Machine learning reveals STAT motifs as predictors for GR-mediated gene repression

Glucocorticoid receptor signaling: intricacies and therapeutic opportunities

The glucocorticoid receptor (GR) is a major nuclear receptor (NR) drug target for the treatment of inflammatory disorders and several cancers. Despite the effectiveness of GR ligands, their systemic action triggers a plethora of side effects, limiting long-term use. Here, we discuss new concepts of and insights into GR mechanisms of action to assist in the identification of routes toward enhanced therapeutic benefits. We zoom in on the communication between different GR domains and how this is influenced by different ligands. We detail findings on the interaction between GR and chromatin, and highlight how condensate formation and coregulator confinement can perturb GR transcriptional responses. Last, we discuss the potential of novel ligands and the therapeutic exploitation of crosstalk with other NRs.

Machine learning unveils RNA polymerase II binding as a predictor for SMAD2-dependent transcription dynamics in response to Actvin signalling

The transforming growth factor-β (TGF-β) superfamily, including Nodal and Activin, plays a critical role in various cellular processes. Understanding the intricate regulation and gene expression dynamics of TGF-β signalling is of interest due to its diverse biological roles. A machine learning approach is used to predict gene expression patterns induced by Activin using features, such as histone modifications, RNA polymerase II binding, SMAD2-binding, and mRNA half-life. RNA sequencing and ChIP sequencing datasets were analysed and differentially expressed SMAD2-binding genes were identified. These genes were classified into activated and repressed categories based on their expression patterns. The predictive power of different features and combinations was evaluated using logistic regression models and their performances were assessed. Results showed that RNA polymerase II binding was the most informative feature for predicting the expression patterns of SMAD2-binding genes. The authors provide insights into the interplay between transcriptional regulation and Activin signalling and offers a computational framework for predicting gene expression patterns in response to cell signalling.