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Pfoertner, Susanne; Jeron, Andreas; Probst‐Kepper, Michael; Guzmán, Carlos A.; Hansen, Wiebke; Westendorf, Astrid M.; Toepfer, Tanja; Schrader, Andres Jan; Franzke, Anke; Buer, Jan; Geffers, Robert

doi:10.1186/gb-2006-7-7-r54

Cited by 95 publications

(44 citation statements)

References 98 publications

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“…Because we expected to find lineage-specific methylation differences with greater probability in regions associated with differential transcriptional activity, we limited our analysis to gene loci that showed cell type-specific gene expression in Treg versus Tconv cells (both in vitro expanded or freshly isolated) plus several control regions that were equally expressed in both cell types. Gene loci were selected based on our own and previously published expression studies (Pfoertner et al 2006;Hill et al 2007) to mainly include those genes that are differentially expressed in freshly isolated (unstimulated) cells but also in ex vivo cultured and expanded T cell subsets that underwent several cycles of polyclonal TCR activation (Hoffmann et al 2006). The microarray used in this study covered 12 Mb of the human genome and contained 69 regions (with a median size of 100 kb) and 128 proximal promoter regions and 181 genes, including a number of well known and functionally relevant genes such as CD40LG, IFNG, FOXP3, IL2R, CTLA4, etc.…”

Section: Identification Of Differentially Methylated Regions (Dmrs) Imentioning

confidence: 99%

Lineage-specific DNA methylation in T cells correlates with histone methylation and enhancer activity

Schmidl

Klug²,

Boeld³

et al. 2009

Genome Res.

208

193

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DNA methylation participates in establishing and maintaining chromatin structures and regulates gene transcription during mammalian development and cellular differentiation. With few exceptions, research thus far has focused on gene promoters, and little is known about the extent, functional relevance, and regulation of cell type-specific DNA methylation at promoter-distal sites. Here, we present a comprehensive analysis of differential DNA methylation in human conventional CD4+ T cells (Tconv) and CD4 + CD25 + regulatory T cells (Treg), cell types whose differentiation and function are known to be controlled by epigenetic mechanisms. Using a novel approach that is based on the separation of a genome into methylated and unmethylated fractions, we examined the extent of lineage-specific DNA methylation across whole gene loci. More than 100 differentially methylated regions (DMRs) were identified that are present mainly in cell type-specific genes (e.g., FOXP3, IL2RA, CTLA4, CD40LG, and IFNG) and show differential patterns of histone H3 lysine 4 methylation. Interestingly, the majority of DMRs were located at promoter-distal sites, and many of these areas harbor DNA methylation-dependent enhancer activity in reporter gene assays. Thus, our study provides a comprehensive, locuswide analysis of lineage-specific methylation patterns in Treg and Tconv cells, links cell type-specific DNA methylation with histone methylation and regulatory function, and identifies a number of cell type-specific, CpG methylationsensitive enhancers in immunologically relevant genes.

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Section: Identification Of Differentially Methylated Regions (Dmrs) Imentioning

confidence: 99%

Lineage-specific DNA methylation in T cells correlates with histone methylation and enhancer activity

Schmidl

Klug²,

Boeld³

et al. 2009

Genome Res.

208

193

View full text Add to dashboard Cite

show abstract

“…This customized oligonucleotide microarray is able to quantify the expression levels of 350 genes associated with the phenotype of T reg cells. For each microarray, 15 g of biotin-labeled cRNA were fragmented and mixed with a hybridization cocktail containing four biotinylated hybridization controls (BioB, BioC, BioD and Cre), as described before [13] , to assure linear correlation between cRNA concentration and signal intensity. After hybridization, washing and staining of microarray slides with Cy5-streptavidin signal intensities and spot qualities were acquired using Imagene software V5.5.2 (BioDiscovery, Los Angeles, Calif., USA).…”

Section: Microarray Analysismentioning

confidence: 99%

“…All microarray results shown were performed at least in duplicate and were generated using the human T reg chip which we developed and introduced before [13] . This customized oligonucleotide microarray is able to quantify the expression levels of 350 genes associated with the phenotype of T reg cells.…”

Section: Microarray Analysismentioning

confidence: 99%

“…For this purpose, an SAM algorithm ('significance analysis of microarray') with stringent settings was applied with a delta value of 1.632. Significant differences in regulated genes (fold change 1 1.5) were further analyzed applying unsupervised hierarchical clustering using Genesis software (version 1.4.0) as described earlier [13] . Finally, microarray replicates of each sample were averaged leading to mean signal intensities for each gene.…”

Section: Analysis Of Microarray Datamentioning

confidence: 99%

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Frequency and Gene Expression Profile of Regulatory T Cells in Renal Cell Carcinoma

Jeron¹,

Pfoertner²,

Bruder³

et al. 2009

Tumor Biol

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CD4⁺CD25^high regulatory T cells (T_reg) have the potent ability to suppress host immune responses, thus preventing autoimmune diseases. However, increased T_reg frequencies have also been found in cancer patients implicating their involvement in tumor escape from immunological control. We investigated the frequency, functional effects and gene expression pattern of T_reg in patients with renal cell carcinoma (RCC). Therefore, T_reg were isolated from the peripheral blood of 11 treatment-naïve RCC patients and 11 healthy donors applying a magnetic cell separation system. Frequency, purity after isolation and function were evaluated using FACS and suppression assays, respectively. Gene expression patterns were compared applying a self-developed customized oligonucleotide microarray and by quantitative RT-PCR. T_reg frequencies were significantly increased in RCC patients; suppression assays proved that the isolated CD4⁺CD25^high cells had the functional characteristics of T_reg cells. Comparing gene expression profiles between T_regof RCC patients and healthy controls revealed significant differences in the expression levels of 49 genes. Gene ontology identified an association of significantly up-/downregulated genes to six functional classes, particularly genes involved in apoptosis control such as LGALS1, LGALS3, BAX, IL7R and TNFRSF25. In RCC patients, frequencies of functionally active T_reg cells were elevated; the T_reg gene expression pattern differed significantly between patients and controls. As several anti-apoptotic genes were upregulated and pro-apoptotic genes were downregulated in RCC patients, we conclude that T_reg cells derived from RCC patients might be less responsive to apoptotic stimuli, possibly promoting their accumulation in tumor patients.

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“…, hybridization, washing, staining, and scanning of Affymetrix GeneChips was performed as previously described (54). Data analysis was performed using the Affymetrix Microarray Suite 5.0, Affymetrix MicroDB 3.0, and Affymetrix Data Mining Tool 3.0.…”

Section: Dna Microarray Hybridization and Analysis-sample Preparationmentioning

confidence: 99%

Serum Response Factor Contributes Selectively to Lymphocyte Development

Fleige

Alberti

Gröbe

et al. 2007

Journal of Biological Chemistry

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Untitled

Cited by 95 publications

References 98 publications

Lineage-specific DNA methylation in T cells correlates with histone methylation and enhancer activity

Lineage-specific DNA methylation in T cells correlates with histone methylation and enhancer activity

Frequency and Gene Expression Profile of Regulatory T Cells in Renal Cell Carcinoma

Serum Response Factor Contributes Selectively to Lymphocyte Development

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