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Metadata

Name
Dissecting the regulatory strategies of NFkB RelA target genes in the inflammatory response reveals differential transactivation logics (ChIP-seq.WT.MEFs)
Repository
Gene Expression Omnibus
Identifier
geo.series:GSE132792
Description
NFkB RelA is the potent transcriptional activator of inflammatory response genes. We stringently defined a list of direct RelA target genes by integrating physical (ChIPseq) and functional (RNAseq in knockouts) datasets. We then dissected each gene's regulatory strategy by testing RelA variants in a novel primary-cell genetic complementation assay. All endogenous target genes required that RelA makes DNA-base-specific contacts, and none could be activated by the DNA binding domain alone. However, endogenous target genes differed widely in how they employ the two transactivation domains. Through model-aided analysis of the dynamic timecourse data we reveal gene-specific synergy and redundancy of TA1 and TA2. Given that post-translational modifications control TA1 activity and intrinsic affinity for coactivators determines TA2 activity, the differential TA logics suggests context-dependent vs. context-independent control of endogenous RelA-target genes. While some inflammatory initiators appear to require co-stimulatory TA1 activation, inflammatory resolvers are a part of the NFkB RelA core response.
Data or Study Types
Genome binding/occupancy profiling by high throughput sequencing
Source Organization
National Center for Biotechnology Information
Access Conditions
available
Year
2020
Access Hyperlink
http://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GSE132792

Distributions

  • Encoding Format: TXT ; URL: ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE132792/matrix/
  • Encoding Format: MINiML ; URL: ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE132792/miniml/
  • Encoding Format: SOFT ; URL: ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE132792/soft/
This project was funded in part by grant U24AI117966 from the NIH National Institute of Allergy and Infectious Diseases as part of the Big Data to Knowledge program. We thank all members of the bioCADDIE community for their valuable input on the overall project.