Acknowledgement: BTCure IMI grant agreement no ArthroMark grant no 01EC1009A Identification of co-expression networks of inflammatory response in immune cells Florian Heyl, Marc Bonin, Jekaterina Kokatjuhha, Sascha Johannes, Irene Ziska, Pascal Schendel, Karsten Mans, Biljana Smiljanovic, Till Sörensen, Bruno Stuhlmüller, Thomas Häupl Department of Rheumatology and Clinical Immunology, Charité University Hospital, Berlin, Germany Marc Bonin Department of Rheumatology and Clinical Immunology Charité University Hospital Charitéplatz 1 D Berlin Germany Tel: +49(0) Fax: +49(0) Web: Contacts: Background and Objective:Materials and Methods: Results: Conclusion: Transcriptomes induced in monocytes by LPS, TNF-α and IFN-α were used as a reference to identify and screen for networks of known stimuli (GSE38351). Programming was based on R, JAVA, PHP and MySQL to provide a framework for analysis and storage of data. The correlation analyses was parallelized to speed up the calculation process. In this study, the influence of the three molecular stimuli IFN-α, TNF-α and LPS on CD14-monocytes was investigated. With the help of this data and the co-expression networks as a model, relationships between the genes were analyzed. Thus study was based on an extensive de novo analysis with heuristic filter methods, correlation analyses and hierarchical clustering. A web application was developed to display the correlations in a defined list and to quickly identify relevant genes and functional relationships. With the aid of the application specific networks for two genes (IFI44 and OASL) were identified that cover no more than a hundred genes. Together with an automatically produced intersection between the results of three analyses of three different measurements the networks appear to be highly reproducible. With Pearson correlation, Spearman correlation and mutual information, three different algorithms were applied. Comparison between two normalization methods (the RMA- normalization and a modified version of the MAS5-normalization) confirms the robustness of the networks. In summary, the study demonstrates that different networks for the individual triggers can be identified. This includes especially networks, which are typical for IFN-α and which are associated with SLE. The data also demonstrate overlapping activities of the different stimuli and that selection of appropriate references for network calculation is critical. In chronic inflammatory diseases the pathomechanisms of persistence are insufficiently understood. Systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) are two of the many diseases, which are increasingly common and lead to new diagnostic and therapeutic challenges. IFN-α, TNF-α and pathogen-associated molecular patterns (PAMPs) like lipopolysaccharides (LPS) play key roles in the process of the inflammatory response in immune cells. Especially IFN-α seems to be associated with SLE and implicates the various changes in the persistence of this illness. Figure 1 Figure 1: The figure displays the networks (A) and (B) based on MAS5 normalized expression data from CD14-monocyte and the the three molecular stimuli IFN-α, TNF-α and LPS. The networks was found due the heuristic filter methods, correlations analyses and hierachical clustering in combination with the aid of our web application. Heatmap (1) presents the values from the correlation analysis and heatmaps (2) and (3) show the associated intensities. Figure 2 Figure 2: The figure points out the networks from figure 1. The sections are part of the heatmaps with the intensities. The networks (A light blue, B dark blue) can be traced back to an influence of LPS and IFN-α which is displayed in section (2). This network can also be found in cells from SLE patients. Figure 3: The two figures displays the network basedd on RMA normalized expression data from CD14-monocyte and the the three molecular stimuli IFN-α, TNF-α and LPS. The structure of the Heatmaps are analogical to figure 1 and 2. The network can also be traced back to an influence of LPS and IFN-α and retrieved in cells of SLE patients. Figure 3 (2) (3) (1)(2)(3)