Analysis of mRNA and EZH2 binding in TGF-B-treated A549 cells
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PROVIDER: PRJNA746461 | ENA |
REPOSITORIES: ENA
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2022-05-25 | GSE180067 | GEO
Project description:To find out genes regulated by TGF-β in A549 cells, we compared gene expression of cells treated with 1ng/ml TGF-β versus non-treated cells and find out that expression of one transmembrane protein, TM4SF20, is reduced by TGF-β. A549 cells were treated with or without 1ng/ml TGF-β for 12h. RNA was extracted and hybridized on Affymetrix microarrays
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2018-04-26 | GSE109296 | GEO
Project description:Gene expression profile of A549 cells treated with TGF-beta
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Project description:To find out genes regulated by TGF-β in A549 cells, we compared gene expression of cells treated with 1ng/ml TGF-β versus non-treated cells and find out that expression of one transmembrane protein, TM4SF20, is reduced by TGF-β.
2014-08-04 | GSE46024 | GEO
Project description:Epithelial–mesenchymal transition (EMT) is a plastic process that converts epithelial cells into migratory and invasive cells. Accumulating evidence indicates that EMT is a key event for metastasis in several types of cancer, including non-small cell lung cancer (NSCLC). Especially, transforming growth factor-beta (TGF-beta) acts as a potent inducer of EMT and contributes to cancer progression. Emerging studies suggest that a metabolic reprograming is essential to acquire the EMT phenotype in cancer cells. However, a comprehensive understanding of metabolism in cancer EMT remains largely unexplored. Here, we analyzed metabolic changes during TGF-beta-induced EMT in NSCLC A549 cells using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS). At the same time, we examined the expression of metabolic-related genes using microarray analysis.
2021-06-17 | GSE136780 | GEO
Project description:Our objective was to identify genes upregulated by TGF-beta in control and Ezh2 cKO decidual stromal cells (DSCs), and to compare this set of genes with a previously identified gene set of H3K27me3-marked decidual genes that are putatively silenced by EZH2/PRC2 (Nancy et al. JCI. 2018). We also wanted to assess baseline (BL) gene expression in control and Ezh2 cKO DSCs, and compare it to an RNA-seq analysis of differential gene expression in whole tissue Ezh2 cKO and control decidua. RNA was isolated from untreated and TGF-beta treated decidual stromal cells from control and Ezh2 cKO mice. Sequencing provided was 706 million total reads with an average of 82.2% of these reads aligning uniquely to the mouse genome. Reads uniquely mapped to known mRNAs were used to identify gene expression changes between housing conditions using DESeq2. TGF-beta induced 638 protein-coding genes in EZH2-deficient DSCs versus 480 in control DSCs (with a 295 gene overlap), demonstrating that EZH2-deficient DSCs are transcriptionally more responsive to TGF- (P=0.0025, Fisher’s exact test). Within the 638 genes, activated fibroblast signature genes were highly over-represented. TGF-target genes were 2.7-fold over-represented within the set of genes with higher expression in EZH2-deficient DSCs at baseline (BL; i.e., when neither EZH2-deficient nor control DSCs were treated with TGF-beta; P<1x10-20) . 741 genes were overexpressed in Ezh2 cKO DSCs compared to control DSCs, and 904 genes were underexpressed. Moreover, by comparing to our laboratory's previous CHIP-Seq analysis (Nancy et al. JCI 2018), we noted that genes overexpressed in cKO mice were enriched in previously identified H3K27me3-marked genes (P<1x10-26).
2022-01-11 | GSE171724 | GEO
Project description:Effect of NORAD shRNA on A549 cells treated with TGF-beta
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Project description:Purpose: To compare the transcriptome profiling of control cells or the A549 cells in the presence or absence of acetate following TGF-β1 stimulation. Methods: A549 cells were treated with control, TGF-β1 or TGF-β1 plus acetate for 2 days. And then, we performed RNA sequencing for transcription profiling of control cells or the A549 cells in the presence or absence of acetate following TGF-β1 stimulation. The raw data were processed with bcl2fastq software and HISAT2. Results: From the transcriptome profiling and analysis, we got gene expression data of control group and in the presence or absence of acetate following TGF-β1 stimulation using A549 cells. Conclusion: The gene expression data revealed the transcriptome variation by TGF-β1 treatment and acetate can reverse TGF-β1 effect.
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Project description:Specific regulation of target genes by transforming growth factor-β (TGF-β) in a given cellular context is determined in part by transcription factors and cofactors that interact with the Smad complex. In the present study, we determined Smad2 and Smad3 (Smad2/3) binding regions in the promoters of known genes in HepG2 hepatoblastoma cells, and compared them to those in HaCaT epidermal keratinocytes to elucidate the mechanisms of cell type- and context-dependent regulation of transcription induced by TGF-β. Our results show that 81% of the Smad2/3 binding regions in HepG2 cells were not shared with those found in HaCaT cells. Hepatocyte nuclear factor 4α (HNF4α) is expressed in HepG2 cells, but not in HaCaT cells, and the HNF4α binding motif was identified as an enriched motif in the HepG2-specific Smad2/3 binding regions. ChIP-sequencing analysis of HNF4A binding regions under TGF-β stimulation revealed that 32.5% of the Smad2/3 binding regions overlapped HNF4A bindings. MIXL1 was identified as a new combinatorial target of HNF4A and Smad2/3, and both the HNF4A protein and its binding motif were required for the induction of MIXL1 by TGF-β in HepG2 cells. These findings generalize the importance of binding of HNF4A on Smad2/3 binding genomic regions for HepG2-specific regulation of transcription by TGF-β, and suggest that certain transcription factors expressed in a cell-type-specific manner play important roles in the transcription regulated by the TGF-β-Smad signaling pathway. HepG2 cells were treated with TGF-beta for 1.5 h or left untreated. anti-HNF4A ChIP-seq was performed. One lane was used for each sample.
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