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Electrophysiological Effects of Dopamine Receptor Stimulation

  • Johan Grenhoff
  • Steven W. Johnson
Part of the The Receptors book series (REC)

Abstract

The involvement of dopamine in multiple aspects of brain function has produced a great interest in dopaminergic pharmacology, and a large number of dopamine receptor ligands have been developed and tested. Dopamine receptors were initially divided into D1 and D2* subtypes on the basis of pharmacological and biochemical criteria (1). D1 receptors mediate stimulation, and D2 receptors inhibition, of adenylate cyclase by dopamine, and certain compounds interact selectively with the two types of receptor (2). In recent years, molecular cloning studies have demonstrated the presence of at least five genetically different dopamine receptor subtypes in the mammalian nervous system (3, 4). Since the cloned D 1 and D5 receptors are highly homologous and pharmacologically similar, they can be viewed as members of the D1 subfamily. In parallel, the cloned D2, D3, and D4 subtypes belong to the D2 subfamily. The pharmacologically characterized D1 and D2 receptors may involve different members of these two subfamilies. In functional studies, such as those reviewed here, the relative lack of selective compounds necessitates the continued use of the conservative D1/D2 classification, where D1 and D2 refer to the entire subfamily and not the cloned subtype. The only exception is the section on expression systems, where genetically characterized subtypes have been studied in isolation. Still, the expanded knowledge of dopamine receptor structures calls for a re-evaluation of previous concepts and an enhanced understanding of the five (or more) subtypes, which will stimulate the development of even more selective pharmacological tools and clinically useful drugs.

Keywords

Dopamine Receptor Adenylate Cyclase Chromaffin Cell Calcium Current Striatal Neuron 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Johan Grenhoff
  • Steven W. Johnson

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