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Pharmacogenomics. 2010 Nov;11(11):1545-60. doi: 10.2217/pgs.10.125.

Quantitative analysis and modeling of glucocorticoid-controlled gene expression.

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1
Faculty of Life Sciences, The University of Manchester, Manchester, M13 9PT, UK.

Abstract

AIMS:

Glucocorticoid hormones are used extensively in the clinic for the treatment of acute lymphoblastic leukemia. Despite intensive research, the molecular mechanisms of glucocorticoid receptor (GR)-mediated transcriptional events that lead to the induction of apoptosis of leukemia cells, as well as the causes for the development of resistance in leukemia patients, are not yet understood. It is thought that the B-cell lymphoma 2 family members that control apoptosis, including some of the GR target genes, may play an important role in deciding cell fate. In this report we have employed pathway modeling due to the recent discovery of its usefulness as a tool for improving understanding of the mechanisms of cellular signaling, and in discovering new therapeutic targets for the treatment of various diseases.

MATERIALS & METHODS:

Detailed kinetics of GR autoregulation, as well as the kinetics of expression of its target genes and proteins Bcl-xL, Bim, Bmf and GILZ in glucocorticoid responsive and resistant leukemia cell lines were carried out. Subsequently in order to obtain further insight into the molecular mechanisms of GR signaling in this pathway a dynamic model of the induction of these genes and proteins by GR was constructed.

RESULTS:

The simulations were in good agreement with the observed experimental data suggesting that Bim was induced between 6 and 10 h after the addition of the synthetic glucocorticoid dexamethasone, possibly through rapid glucocorticoid dependent modulation of an unknown factor. Simulations and experimental results also suggested that Bmf induction did not require novel protein synthesis, and is a potential direct GR target.

CONCLUSION:

This combination of experimental analysis and model development initiates a virtuous cycle enabling further data integration and model expansion, and constitutes a novel promising framework towards a global mechanistic understanding of GR function.

PMID:
21121775
DOI:
10.2217/pgs.10.125
[Indexed for MEDLINE]
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