Project description:Global nuclear run-on sequencing analysis was performed to determine transcription elongation rates across the genome of three human cell lines.
Project description:The Wnt3a/?-catenin and Activin/Smad2,3 signaling pathways synergize to induce endodermal differentiation of human embryonic stem cells, however the mechanism is not well-understood. Using ChIP-seq and GRO-seq analyses, we report here that hESC enhancers, including Wnt3a/LEF-1 sites, hold enhancer RNAPII complexes (eRNAPII) containing high levels of Ser5P and low Ser7P. In Wnt3a signaling, ?-catenin recruits cohesin to the LEF-1:eRNAPII sites to induce enhancer-promoter looping and activate transcription of mesoendodermal (ME) genes. However, paused Ser5P-RNAPII complexes accumulate at these genes, indicating that elongation remains limiting. Subsequent Activin/Smad2,3 signaling increases P-TEFb occupancy, CTD-Ser7P, and productive elongation at ME genes. Additionally, ME genes, including EOMES and MIXL1, are repressed by the Hippo regulator, Yap1, an essential pluripotency factor. GRO-seq experiments indicate that Yap1 blocks nascent transcription and controls NELF occupancy on ME genes. Thus, Wnt3a/?-catenin and Activin/Smad2,3 pathways up-regulate transcription initiation and elongation, respectively, to overcome Yap1 repression during early hESC differentiation ChIP-seq and GROseq experiments in H1 hESCs. Cells were treated with Wnt3a (200ng/ml), Activin A (100ng/ml) or Wnt3a+Activin A (W200ng/ml+A100ng/ml) for 4h (ChIP-seq) or 6h (GRO-seq). GRO-seq in YAP depleted cells were carried out following transfection with control or YAP siRNAs . After 48h transfection, cells were left untreated or treated with Wnt3a+Activin (W200ng/ml+A100ng/ml) for additional 6h.
Project description:Transcriptional regulatory elements (TREs), including enhancers and promoters, determine the transcription levels of associated genes. We have recently shown that global run-on and sequencing (GRO-seq) with enrichment for 5'-capped RNAs reveals active TREs with high accuracy. Here, we demonstrate that active TREs can be identified by applying sensitive machine-learning methods to standard GRO-seq data. This approach allows TREs to be assayed together with gene expression levels and other transcriptional features in a single experiment. Our prediction method, called discriminative Regulatory Element detection from GRO-seq (dREG), summarizes GRO-seq read counts at multiple scales and uses support vector regression to identify active TREs. The predicted TREs are more strongly enriched for several marks of transcriptional activation, including eQTL, GWAS-associated SNPs, H3K27ac, and transcription factor binding than those identified by alternative functional assays. Using dREG, we survey TREs in eight human cell types and provide new insights into global patterns of TRE function. We analyzed GRO-seq or PRO-seq data from eight human cell lines. Please note that this study comprises new sample data plus reanalysis of old Sample data submitted by another user. Existing PRO-seq or GRO-seq data was combined as detailed in the GSE66031_readme.txt. See GSM1613181 and GSM1613182 Sample records for data processing information.
