We previously found that a native lipoprotein mix with a high VLDL+LDL/HDL ratio causes a global de novoDNA methylation in THP-1 macrophages. In the present experiment we assessed the consequences of global lipoprotein-induced de novo DNA methylation on global gene expression in the same cells. Moreover, we sought to use gene expression array data to measure RNA expression levels for candidate factors mediating the epigenetic effects of lipoproteins.
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View SamplesIn this study, we use a conditional mouse model for Cebpa to investigate the significance of C/EBP in HSCs. The frequency of HSCs is unaltered following deletion of C/EBP, however, upon serial transplantations of either full BM or purified HSCs, the stem cells and stem cell activity is lost. This is not due to increased proliferation, but rather caused by a shift from quiescence to apoptosis with a resultant exhaustion of the stem cell pool. We identify direct C/EBP target genes by combining genome-wide C/EBP ChIP-seq analysis in stem and progenitor cells with gene expression data from HSC with and without C/EBP. Furthermore, we explore the impact of C/EBP on active and repressive histone modifications by doing functional genome-wide ChIP-seq analysis of H3K4Me3 and H3K27Me3 in stem and progenitor cells with and without C/EBP.
C/EBPα is required for long-term self-renewal and lineage priming of hematopoietic stem cells and for the maintenance of epigenetic configurations in multipotent progenitors.
Sex, Specimen part
View SamplesHistone modifications are a key epigenetic mechanism to activate or repress the expression of genes. Data sets of matched microarray expression data and histone modification data measured by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel bioinformatic approach to detect genes that are differentially expressed between two conditions putatively caused by alterations in histone modification. We introduce a correlation measure for integrative analysis of ChIP-seq and gene expression data and demonstrate that a proper normalization of the ChIP-seq data is crucial. We suggest applying Bayesian mixture models of different distributions to further study the distribution of the correlation measure. The implicit classification of the mixture models is used to detect genes with differences between two conditions in both gene expression and histone modification. The method is applied to different data sets and its superiority to a naive separate analysis of both data types is demonstrated. This GEO series contains the expression data of the Cebpa example data set.
Integrative analysis of histone ChIP-seq and transcription data using Bayesian mixture models.
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View SamplesThe aim of the study was to investigate the role of TGIF1 in MLL-AF9 transformed cells
TGIF1 is a negative regulator of MLL-rearranged acute myeloid leukemia.
Cell line
View SamplesAnalysis of spleens from ficolin knockout and wildtype mice treated without and with LPS to induce inflammation. Ficolins are pattern recognition molecules that initiate the lectin pathway of complement. Results provide insigt into the molecular basis of the inflammatory response of ficolin knockout mice.
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Sex, Age, Specimen part, Treatment
View SamplesASCs cultured in complete medium, ASCs cultured in serum serum-deprived medium, and ASCs stimulated with VEGF in serum-deprived medium were compared. Using microarray analysis, gene expression from the whole genome was compared between conditions. Compared to ASCs in complete medium, expression of 190 and 108 ASC genes were significantly regulated altered by serum deprivation and serum deprivation combined with VEGF, respectively. No significant differences in gene expression patterns between serum-deprived ASCs and serum-deprived ASC combined with VEGF stimulation were found. Genes most prominently and significantly up-regulated by both conditions were growth factors (IGF1, BMP6, PDGFD, FGF9), adhesion molecule CLSTN2, extracellular matrix related proteins like matricellular proteins SMOC2, SPON1 and ADAMTS12, and inhibitors of proliferation (JAG1). The most significantly down-regulated genes included matrix metalloproteinases (MMP3, MMP1), and proliferation markers (CDKN3) and GREM2, - a BMP6 antagonist.
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Specimen part, Subject
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