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Updated: Aug 25, 2026

Methods for the Discovery of Novel Compounds Modulating a Gamma-Aminobutyric Acid Receptor Type A Neurotransmission
Published on: August 16, 2018
GABAA receptors: building the bridge between subunit mRNAs, their promoters, and cognate transcription factors
Janine L Steiger1, Shelley J Russek
1Laboratory of Molecular Neurobiology, Department of Pharmacology, Boston University School of Medicine, Boston, MA 02118, USA.
Abstract:
The type A gamma-aminobutyric acid (GABA(A)) receptors mediate the majority of fast inhibitory neurotransmission in the CNS, and alterations in GABA(A) receptor function is believed to be involved in the pathology of several neurological and psychiatric illnesses, such as epilepsy, anxiety, Alzheimer's disease, and schizophrenia. GABA(A) receptors can be assembled from eight distinct subunit families defined by sequence similarity: alpha(1-6), beta(1-3), gamma(1-3), delta, pi, theta, and rho(1-3). The regulation of GABA(A) receptor function in the brain is a highly compensating system, influencing both the number and the composition of receptors at the cell surface. While transcriptional and translational points of control operate in parallel, it is becoming increasingly evident that many functional changes in GABA(A) receptors reflect the differential gene regulation of its subunits. The fact that certain GABA(A) receptor subunit genes are transcribed in distinct cell types during specific periods of development strongly suggests that genetic control plays a major role in the choice of subunit variants available for receptor assembly. This review focuses on the physiological conditions that alter subunit mRNA levels, the promoters that may control such levels, and the use of a conceptual framework created by bioinformatics to study coordinate and independent GABA(A) receptor subunit gene regulation. As this exciting field moves closer to identifying the language hidden inside the chromatin of GABA(A) receptor subunit gene clusters, future experiments will be aimed at testing models generated by computational analysis with biologically relevant in vivo and in vitro assays. It is hoped that through this functional genomic approach there will be the identification of new targets for therapeutic intervention.
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