Federico Sicca

Idiopathic Epilepsies with Seizures Precipitated by Fever and SCN1A Abnormalities

Summary:  Purpose: SCN1A is the most clinically relevant epilepsy gene, most mutations lead to severe myoclonic epilepsy of infancy (SMEI) and generalized epilepsy with febrile seizures plus (GEFS+). We studied 132 patients with epilepsy syndromes with seizures precipitated by fever, and performed phenotype–genotype correlations with SCN1A alterations.

Germline and mosaic mutations of FLN1 in men with periventricular heterotopia

Objective: To describe the phenotypic spectrum and genetics of periventricular nodular heterotopia (PNH) caused by FLN1 mutations in four men. Background: X-linked PNH caused by FLN1 mutations (MIM #300049) implies prenatal or early postnatal lethality in boys and 50% recurrence risk in daughters of affected women. Methods: Clinical examination, cognitive testing, MRI, and mutation analysis (denaturing high-performance liquid chromatography and direct sequencing) on blood lymphocytes and single hair roots were performed for nine affected individuals, including three men. Neuropathologic study of the brain was performed for an affected boy. Results: In two families, missense mutations were transmitted from mother to son (Met102Val) and from father to daughter (Ser149Phe), causing mild phenotypes in both genders, including unilateral PNH. In a third family, a man was mosaic for an A>G substitution (intron 11 acceptor splice site) on leukocyte DNA and hair roots (mutant = 42% and 69%). Single hair root analysis confirmed that the mutation was not present in all ectodermal derivative cells. A healthy daughter had inherited the X chromosome from her father’s wild-type germinal cell population. In the fourth family, an eight-base deletion (AGGAGGTG, intron 25 donor splice site) led to early deaths of boys. Postmortem study in a newborn boy revealed PNH and cardiovascular, genitourinary, and gut malformations. Conclusions: Periventricular nodular heterotopia caused by FLN1 mutations in men has a wide clinical spectrum and is caused by different genetic mechanisms, including somatic mosaicism. Mutation analysis of FLN1 should support genetic counseling in men with periventricular nodular heterotopia.

Mosaic mutations of the LIS1 gene cause subcortical band heterotopia.

Background: Subcortical band heterotopia (SBH) is a neuronal migration disorder. DCX mutations are responsible for almost all familial cases, 80% of sporadic female cases, and 25% of sporadic male cases of SBH, and are associated with more severe gyral and migration abnormality over the anterior brain regions. Somatic mosaicism has previously been hypothesized in a patient with posteriorly predominant SBH and a mutation of the LIS1 gene, which is usually mutated in patients with severe lissencephaly. The authors identified mosaic mutations of LIS1 in two patients (Patients 1 and 2) with predominantly posterior SBH. Methods: After ruling out DCX mutations, the authors performed sequencing of the LIS1 gene in lymphocyte DNA. Because sequence peaks in both patients were suggestive of mosaic mutations, they followed up with denaturing high-pressure liquid chromatography analysis on blood and hair root DNA and compared the areas of heteroduplex and homoduplex peaks. A third patient showing the same mutation as Patient 2 but with no evidence of mosaicism was used for comparing the phenotype of mosaic vs full mutation. Results: The two patients with posterior SBH harbored a missense (Arg241Pro) and a nonsense (R8X) mosaic mutation of LIS1. The rate of mosaicism in Patient 1 was 18% in the blood and 21% in the hair roots, whereas in Patient 2 it was 24% and 31% in the same tissues. The patient with a full R8X mutation of LIS1 had severe lissencephaly. Conclusions: Subcortical band heterotopia can occur with mosaic mutations of the LIS1 gene. Mutation analysis of LIS1, using highly sensitive techniques such as denaturing high-pressure liquid chromatography, should be considered for patients with posteriorly predominant subcortical band heterotopia and pachygyria.

Spectrum of phenotypes in female patients with epilepsy due to protocadherin 19 mutations.

Purpose:  To describe clinical and neuropsychological features of six consecutive sporadic girls with protocadherin 19 (PCDH19) mutations.

On the application of quantitative EEG for characterizing autistic brain: a systematic review

Autism-Spectrum Disorders (ASD) are thought to be associated with abnormalities in neural connectivity at both the global and local levels. Quantitative electroencephalography (QEEG) is a non-invasive technique that allows a highly precise measurement of brain function and connectivity. This review encompasses the key findings of QEEG application in subjects with ASD, in order to assess the relevance of this approach in characterizing brain function and clustering phenotypes. QEEG studies evaluating both the spontaneous brain activity and brain signals under controlled experimental stimuli were examined. Despite conflicting results, literature analysis suggests that QEEG features are sensitive to modification in neuronal regulation dysfunction which characterize autistic brain. QEEG may therefore help in detecting regions of altered brain function and connectivity abnormalities, in linking behavior with brain activity, and subgrouping affected individuals within the wide heterogeneity of ASD. The use of advanced techniques for the increase of the specificity and of spatial localization could allow finding distinctive patterns of QEEG abnormalities in ASD subjects, paving the way for the development of tailored intervention strategies.

