Insight into glucocorticoid receptor signalling through interactome model analysis: Boolean modelling of the glucocorticoid receptor

Research output: Research - peer-reviewArticle

  • External authors:
  • Emyr Yosef Bakker
  • Kun Tian
  • Luciano Mutti
  • Marija Krstic-Demonacos

Abstract

Glucocorticoid steroid hormones (GCs) are used to treat a wide variety of diseases because of their potent anti-inflammatory effect and their ability to induce apoptosis in lymphoid malignancies through the glucocorticoid receptor (GR). Despite ongoing research and glucocorticoid efficacy and widespread usage in medicine, resistance, disease relapse and toxicity all remain problematic. Understanding the mechanisms of glucocorticoid signalling and how resistance may arise is highly important towards improving therapy. To gain insight into this we undertook a systems biology approach, aiming to generate a Boolean model of the glucocorticoid receptor protein interaction network that encapsulates functional relationships between the GR, its target genes/genes that target GR, and the interactions between the genes that interact with the GR. This first model consists of 52 nodes representing genes or proteins, the model input (GC) and model outputs (Cell Death and Inflammation), connected by 241 logical interactions of activation or inhibition. In silico analysis followed by cell-based microarray genome-wide model validation led to an average of 57% correct predictions, which was taken further by assessment of model predictions against patient microarray data. Lastly, semi-quantitative model analysis via microarray data superimposed onto the model with a score flow algorithm has also been performed, which demonstrated significantly higher correct prediction ratios (an average of 80%), and the model has been initially assessed as a predictive clinical tool using published patient microarray data.

In summary we present an in silico simulation of the glucocorticoid receptor interaction network, linked to downstream biological processes that can be analysed to uncover relationships between GR and its interactants. Ultimately the model provides a platform for future development both by directing laboratory research and allowing for further components to be incorporated, encapsulating more interactions and genes/proteins involved in glucocorticoid receptor signalling.

Bibliographical metadata

Original languageEnglish
Article numbere1005825
JournalPL o S Computational Biology
DOIs
StatePublished - 6 Nov 2017