Background and Objective 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.

Materials and Methods 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.

Results 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.

Conclusions This type of screening enables to preselect CpG sites putatively involved in differntiation 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.