Seizure and movement disorder in CACNA1E developmental and epileptic encephalopathy: Two sides of the same coin or same side of two different coins?

Pathogenic variants in CACNA1E are associated with early-onset epileptic and developmental encephalopathy (DEE). Severe to profound global developmental delay, early-onset refractory seizures, severe hypotonia, and macrocephaly are the main clinical features. Patients harboring the recurrent CACNA1E variant p.(Gly352Arg) typically present with the combination of early-onset DEE, dystonia/dyskinesia, and contractures. We describe a 2-year-and-11-month-old girl carrying the p.(Gly352Arg) CACNA1E variant. She has a severe DEE with very frequent drug-resistant seizures, profound hypotonia, and episodes of dystonia and dyskinesia. Long-term video-EEG-monitoring documented subsequent tonic asymmetric seizures during wakefulness and mild paroxysmal dyskinesias of the trunk out of sleep which were thought to be a movement disorder and instead turned out to be focal hyperkinetic seizures. This is the first documented description of the EEG findings in this disorder. Our report highlights a possible overlap between cortical and subcortical phenomena in CACNA1E-DEE. We also underline how a careful electro-clinical evaluation might be necessary for a correct discernment between the two disorders, playing a fundamental role in the clinical assessment and proper management of children with CACNA1E-DEE.

Screen exposure and sleep: How the COVID-19 pandemic influenced children and adolescents - A questionnaire-based study.

BACKGROUND: COVID-19 pandemic has drastically increased the exposure to electronic devices in children, influencing their lifestyle and their sleep. This study was conducted to explore the relationship between the augmented screen exposure and sleep habits in children during and after the pandemic. METHODS: Using the "Google Forms" tool, we created an online questionnaire addressed to parents of children and adolescents aged 2-18 years. We explored the use of screens before and during/after the lockdown and assessed the presence of sleep disturbances through the Sleep Disturbance Scale for Children (SDSC), referring to the period before and during/after COVID-19 pandemic. RESULTS: We collected 1084 valid questionnaires (median age 8.5 ± 4.1 years). We observed a significant increase in screens exposure for school (72%) and for leisure (49.7%) during the pandemic. We reported an increased sleep disturbances prevalence from 22.1% before the pandemic to 33.9% during the outbreak (p < 0.001). Even before the pandemic, the highest risks for sleep disorders were related to daily screen time for school reasons (OR 1.65, p < 0.001) and total screen time after 6 p.m. (OR 1.59, p < 0.001). The augmented exposure to screens for any reasons during the pandemic was significantly related to an increase of sleep disorders, especially regarding the increased exposure after 6 p.m. (OR 1.67, p < 0.001). CONCLUSIONS: The augmented use of electronic devices was recognized to be a significant predisposing factor in increasing the rate of sleep disorders during and after the pandemic, thus sleep hygiene recommendations should be highlighted to improve sleep habits.

Using Machine Learning to Explore Shared Genetic Pathways and Possible Endophenotypes in Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a heterogeneous condition, characterized by complex genetic architectures and intertwined genetic/environmental interactions. Novel analysis approaches to disentangle its pathophysiology by computing large amounts of data are needed. We present an advanced machine learning technique, based on a clustering analysis on genotypical/phenotypical embedding spaces, to identify biological processes that might act as pathophysiological substrates for ASD. This technique was applied to the VariCarta database, which contained 187,794 variant events retrieved from 15,189 individuals with ASD. Nine clusters of ASD-related genes were identified. The 3 largest clusters included 68.6% of all individuals, consisting of 1455 (38.0%), 841 (21.9%), and 336 (8.7%) persons, respectively. Enrichment analysis was applied to isolate clinically relevant ASD-associated biological processes. Two of the identified clusters were characterized by individuals with an increased presence of variants linked to biological processes and cellular components, such as axon growth and guidance, synaptic membrane components, or transmission. The study also suggested other clusters with possible genotype-phenotype associations. Innovative methodologies, including machine learning, can improve our understanding of the underlying biological processes and gene variant networks that undergo the etiology and pathogenic mechanisms of ASD. Future work to ascertain the reproducibility of the presented methodology is warranted.

The epilepsy-autism spectrum disorder phenotype in the era of molecular genetics and precision therapy.

Autism spectrum disorder (ASD) is frequently associated with infants with epileptic encephalopathy, and early interventions targeting social and cognitive deficits can have positive effects on developmental outcome. However, early diagnosis of ASD among infants with epilepsy is complicated by variability in clinical phenotypes. Commonality in both biological and molecular mechanisms have been suggested between ASD and epilepsy, such as occurs with tuberous sclerosis complex. This review summarizes the current understanding of causal mechanisms between epilepsy and ASD, with a particularly genetic focus. Hypothetical explanations to support the conjugation of the two conditions include abnormalities in synaptic growth, imbalance in neuronal excitation/inhibition, and abnormal synaptic plasticity. Investigation of the probable genetic basis has implemented many genes, although the main risk supports existing hypotheses in that these cluster to abnormalities in ion channels, synaptic function and structure, and transcription regulators, with the mammalian target of rapamycin (mTOR) pathway and "mTORpathies" having been a notable research focus. Experimental models not only have a crucial role in determining gene functions but are also useful instruments for tracing disease trajectory. Precision medicine from gene therapy remains a theoretical possibility, but more contemporary developments continue in molecular tests to aid earlier diagnoses and better therapeutic targeting.

Clustering Analysis Supports the Detection of Biological Processes Related to Autism Spectrum Disorder.

Genome sequencing has identified a large number of putative autism spectrum disorder (ASD) risk genes, revealing possible disrupted biological pathways; however, the genetic and environmental underpinnings of ASD remain mostly unanswered. The presented methodology aimed to identify genetically related clusters of ASD individuals. By using the VariCarta dataset, which contains data retrieved from 13,069 people with ASD, we compared patients pairwise to build "patient similarity matrices". Hierarchical-agglomerative-clustering and heatmapping were performed, followed by enrichment analysis (EA). We analyzed whole-genome sequencing retrieved from 2062 individuals, and isolated 11,609 genetic variants shared by at least two people. The analysis yielded three clusters, composed, respectively, by 574 (27.8%), 507 (24.6%), and 650 (31.5%) individuals. Overall, 4187 variants (36.1%) were common to the three clusters. The EA revealed that the biological processes related to the shared genetic variants were mainly involved in neuron projection guidance and morphogenesis, cell junctions, synapse assembly, and in observational, imitative, and vocal learning. The study highlighted genetic networks, which were more frequent in a sample of people with ASD, compared to the overall population. We suggest that itemizing not only single variants, but also gene networks, might support ASD etiopathology research. Future work on larger databases will have to ascertain the reproducibility of this methodology.