Aim

Staphylococcus epidermidis (S. epidermidis) is one of the main pathogens responsible for bone and joint infections especially those involving prosthetic materials (PJI). Although less virulent than S. aureus, S. epidermidis is involved in chronic infections notably due to its ability to form biofilm. Moreover, it is frequently multiresistant to antibiotics. In this context, the development of additional or alternative antibacterial therapies targeting the biofilm is a priority.

The concept of translational research is always hampered by the problem that most of the disease phenotypes do not have a mono causal origin. Therefore most treatment schemes based on one to three drugs are not really productive for most of the patients even if the patients are carefully selected from the responder group. Here the array techniques has inspired many research groups to develop algorithms deriving interaction networks or regulatory networks from this type of data to better get rid of the complexity of the biochemical interactions. The challenge is to find networks and to select the group of master nodes which might be good targets for a balanced multi-drug treatment. This means not only to measure one data type with array techniques but to join array data from multiple platforms and different data levels. Our goal is to integrate these data types to form networks with a predictive character for osteosarcomas.

The existing web platform CAPweb/VAMP from the Institute Curie is based on a Java web-client and R. This platform is focused on array data analysis and visualisation, can be extended by additional R modules and is therefore an excellent choice to implement further algorithms for data integration and network prediction. We are now establishing algorithms beyond a pure association of effects like permutation procedures for optimal rank orders of effects in a given subset of 16 factors which can be assembled to bigger units and selection procedures of gene expression signals by gene dosage concepts.

The presented approach is sustainable because the platform can be constantly extended and improved. On the other hand this platform is end-user suitable. This is the best way to bring theoretical concepts to the bench scientist. As a consequence translational research will become more real and complex systems more feasible.

Tumorgenesis is often accompanied by transcriptional deregulation of oncogenes, such as the Epidermal Growth Factor Receptor (EGFR). Transcriptional activation of a gene requires the binding of transcription factors (TF) to regulatory DNA elements at specific transcription binding sites (TFBS). A better understanding of these interactions and regulation mechanisms is essential for the development of improved therapeutic applications.

ChIP was carried out to prove the existence of four new SP1 binding sites within intron 1 of the egfr gene. Site-directed Mutagenesis was performed on plasmids carrying the regulative sequence of the egfr gene in order to alter these binding sites. Activity of these sites and their influence on the transcriptional regulation were analysed by in vitro transcription and quantification using Ribonuclease Protection Assay (RPA) and qRT PCR.

Using ChIP, four novel SP1 binding sites could be confirmed to be active at the egfr gene intron 1 locus. Expression of the egfr gene was found to be highly dependent of these sites. Consequently, their mutation led to a 50% decrease of the transcriptional activity of the egfr gene.

The four new SP1 binding sites in the egfr intron 1 have a functional role in the egfr gene regulation, leading to a higher transcription rate. As so far only little is known about egfr gene activation, more TFs and TFBSs have to be analysed in order to gain a comprehensive understanding about the regulation of this important oncogene.