Identification of gene co-expression networks associated with different cellular and immunological states Marc Bonin 1, Jekaterina Kokatjuhha 1, Stephan Flemming 2, Biljana Smiljanovic 1, Andreas Grützkau 3, Till Sörensen 1, Thomas Häupl 1 1 Department of Rheumatology and Clinical Immunology, Charité University Hospital, Berlin, Germany, 2 Institute of Pharmaceutical Sciences, University of Freiburg, Germany, 3 German Arthritis Research Center, 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: Introduction:Methods: Results: Conclusion: GeneChip HG-U133 Plus 2.0 transcriptomes from highly purified blood cell types (granulocytes, monocytes, CD4+ and CD8+ T-cell, B-cells, NK-cells) as well as from monocyte stimulation with LPS, TNF and type 1 IFN were selected from the BioRetis database ( Correlations of expression between all probesets were calculated to filter for co-regulation. Correlation matrices of selected genes were calculated, clustered and displayed in heat maps (Fig. 2). The web-platform (Fig. 1) was constructed based on Ruby on Rails to provide a framework for analysis and storage of data. Initially, correlation matrices were determined for each individual stimulation condition and its control. Stepwise combination of the three different conditions for calculation of correlation coefficients revealed a reduction of the correlation network and a reduction of overlap between the networks. This indicates increasing functional specificity of the identified candidates. All of the typical previously published IFN related genes were identified and thus confirmed our strategy. In a similar way, cell type specific co-expression networks were determined. Additional filtering for high signal intensity provides candidates for sensitive detection of the function related patterns even in highly diluted conditions. These marker panels are currently tested for detection and quantification of functional signatures in biopsies of inflamed tissue. Correlating transcription between genes in well-defined biological states identifies function-related markers and signatures. Depending on the type of function, appropriate conditions have to be selected. Knowledge about gene networks is of great importance for analysis of transcriptome data. However, current tools mainly rely on information about direct molecular interactions between proteins, which is not reflected by expression levels. These differences between transcriptome based perception of biological information and tools for network analysis are the main reason for difficulties in functional interpretation. Therefore, we started to use transcriptome data of biologically well- defined states to define functional markers and signatures as tools for future analysis. Contacts: Figure 1 a.File-Upload (drag &drop) a.Create a correlation-group (select the CEL-Files and type in a name) b.Start correlation a. Notification (Including: Name of the correlation-group, number of probesets, the threshold, number of chips, number of correlations, statistical information's, download-links) Relative expression values and correlationon matrix A. Pearson correlation of all probesets in CD14+ monocytes, "CD14_Ctl" controls before and CD14+ after IFN-α2a stimulation revealed 1808 correlation pairs with correlations coeffizient ≥0.99 or ≤ These consisted of 869 genes. The heatmap of the hierarchical clustering illustrates positive correlation in red and negative corelation in green. B. Filtering for IFN-specific genes and exclusion of "false" positive correlations reduced the 1808 probesets to 543 and the 869 genes to 151. Of all genes, only a single one was identified to be negatively correlated (suppressed by IFN-α2a stimulation), while all others were induced by IFN-α2a. Figure 2 correlation matrix relative expression CD14 CD14 IFNa2a CD14 Ctl. CD14 CD14 IFNa2a CD14 Ctl. correlation matrix relative expression AB