Project description:RNA-Seq experiments on SETBP1-G870S and Empty cells was performed in order to assess if the presence of mutated SETBP1 may induce differential RNA expression.
Project description:Eukaryotic transcription factors (TFs) are key determinants of gene activity, yet they bind only a fraction of their corresponding DNA sequence motifs in any given cell type. Chromatin has the potential to restrict accessibility of binding sites; however, in which context chromatin states are instructive for TF binding remains mainly unknown. To explore the contribution of DNA methylation to constrained TF binding, we mapped DNase-I-hypersensitive sites in murine stem cells in the presence and absence of DNA methylation. Methylation-restricted sites are enriched for TF motifs containing CpGs, especially for those of NRF1. In fact, the TF NRF1 occupies several thousand additional sites in the unmethylated genome, resulting in increased transcription. Restoring de novo methyltransferase activity initiates remethylation at these sites and outcompetes NRF1 binding. This suggests that binding of DNA-methylationsensitive TFs relies on additional determinants to induce local hypomethylation. In support of this model, removal of neighbouring motifs in cis or of a TF in trans causes local hypermethylation and subsequent loss of NRF1 binding. This competition between DNA methylation and TFs in vivo reveals a case of cooperativity between TFs that acts indirectly via DNA methylation. Methylation removal by methylation-insensitive factors enables occupancy of methylation-sensitive factors, a principle that rationalizes hypomethylation of regulatory regions. DNase-seq (2 replicates) in mouse embryonic stem cells with (WT) and without DNA methylation (DNMT TKO). RNA-seq (3 replicates) in WT and DNMT TKO cells and in DNMT TKO cells after treatment with control siRNA or siRNA targeting Nrf1. H3K27ac ChIP-seq (2 replicates) in WT and DNMT TKO cells. NRF1 ChIP-seq (2 replicates) in WT and DNMT TKO cells, in WT upon culture in different conditions (adaptation to 2i and back to serum), upon transient overexpression of NRF1 and after differentiation into neuronal progenitor cells (NP). Whole-genome bisulfite sequencing in DNMT TKO cells and in WT upon culture in different conditions (adaptation to 2i and back to serum). NRF1 ChIP-seq (2 replicates) in human HMEC and HCC1954 cells.
Project description:ChIP-Seq experiments targeting H3K4me2, H3K4me3, H3K9ac, H3K27ac, H3K36me3 histone modifications have been performed in order to assess if SETBP1 binding to gDNA was associated with chromatin remodeling and to further characterize the mechanisms responsible for SETBP1-mediated transcriptional regulation
Project description:Global gene expression changes induced by Setbp1 and Setbp1(D/N) in purified mouse hematopoietic stem and progenitor cells were characterized by RNA-seq analysis; ChIP-seq studies to identify cooperation between SETBP1/SETBP1(D/N) and MLL1 in regulating gene transcription in hematopoietic stem and progenitor cells.
Project description:Understanding the binding of GATA2, RUNX1 and SMC3 in RAD21 WT vs. mutant TF-1 Cells We examined GATA2, RUNX1 and SMC3 using ChIP-seq in TF-1 erythroleukemia cells that were transduced with an inducible vector expression WT or mutant (Q592*) RAD21 after 6 days of DOX induction
Project description:The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid cost and labour intensive TF ChIP-seq assays.It is important to develop reliable and fast computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices.TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq, using either peaks or footprints as input.In addition to open-chromatin data, also Histone-Marks (HMs) can be used in TEPIC to identify candidate TF binding sites.TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength.Using machine learning techniques, we show that incorporating low affinity binding sites improves our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites.Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance.In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets.Finally, we show that these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.