Despite a growing number of ion channel genes implicated in hereditary ataxia, it remains unclear how ion channel mutations lead to loss-of-function or death of cerebellar neurons. Mutations in the gene , encoding the α-subunit of the BK channel have emerged as responsible for a variety of neurological phenotypes. We describe a mutation (BK) in , in a child with congenital and progressive cerebellar ataxia with cognitive impairment. The mutation in the BK channel selectivity filter dramatically reduced single-channel conductance and ion selectivity. The BK channel trafficked normally to plasma, nuclear, and mitochondrial membranes, but caused reduced neurite outgrowth, cell viability, and mitochondrial content. Small interfering RNA (siRNA) knockdown of endogenous BK channels had similar effects. The BK activator, NS1619, rescued BK cells but not siRNA-treated cells, by selectively blocking the mutant channels. When expressed in cerebellum via adenoassociated virus (AAV) viral transfection in mice, the mutant BK channel, but not the BK channel, caused progressive impairment of several gait parameters consistent with cerebellar dysfunction from 40- to 80-d-old mice. Finally, treatment of the patient with chlorzoxazone, a BK/SK channel activator, partially improved motor function, but ataxia continued to progress. These studies indicate that a loss-of-function BK channel mutation causes ataxia and acts by reducing mitochondrial and subsequently cellular viability.
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http://dx.doi.org/10.1073/pnas.1920008117 | DOI Listing |
Biophys J
January 2025
Department of Chemistry, University of Alabama at Birmingham, Birmingham, Alabama. Electronic address:
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Department of Rehabilitation and Regenerative Medicine, College of Physicians and Surgeons, Columbia University, HHSC-1518, 701 W. 168th Street, New York, NY 10032, USA.
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January 2025
Department of Chemistry and Environmental Science, New Jersey Institute of Technology, Newark, NJ 07102, USA.
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Instituto de Bioeletricidade Celular (IBIOCEL): Ciência & Saúde, Departamento de Bioquímica, Centro de Ciências Biológicas, Universidade Federal de Santa Catarina, Rua João Pio Duarte Silva, 241, Sala G 301, Florianópolis 88038-000, SC, Brazil.
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January 2025
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China.
Due to the complex and uncertain physics of lightning strike on carbon fiber-reinforced polymer (CFRP) laminates, conventional numerical simulation methods for assessing the residual strength of lightning-damaged CFRP laminates are highly time-consuming and far from pretty. To overcome these challenges, this study proposes a new prediction method for the residual strength of CFRP laminates based on machine learning. A diverse dataset is acquired and augmented from photographs of lightning strike damage areas, C-scan images, mechanical performance data, layup details, and lightning current parameters.
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