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BIOCONPAGES
Comparison of DNA-methylation
and gene expression in different immune cells
Marc Bonin1, Lorette Weidel1, Pascal Schendel1, Sascha Johannes1, Karsten Mans, Stephan Flemming2, Andreas Grützkau3, Biljana Smiljanovic1, Till Sörensen1,
Stefan Günther 2, Thomas Häupl1
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
Background and Objective:
Material and Methods:
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.
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.
Figure 1 – Global overview of differential expressed genes & CpG-Sites
SLR 2
a. Transcription
b. Methylation
Distribution of methylated (a) and unmetyhlated (b) CpG-Sites in high and low
expressed monocytes depending on distance to TSS.
a.
b.
CD15
CD14
CD19naive
CD4memory
CD8naive
CD4naive
CD19memory
CD8memory
CD56
CD15
CD14
CD19naive
CD4memory
CD8naive
CD4naive
CD19memory
CD8memory
CD56
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
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 2
all
629
top 50
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.
Distribution of methylated states of genes in low (a) and high (b) expressed
monocytes depending on distance to TSS.
a.
b.
% methylation in CD14 cells
Figure 3 - Distance and methylation for CpGs of individual genes
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.
Contacts:
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: [email protected]
Web: www.charite-bioinformatik.de
www.charite-bioinformatik.de