1. BIOCONPAGES Comparison of DNA-methylation and gene expression in different immune cells Marc Bonin 1, Lorette Weidel 1, Pascal Schendel 1, Sascha Johannes 1, Karsten Mans, Stephan Flemming 2, Andreas Grützkau 3, Biljana Smiljanovic 1, Till Sörensen 1, Stefan Günther 2, 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-10117 Berlin Germany Tel: +49(0) 30 450 513 296 Fax: +49(0) 30 450 513 968 E-Mail:marc.bonin@charite.de Web: www.charite-bioinformatik.de Background and Objective:Material and Methods: Results: Conclusions: Cells from 4 healthy donors were sorted by FACS technology for naive and activated/memory T-cells and B-cells, NK-cells, monocytes, and granulocytes. Genome wide DNA methylation was assessed using the HumanMethylation450 BeadChip platform and Genome Studio (Illumina). Transcriptomes were determined with Affymetrix HG-U133 Plus 2.0 GeneChips. A tool has been implemented in Java and R. In a first step the program checks the quality of each microarray and normalizes the data (Affymetrix & Illimunina). Afterwards the program imports and analyses the transcription and methylation data to determine high and low transcribed genes, match them with the status of DNA methylation and save the results as.txt and.jpg files. The tool will be provided on our homepage http://www.charite-bioinformatik.de. Site specific methylation of DNA may contribute to the regulation of gene expression. Microarray based analysis of methylation refers to CpG site selected by a biostatistic algorithm without proof for actual involvement. To test for putatively effective CpG sites in immunity, we compared methylation with transcription in parallel in different sorted immune cell types. In order to perform primary analysis and to map corresponding results, software tools and an online database were developed. Contacts: www.charite-bioinformatik.de As an example, one of the performed analyses compared monocytes and T-cells. We found 4.624 genes, which showed differences in gene expression and 19.261 different DNA methylation sites. Between closer related cells like naive and activated/memory cells of the same lymphocyte subtype (CD4+ T-cells) the number decrease to 638 genes and 9.412 sites. Comparing monocytes against T-cells, corresponding changes of expression and methylation were found in only 629 of 1951 increased and in 279 of 2673 decreased expressed genes. These results and other comparisons will be presented in the BioConpages database. The database can be searched by GeneID and to retrieve information of the corresponding transcription signals and percentage of methylation in the different cell types. In general, when selecting genes differentially expressed in immune cells, only around 10% of all CpG sites annotated to a single gene were compatible with the differential expression pattern in immune cells. This type of screening enables to preselect CpG sites putatively involved in differentiation of immune cells. Thus, corresponding information of transcription and methylation is indispensible to infer methylation associated gene regulation. This applies not only for microarray but also for sequencing approaches. Figure 1 – Global overview of differential expressed genes & CpG-Sites SLR 2 CD56 CD8memory CD19memory CD4naive CD8naive CD4memory CD19naive CD14 CD15 CD56 CD8memory CD19memory CD4naive CD8naive CD4memory CD19naive CD14 CD15 Distribution of methylated (a) and unmetyhlated (b) CpG-Sites in high and low expressed monocytes depending on distance to TSS. a.b. Distribution of methylated states of genes in low (a) and high (b) expressed monocytes depending on distance to TSS. a.b. Figure 2 Increased DNA methylation in CD4+ naive cells compared to CD14+ monocytes when focussing on monocyte (CD14+) but not naive T-cell (CD4+) related genes: distribution of methylation frequency for A) all CpG sites; B) 629 genes with increased expression in CD14+ compared to CD4+ cells; C) 50 top candidates with increased expression in CD14+ cells. all629top 50 % methylation in CD14 cells nucleotides upstream of gene start Methylation of CpGs in CD14+ monocytes and CD4+ naive T-cells for the top 10 genes increased in CD14+ but not CD4+ cells. X-axis: nucleotides upstream of the gene start; Y-axis: percentage of methylation. The closer the CpG to the start of the gene, the lower the methylation level in actively transcriped genes. Only a minor fraction of all CpGs measured for a defined gene are indicative for activation or silencing of the corresponding gene. Figure 3 - Distance and methylation for CpGs of individual genes a. Transcriptionb. Methylation