The purpose of the experiment was to generate a time course of gene expression following irradiation. The goal was then to model this data to extract hidden variables - chiefly, the activity profiles of the p53 transcription factor. Using this information the aim was to predict which transcripts changed by IR were targets of p53. Cells in log phase (1 x 106 ml-1) were ?-irradiated with 5 Gy at room temperature (RT) at a dose rate of 2.45 Gy per minute with a 137Cs ?-irradiator. Cells were harvested at 0, 2, 4, 6, 8, 10 and 12 hours, and RNA and protein were extracted (Trizol, Invitrogen). Affymetrix U133A arrays were hybridized as standard (www.affymetrix.co.uk). Array quality was determined using R and GCOS .rpt file values. The time course was replicated three times from independent cell preparations.
Ranked prediction of p53 targets using hidden variable dynamic modeling.
Specimen part, Disease, Cell line, Time
View SamplesIn this study we used Genome Wide Transcriptional Modelling (GWTM) to investigate the temporal transcriptional changes during CD4 Th0, Th1 and Th2 differentiation in the first 24 hours after T cell activation. We measured the transcriptional response by RNA seq every four hours for a 24 hour time course. Overall design: WT CD4 T cells were isolated and purified from adult murine spleen. The purified CD4 cells were then set up in culture under three different conditions: Th0, Th1 and Th2. Cells were extracted at 4 hour timepoints during a 24hour timecourse and RNA was extracted for each timepoint under each condition. This RNA was further sequenced to analyse the genome wide transcriptional changes through time under each of the three conditions.
IFITM proteins drive type 2 T helper cell differentiation and exacerbate allergic airway inflammation.
Cell line, Subject
View SamplesThis SuperSeries is composed of the SubSeries listed below.
Transcriptional network analysis in muscle reveals AP-1 as a partner of PGC-1α in the regulation of the hypoxic gene program.
Specimen part, Treatment
View SamplesSkeletal muscle tissue shows an extraordinary cellular plasticity, but the underlying molecular mechanisms are still poorly understood. Here we use a combination of experimental and computational approaches to unravel the complex transcriptional network of muscle cell plasticity centered on the peroxisome proliferator-activated receptor coactivator 1 (PGC-1), a regulatory nexus in endurance training adaptation. By integrating data on genome-wide binding of PGC-1 and gene expression upon PGC-1 over-expression with comprehensive computational prediction of transcription factor binding sites (TFBSs), we uncover a hitherto underestimated number of transcription factor partners involved in mediating PGC-1 action. In particular, principal component analysis of TFBSs at PGC-1 binding regions predicts that, besides the well-known role of the estrogen-related receptor (ERR), the activator protein-1 complex (AP-1) plays a major role in regulating the PGC-1-controlled gene program of hypoxia response. Our findings thus reveal the complex transcriptional network of muscle cell plasticity controlled by PGC-1.
Transcriptional network analysis in muscle reveals AP-1 as a partner of PGC-1α in the regulation of the hypoxic gene program.
Treatment
View SamplesThe peroxisome proliferator-activated receptor co-activator 1 (PGC-1) coordinates the transcriptional network response to promote an improved endurance capacity in skeletal muscle, e.g. by co-activating the estrogen-related receptor (ERR) in the regulation of oxidative substrate metabolism. Despite a close functional relationship, the interaction between these two proteins has not been studied on a genomic level. We now mapped the genome-wide binding of ERR to DNA in skeletal muscle cell line with elevated PGC-1 and linked the DNA recruitment to global PGC-1 target gene regulation. We found that, surprisingly, ERR co-activation by PGC-1 is only observed in the minority of all PGC-1 recruitment sites. Nevertheless, a majority of PGC-1 target gene expression is dependent on ERR. Intriguingly, the interaction between these two proteins is controlled by the genomic context of response elements, in particular the relative GC and CpG content, monomeric and dimeric repeat binding site configuration for ERR, and adjacent recruitment of the transcription factor SP1. These findings thus not only reveal an unprecedented insight into the regulatory network underlying muscle cell plasticity, but also strongly link the genomic context of DNA response elements to control transcription factor - co-regulator interactions.
The Genomic Context and Corecruitment of SP1 Affect ERRα Coactivation by PGC-1α in Muscle Cells.
Specimen part
View SamplesSkeletal muscle tissue shows an extraordinary cellular plasticity, but the underlying molecular mechanisms are still poorly understood. Here we use a combination of experimental and computational approaches to unravel the complex transcriptional network of muscle cell plasticity centered on the peroxisome proliferator-activated receptor coactivator 1 (PGC-1), a regulatory nexus in endurance training adaptation. By integrating data on genome-wide binding of PGC-1 and gene expression upon PGC-1 over-expression with comprehensive computational prediction of transcription factor binding sites (TFBSs), we uncover a hitherto underestimated number of transcription factor partners involved in mediating PGC-1 action. In particular, principal component analysis of TFBSs at PGC-1 binding regions predicts that, besides the well-known role of the estrogen-related receptor (ERR), the activator protein-1 complex (AP-1) plays a major role in regulating the PGC-1-controlled gene program of hypoxia response. Our findings thus reveal the complex transcriptional network of muscle cell plasticity controlled by PGC-1.
Transcriptional network analysis in muscle reveals AP-1 as a partner of PGC-1α in the regulation of the hypoxic gene program.
No sample metadata fields
View SamplesThis SuperSeries is composed of the SubSeries listed below.
The Genomic Context and Corecruitment of SP1 Affect ERRα Coactivation by PGC-1α in Muscle Cells.
Specimen part
View SamplesDetermination of the genes regulated by ERRalpha nuclear receptor in MDA-MB231 cells Overall design: MDA-MB231 cells were inactivated for ERRalpha using siRNA. Three different siRNAs were used (siE1, siE2, siE3). Cells treated with a control siRNA (siC samples) were used for comparison. Duplicate samples were analyzed. Transcriptomic analysis was performed by RNA-Seq
ERRα induces H3K9 demethylation by LSD1 to promote cell invasion.
Cell line, Subject
View SamplesDetermination of the genes regulated by LSD1 in MDA-MB231 cells Overall design: MDA-MB231 cells were inactivated for LSD1 using siRNA. Two different siRNAs were used (siL1, siL2). Cells treated with a control siRNA (siC samples) were used for comparison. Duplicate samples were analyzed. Transcriptomic analysis was performed by RNA-Seq
ERRα induces H3K9 demethylation by LSD1 to promote cell invasion.
No sample metadata fields
View SamplesMelanomas are often infiltrated by activated inflammatory cells. Thus, melanoma cells are very likely stimulated by inflammatory cytokines.
Interleukins 1alpha and 1beta secreted by some melanoma cell lines strongly reduce expression of MITF-M and melanocyte differentiation antigens.
Cell line
View Samples