Repression of transcription by the glucocorticoid receptor: A parsimonious model for the genomics era.

Título

Repression of transcription by the glucocorticoid receptor: A parsimonious model for the genomics era.

Autor

Anthony N Gerber, Robert Newton, Sarah K Sasse

Descripción

Glucocorticoids are potent anti-inflammatory drugs that are used to treat an extraordinary range of human disease, including COVID-19, underscoring the ongoing importance of understanding their molecular mechanisms. Early studies of GR signaling led to broad acceptance of models in which glucocorticoid receptor (GR) monomers tether repressively to inflammatory transcription factors, thus abrogating inflammatory gene expression. However, newer data challenge this core concept and present an exciting opportunity to reframe our understanding of GR signaling. Here, we present an alternate, two-part model for transcriptional repression by glucocorticoids. First, widespread GR-mediated induction of transcription results in rapid, primary repression of inflammatory gene transcription and associated enhancers through competition-based mechanisms. Second, a subset of GR-induced genes, including targets that are regulated in coordination with inflammatory transcription factors such as NF-κB, exerts secondary repressive effects on inflammatory gene expression. Within this framework, emerging data indicate the gene set regulated through the cooperative convergence of GR and NF-κB signaling is central to the broad clinical effectiveness of glucocorticoids in terminating inflammation and promoting tissue repair.

Fecha

2021

Materia

inflammation, Glucocorticoid receptor, negative feedback, repression, transcriptional enhancer

Identificador

10.1016/j.jbc.2021.100687

Fuente

The Journal of biological chemistry

Archivos

https://socictopen.socict.org/files/to_import/pdfs/1120ebe46602f6d925f4f550df35ad2b.pdf

Colección

Citación

Anthony N Gerber, Robert Newton, Sarah K Sasse, “Repression of transcription by the glucocorticoid receptor: A parsimonious model for the genomics era.,” SOCICT Open, consulta 18 de abril de 2026, https://www.socictopen.socict.org/items/show/6544.

Formatos de Salida

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