Major depression is a devastating disease that affects up to 20% of world population and is classified today as a leading cause of disability-adjusted life years. Since late 50s with the serendipitous discovery of the first antidepressant agents, pathophysiology and therapeutics of depression are governed by the monoaminergic hypothesis. Monoaminergic-based treatment, although still in use today, was proven inefficient to treat a significant proportion of cases and presents a delayed onset of action. Recent research has unveiled an array of new mechanisms through which antidepressant medication helps restore neuronal plasticity and neurotransmission that is disrupted in mood disorders and in animal models of depression. Glutamatergic transmission, in particular AMPA receptor, and signal transduction cascades have been implicated both in antidepressant action and the pathophysiology of depression, as here and now regulators that mediate persistent changes. In this study, AMPA receptors positive modulators demonstrated antidepressant-like effects in a chronic model of depression and preliminary data suggest a faster onset of action than conventional antidepressants. The ERK/MAPK signaling pathway was also studied as a major integrator of synaptic plasticity modifications that links extracellular signals to gene expression regulation via its downstream molecular partners. We used a new class of inhibitors of the ERK pathway, whose design was based on the particular property of ERK to bind to its downstream targets via specific docking domains. A considerable amount of data provided evidence for an antidepressant action of selective inhibition of the ERK/Elk1 signaling complex in multiple animal models of depression. Overall, the findings of this work reveal novel, promising targets for the treatment of depression.
| Identifer | oai:union.ndltd.org:CCSD/oai:tel.archives-ouvertes.fr:tel-00879897 |
| Date | 06 January 2012 |
| Creators | Apazoglou, Kalliopi |
| Publisher | Université René Descartes - Paris V |
| Source Sets | CCSD theses-EN-ligne, France |
| Language | English |
| Detected Language | English |
| Type | PhD thesis |
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