Gene analysis of the glucocorticoid receptor alpha in Alzheimer's disease.

Background: In Alzheimer's disease (AD), the hypothalamic-pituitary-adrenal (HPA) axis is hyperactive and the sensitivity to dexamethasone is decreased, suggesting a possible involvement of glucocorticoid receptor alpha (GRalpha) defects in the aetiopathology of the disease.

Methods: We, therefore, searched for the presence of mutations in the human GRalpha (hGRalpha) gene, focusing on the hormone-binding domain due to its importance in mediating glucocorticoids' effects. RNA isolated from peripheral blood mononuclear cells (PBMCs) of 15 patients with Alzheimer's disease and 20 healthy individuals was subjected to reverse transcription-polymerase chain reaction amplification (RT-PCR) analysis followed by denaturing gradient gel electrophoresis (DGGE).

Antidepressants differentially influence the transcriptional activity of the glucocorticoid receptor in vitro.

Functional normalization of the hypothalamic-pituitary-adrenal axis in depressive patients by successful treatment with antidepressants is associated with increased efficiency of corticosteroid signal transduction. Accordingly, some antidepressants have been shown to influence the activity of the glucocorticoid receptor (GR) in cultured cells. It is not clear, however, whether this is a common principle for all antidepressants throughout all classes.

Application of denaturing gradient gel electrophoresis (DGGE) to screen for mutations of the human glucocorticoid receptor alpha gene (hGRalpha).

Objectives: In a previous publication, we had presented a sensitive method to detect mutations of the segment of the human glucocorticoid receptor alpha (hGRalpha) gene encoding the ligand binding domain (LBD) and part of the DNA binding domain (DBD) of hGRalpha, as several types of glucocorticoid resistance syndromes have been correlated with mutations in the respective nucleotide sequences. However, mutations affecting various regions covering the whole length of hGRalpha are increasingly reported in a variety of disease states. We now present an expanded screening methodology to detect mutations covering the whole length of hGRalpha.