Reconsidering GHB: orphan drug or new model antidepressant?

For six decades, the principal mode of action of antidepressant drugs is the inhibition of monoamine re-uptake from the synaptic cleft. Tricyclic antidepressants, selective serotonin re-uptake inhibitors (SSRIs) and the new generation of dual antidepressants all exert their antidepressant effects by this mechanism. In the early days of the monoaminergic era, other efforts have been made to ameliorate the symptoms of depression by pharmacological means. The gamma-aminobutyric acid (GABA) system was and possibly still is one of the main alternative drug targets. Gammahydroxybutyrate (GHB) was developed as an orally active GABA analogue. It was tested in animal models of depression and human studies. The effects on sleep, agitation, anhedonia and depression were promising. However, the rise of benzodiazepines and tricyclic antidepressants brought GHB out of the scope of possible treatment alternatives. GHB is a GABA(B) and GHB receptor agonist with a unique spectrum of behavioural, neuroendocrine and sleep effects, and improves daytime sleepiness in various disorders such as narcolepsy, Parkinson's disease and fibromyalgia. Although it was banned from the US market at the end of the 1990s because of its abuse and overdose potential, it later was approved for the treatment of narcolepsy. New research methods and an extended view on other neurotransmitter systems as possible treatment targets of antidepressant treatment brought GHB back to the scene. This article discusses the unique neurobiological effects of GHB, its misuse potential and possible role as a model substance for the development of novel pharmacological treatment strategies in depressive disorders

Le Village suisse comme modèle d'urbanisme

This chapter introduces systems biology, its context, aims, concepts and strategies. It then describes approaches and methods used for collection of high-dimensional structural and functional genomics data, including epigenomics, transcriptomics, proteomics, metabolomics and lipidomics, and how recent technological advances in these fields have moved the bottleneck from data production to data analysis and bioinformatics. Finally, the most advanced mathematical and computational methods used for clustering, feature selection, prediction analysis, text mining and pathway analysis in functional genomics and systems biology are reviewed and discussed in the context of use cases