Metabotropic glutamate receptors in hippocampal long-term potentiation and learning and memory

Abstract

Glutamate receptors have been identified as important interfaces in learning and memory paradigms as well as in mechanisms of synaptic plasticity, such as long-term potentiation (LTP) and long-term depression (LTD), which are believed to be the underlying cellular basis of at least some forms of learning. Although investigations of G-protein-coupled receptors have a long history, those depending on ligand-binding of glutamate have only been discovered recently, and this is the reason why our knowledge about metabotropic glutamate receptors (mGluRs) is at present very limited. However, the development of relatively specific antagonists and agonists has enabled the analysis of the role of mGluRs in synaptic plasticity, mostly studied on the models of LTP and LTD. Among others, we have been able to demonstrate that activation of mGluRs is essential for induction and maintenance of long-lasting hippocampal LTP in vitro and in vivo. The work conducted by several groups, including ours, has now provided compelling evidence that mGluR activation is an important step in the cellular cascades leading to memory formation in vertebrates. This led us to assume, given that the hippocampus plays a prominent role in spatial rather than discrimination learning, that mGluRs may participate in the processing of spatial information via hippocampal mechanisms, and may thus be similarly important as N-methyl-D-aspartate receptors. This article surveys the literature dealing with mGluRs in hippocampal LTP and learning and memory. We will demonstrate that, although the understanding of cellular mechanisms of neuronal plasticity and of the pharmacology of learning and memory has advanced, the missing link to prove that LTP is a substrate for some form forms of learning still remains unsolved. Nevertheless, it appears reasonable to argue that mGluRs in LTP and learning may share some, but not all features, and it will be an interesting approach for further analysis to address the unresolved issues.