Genetically induced dysfunctions of Kir2.1 channels: implications for short QT3 syndrome and autism–epilepsy phenotype

Short QT3 syndrome (SQT3S) is a cardiac disorder characterized by a high risk of mortality and associated with mutations in Kir2.1 (KCNJ2) channels. The molecular mechanisms leading to channel dysfunction, cardiac rhythm disturbances and neurodevelopmental disorders, potentially associated with SQT3S, remain incompletely understood. Here, we report on monozygotic twins displaying a short QT interval on electrocardiogram recordings and autism–epilepsy phenotype. Genetic screening identified a novel KCNJ2 variant in Kir2.1 that (i) enhanced the channels surface expression and stability at the plasma membrane, (ii) reduced protein ubiquitylation and degradation, (iii) altered protein compartmentalization in lipid rafts by targeting more channels to cholesterol-poor domains and (iv) reduced interactions with caveolin 2. Importantly, our study reveals novel physiological mechanisms concerning wild-type Kir2.1 channel processing by the cell, such as binding to both caveolin 1 and 2, protein degradation through the ubiquitin–proteasome pathway; in addition, it uncovers a potential multifunctional site that controls Kir2.1 surface expression, protein half-life and partitioning to lipid rafts. The reported mechanisms emerge as crucial also for proper astrocyte function, suggesting the need for a neuropsychiatric evaluation in patients with SQT3S and offering new opportunities for disease management.

Focal seizures with affective symptoms are a major feature of PCDH19 gene-related epilepsy

Purpose:  Mutations of the protocadherin19 gene (PCDH19) cause a female‐related epilepsy of variable severity, with or without mental retardation and autistic features. Despite the increasing number of patients and mutations reported, the epilepsy phenotype associated with PCDH19 mutations is still unclear. We analyzed seizure semiology through ictal video–electroencephalography (EEG) recordings in a large series of patients.

Epilepsy, Behavioral Abnormalities, and Physiological Comorbidities in Syntaxin-Binding Protein 1 (STXBP1) Mutant Zebrafish

Mutations in the synaptic machinery gene syntaxin-binding protein 1, STXBP1 (also known as MUNC18-1), are linked to childhood epilepsies and other neurodevelopmental disorders. Zebrafish STXBP1 homologs (stxbp1a and stxbp1b) have highly conserved sequence and are prominently expressed in the larval zebrafish brain. To understand the functions of stxbp1a and stxbp1b, we generated loss-of-function mutations using CRISPR/Cas9 gene editing and studied brain electrical activity, behavior, development, heart physiology, metabolism, and survival in larval zebrafish. Homozygous stxbp1a mutants exhibited a profound lack of movement, low electrical brain activity, low heart rate, decreased glucose and mitochondrial metabolism, and early fatality compared to controls. On the other hand, homozygous stxbp1b mutants had spontaneous electrographic seizures, and reduced locomotor activity response to a movement-inducing “dark-flash” visual stimulus, despite showing normal metabolism, heart rate, survival, and baseline locomotor activity. Our findings in these newly generated mutant lines of zebrafish suggest that zebrafish recapitulate clinical phenotypes associated with human syntaxin-binding protein 1 mutations.

Autism-epilepsy phenotype with macrocephaly suggests PTEN, but not GLIALCAM, genetic screening.

BackgroundWith a complex and extremely high clinical and genetic heterogeneity, autism spectrum disorders (ASD) are better dissected if one takes into account specific endophenotypes. Comorbidity of ASD with epilepsy (or paroxysmal EEG) has long been described and seems to have strong genetic background. Macrocephaly also represents a well-known endophenotype in subgroups of ASD individuals, which suggests pathogenic mechanisms accelerating brain growth in early development and predisposing to the disorder. We attempted to estimate the association of gene variants with neurodevelopmental disorders in patients with autism-epilepsy phenotype (AEP) and cranial overgrowth, analyzing two genes previously reported to be associated with autism and macrocephaly.MethodsWe analyzed the coding sequences and exon-intron boundaries of GLIALCAM, encoding an IgG-like cell adhesion protein, in 81 individuals with Autism Spectrum Disorders, either with or without comorbid epilepsy, paroxysmal EEG and/or macrocephaly, and the PTEN gene in the subsample with macrocephaly.ResultsAmong 81 individuals with ASD, 31 had concurrent macrocephaly. Head circumference, moreover, was over the 99.7th percentile (“extreme” macrocephaly) in 6/31 (19%) patients. Whilst we detected in GLIALCAM several single nucleotide variants without clear pathogenic effects, we found a novel PTEN heterozygous frameshift mutation in one case with “extreme” macrocephaly, autism, intellectual disability and seizures.ConclusionsWe did not find a clear association between GLIALCAM mutations and AEP-macrocephaly comorbidity. The identification of a novel frameshift variant of PTEN in a patient with “extreme” macrocephaly, autism, intellectual disability and seizures, confirms this gene as a major candidate in the ASD-macrocephaly endophenotype. The concurrence of epilepsy in the same patient also suggests that PTEN, and the downstream signaling pathway, might deserve to be investigated in autism-epilepsy comorbidity. Working on clinical endophenotypes might be of help to address genetic studies and establish actual causative correlations in autism-epilepsy.

Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates

OBJECTIVE The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. APPROACH Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. MAIN RESULTS The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. SIGNIFICANCE The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.