grainyhead-like proteins GRHL1 and GRHL2 and the thyroid-specific factor NKX2-1, for which we have determined initial binding data and crystal structures. GRHL1/2 and NKX2-1 are representative TFs because they employ distinct modes of DNA sequence recognition and binding, promising to yield complementary insight into the sequence variant-dependent DNA binding process. In addition, GRHL2 and NKX2-1 act cooperatively in epithelial tissue development, with recent data suggesting a pioneering TF role for GRHL2. We will determine the binding parameters of GRHL1/2 and NKX2-1 to hundreds of DNA target sequences identified in genome-wide mapping studies and analyze the crystal structures of these proteins bound to tens of target sequences. We will validate the impact of DNA sequence variation on TF-DNA binding using in vivo systems. From our structural and biophysical data we aim to derive generalizable rules for TF binding to enhancers and promoters that will help the RU’s bioinformaticians to im-prove their algorithms for identification and prediction of disease-causing variants in the non-coding genome. This in turn will enable clinical scientists to identify a larger number of underlying genetic causes of the rare diseases seen in their patients.