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Effect of ketamine on dendritic arbor development and survival of immature GABAergic neurons in vitr

Effect of ketamine on dendritic arbor development and survival of immature GABAergic neurons in vitr

  1. Paracelsus
    Vutskits L, Gascon E, Tassonyi E, Kiss JZ. Toxicological Sciences 2006;91(2):540-9.

    Ketamine, a noncompetitive antagonist of the N-methyl-D-aspartate type of glutamate receptors, was reported to induce neuronal cell death when administered to produce anesthesia in young rodents and monkeys. Subanesthetic doses of ketamine, as adjuvant to postoperative sedation and pain control, are also frequently administered to young children. However, the effects of these low concentrations of ketamine on neuronal development remain unknown. The present study was designed to evaluate the effects of increasing concentrations (0.01-40 microg/ml) and durations (1-96 h) of ketamine exposure on the differentiation and survival of immature gamma-aminobutyric acidergic (GABAergic) interneurons in culture. In line with previous studies (Scallet et al., 2004), we found that a 1-h-long exposure to ketamine at concentrations > or = 10 microg/ml was sufficient to trigger cell death. At lower concentrations of ketamine, cell loss was only observed when this drug was chronically (> 48 h) present in the culture medium. Most importantly, we found that a single episode of 4-h-long treatment with 5 microg/ml ketamine induced long-term alterations in dendritic growth, including a significant (p < 0.05) reduction in total dendritic length and in the number of branching points compared to control groups. Finally, long-term exposure (> 24 h) of neurons to ketamine at concentrations as low as 0.01 microg/ml also severely impaired dendritic arbor development. These results suggest that, in addition to its dose-dependent ability to induce cell death, even very low concentrations of ketamine could interfere with dendritic arbor development of immature GABAergic neurons and thus could potentially interfere with the development neural networks.