POGZ-related disorders (also known as White-Sutton syndrome) encompass a wide range of neurocognitive abnormalities and other accompanying anomalies. Disease severity varies widely among POGZ patients and studies investigating genotype-phenotype association are scarce. Therefore, our aim was to collect data on previously unreported POGZ patients and perform a large-scale phenotype-genotype comparison from published data. Overall, 117 POGZ patients’ genotype and phenotype data were included in the analysis, including 12 novel patients. A severity scoring system was developed for the comparison. Mild and severe phenotypes were compared with the types and location of the variants and the predicted presence or absence of nonsense-mediated RNA decay (NMD). Missense variants were more often associated with mild phenotypes (p = 0.0421) and truncating variants predicted to escape NMD presented with more severe phenotypes (p < 0.0001). Within this group, variants in the prolin-rich region of the POGZ protein were associated with the most severe phenotypes (p = 0.0004). Our study suggests that gain-of-function or dominant negative effect through escaping NMD and the location of the variants in the prolin-rich domain of the protein may play an important role in the severity of manifestations of POGZ–associated neurodevelopmental disorders.

Recently, others and we identified de novo FBXO11 (F-Box only protein 11) variants as causative for a variable neurodevelopmental disorder (NDD). We now assembled clinical and mutational information on 23 additional individuals. The phenotypic spectrum remains highly variable, with developmental delay and/or intellectual disability as the core feature and behavioral anomalies, hypotonia and various facial dysmorphism as frequent aspects. The mutational spectrum includes intragenic deletions, likely gene disrupting and missense variants distributed across the protein. To further characterize the functional consequences of FBXO11 missense variants, we analyzed their effects on protein expression and localization by overexpression of 17 different mutant constructs in HEK293 and HeLa cells. We found that the majority of missense variants resulted in subcellular mislocalization and/or reduced FBXO11 protein expression levels. For instance, variants located in the nuclear localization signal and the N-terminal F-Box domain lead to altered subcellular localization with exclusion from the nucleus or the formation of cytoplasmic aggregates and to reduced protein levels in western blot. In contrast, variants localized in the C-terminal Zn-finger UBR domain lead to an accumulation in the cytoplasm without alteration of protein levels. Together with the mutational data, our functional results suggest that most missense variants likely lead to a loss of the original FBXO11 function and thereby highlight haploinsufficiency as the most likely disease mechanism for FBXO11-associated NDDs.

A child with a de novo STXBP1 heterozygous missense mutation, believed to be a pathogenic variant, presented with clustering focal seizures affecting both hemispheres. These had begun at the age of 10 months with a phenotype similar to that of PCDH19 encephalopathy. MRI suggested a similarity to focal cortical dysplasia, though further research is needed. There was no evidence of either suppression-bursts or infantile spasms. This new case adds to the few other cases of patients with STXBP1 mutation in whom imaging features of focal cortical dysplasia on MRI have been reported, implying a possible role of STXBP1 mutation in neuronal migration disorders. If such a mutation with focal seizures is suspected, the possibility of focal cortical dysplasia should be investigated. [Published with video sequences].

Sleep spindles, together with K-complexes, are hallmark oscillatory events observed in electroencephalographic (EEG) recordings during stage N2 sleep. Alterations in spindle characteristics, including frequency, amplitude, duration, and density, are frequently reported in epilepsy and may reflect underlying disturbances in thalamocortical network function. Quantitative analysis of these alterations has the potential to improve our understanding of epileptiform activity and support the development of clinically useful biomarkers. In this work, we present an automated framework for sleep spindle analysis in EEG recordings from both healthy subjects and patients with epilepsy. The framework integrates deep learning architectures (1D U-Net, SlumberNet, and SEED) with statistical evaluation methods to address two complementary tasks: (i) spindle segmentation and (ii) direct regression-based prediction of spindle characteristics. The proposed approach was validated on two datasets: the open-access Montreal Archive of Sleep Studies (MASS) and a custom clinical database of pediatric epilepsy patients acquired at the Video-EEG Laboratory “Genomed” (Moscow, Russia). Our results demonstrate that while both convolutional and hybrid recurrent–convolutional architectures achieve comparable overall F1-scores, their precision–recall profiles differ substantially. This enables a principled, context-specific selection of models, with U-Net favoring high sensitivity and SEED favoring high precision. Moreover, we show that segmentation-based pipelines consistently outperform direct regression (segmentation-free) approaches for characteristic prediction. These findings provide methodological guidance for the optimal deployment of deep learning models in sleep spindle analysis and establish a foundation for robust, automated, and clinician-independent EEG biomarkers in epilepsy.

Клинико-генетические характеристики ранней эпилептической энцефалопатии II типа, обусловленной мутациями в гене SCN2A