YWHAG Syndrome (Developmental and Epileptic Encephalopathy 56, DEE56) is an ultra- rare childhood epilepsy associated with neurodevelopmental delays, with no therapeutic intervention available. Multiple de novo mutations in the YWHAG gene, encoding for the 14-3-3gamma protein, have been identified as causative for YWHAG Syndrome. 14-3-3gamma interacts with various targets, including major neurodevelopmental signaling proteins such as components of the ROCK pathway. Despite substantial evidence of the essential role of 14-3-3gamma in neurite outgrowth, cytoskeletal rearrangements, and neuronal migration during cortical development, little is known regarding the molecular consequences of YWHAG mutations and their effect on neuronal function and survival. Here, we characterized an isogenic, pluripotent stem cell (iPSC) model of YWHAGR132C/+ cortical neurons. The YWHAGR132C/+ iPSC-derived neurons exhibited early cytoskeletal phenotypes, coupled with an elevated calcium baseline, lower frequency of calcium spikes, and reduced network activity. The widespread alterations in the transcriptome of mutant neurons revealed a biphasic dysregulation in the core genes and modulators associated with the ROCK pathway that resulted in maturation-dependent changes to cytoskeletal protein stability and calcium phenotypes. Direct inhibition of ROCK with Y27632 further increased the calcium baseline compared to the isogenic control. Exposure of YWHAGR132C/+ neurons to Trypsin-EDTA revealed underlying cytoskeletal instability, which was partially reversed by lovastatin treatment. Further, lovastatin partially rescued the elevated calcium baseline, but not the frequency or amplitude of calcium spikes. Together, these results suggest decoupling of calcium homeostasis and calcium signaling associated with cytoskeletal instability in YWHAGYWHAGR132C/+ neurons. These findings lay the groundwork for future mechanistic studies of YWHAG function and molecular therapeutic targets for YWHAG Syndrome and YWHAG-associated conditions.
Assessing nurses’ attitudes toward artificial intelligence in Kazakhstan: psychometric validation of a nine-item scale
BackgroundArtificial intelligence (AI) is increasingly integrated into healthcare, yet the attitudes and knowledge of nurses, who are the key mediators of AI implementation, remain underexplored.