Diversity matters: combinatorial information coding by GABAA receptor subunits during spatial learning and its allosteric modulation.

Abstract In the hippocampus, GABA inhibition tunes network oscillations and shapes synchronous activity during spatial learning and memory coding. Once released from the presynapse, GABA primarily binds to ionotropic GABA A receptors (GABA A Rs), which are heteropentamers combinatorially assembled from nineteen known subunits to induce Cl - currents postsynaptically. Dissecting GABA A R subtype specificities in neurobiology is daunting because of differences in their developmental dynamics, regional distribution and subcellular compartmentalization. Here, we review recent data to show that the combination of single-cell mRNA-seq and neuroanatomy can reveal unprecedented cell-type and network-specificity of GABA A R subunits and limit the permutation in subunit configurations, thus rationalizing GABA A R physiology and pharmacology. By comparing RNA-seq data on principal cells and interneurons we discuss a tight match between GABA A R subunit allocation, diversity in the origins of GABA inputs and operational rules at synaptic and extrasynaptic sites. We propose that coincident analysis of all GABA A R subunits, particularly in relation to specific behaviors, could overcome existing pitfalls of the genetic and pharmacological manipulation of single subunits. By using α1 and α5 GABA A R subunits, we single out hippocampal spatial learning as a process in which, despite the many studies available to date, neither consensus nor causality exists with regards to GABA A R subtype requirements, curtailing a unifying concept on postsynaptic coding of GABA signals. Finally, we address the modulation of GABA A R subunits by dopamine and endocannabinoids through receptor heteromerization, cross-modulation of signal transduction and allostery. In sum, data in this review infer that multiparametric computation gains momentum to improve knowledge on GABA A Rs function in cognition and neuropsychiatric illnesses.