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Increased GSK-3 activity is believed to contribute to the etiology of chronic disorders like Alzheimer's disease (AD), schizophrenia, diabetes, and some types of cancer, thus supporting therapeutic potential of GSK-3 inhibitors. Numerous mouse models with modified GSK-3 have been generated in order to study the physiology of GSK-3, its implication in diverse pathologies and the potential effect of GSK-3 inhibitors. In this review we have focused on the relevance of these mouse models for the study of the role of GSK-3 in apoptosis. GSK-3 is involved in two apoptotic pathways, intrinsic and extrinsic pathways, and plays opposite roles depending on the apoptotic signaling process that is activated. It promotes cell death when acting through intrinsic pathway and plays an anti-apoptotic role if the extrinsic pathway is occurring. It is important to dissect this duality since, among the diseases in which GSK-3 is involved, excessive cell death is crucial in some illnesses like neurodegenerative diseases, while a deficient apoptosis is occurring in others such as cancer or autoimmune diseases. The clinical application of a classical GSK-3 inhibitor, lithium, is limited by its toxic consequences, including motor side effects. Recently, the mechanism leading to activation of apoptosis following chronic lithium administration has been described. Understanding this mechanism could help to minimize side effects and to improve application of GSK-3 inhibitors to the treatment of AD and to extend the application to other diseases.
GSK-3, apoptosis, Neurodegenerative disorder, Mouse models, GSK-3, apoptosis, Neurodegenerative disorders, mouse models, neurodegenerative disorder, Alzheimer’s disease, Neuroscience
GSK-3, apoptosis, Neurodegenerative disorder, Mouse models, GSK-3, apoptosis, Neurodegenerative disorders, mouse models, neurodegenerative disorder, Alzheimer’s disease, Neuroscience
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 72 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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