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Excitotoxic Cell Death

  • John W. Olney
  • Masahiko J. Ishimaru

Abstract

In recent years, the excitatory amino acids (EAAs), glutamate (Glu) and aspartate (Asp), have become recognized as the Jekyll/Hyde molecules of the central nervous system (CNS). These common acidic amino acids, which are naturally present in higher concentrations than any other amino acids in the CNS, serve vitally important metabolic, neurotrophic and neurotransmitter roles, but also harbor treacherous neurotoxic (excitotoxic) potential. Recent advances in understanding the excitotoxic properties of these compounds include the identification of several receptor subtypes that mediate Glu/Asp excitotoxicity, the generation of evidence potentially linking excitotoxins of both exogenous and endogenous origin to both acute and chronic neurodegenerative disorders and the development of antiexcitotoxic drugs for protecting against such disorders. Currently, in addition to classical excitotoxicity resulting from hyperactivation of EAA ionotropic receptors, new information is beginning to emerge pertaining to other forms of excitatory transmitter neurotoxicity. Surprisingly, some of the new forms are triggered not by hyperactivation but by hypoactivation of EAA receptors. Over the past several years we have witnessed an explosion in the molecular biology of Glu receptors; over 20 receptor subunits or subtypes having been cloned and sequenced within the past five years. In addition, several Glu transporter receptors have recently been cloned and are currently being studied for their potential role in neurodegenerative diseases. These important new developments are certain to accelerate progress in understanding the relationship between Glu receptor systems and both physiological and pathological processes in the mammalian CNS.

Keywords

Amyotrophic Lateral Sclerosis NMDA Receptor Kainic Acid Domoic Acid Cell Death Process 
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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Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • John W. Olney
  • Masahiko J. Ishimaru

There are no affiliations available

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