Molecular aspects of Dravet syndrome patients in Taiwan

Genetics and clinical correlation of Dravet syndrome and its mimics - experience of a tertiary center in Taiwan

BACKGROUND

Dravet syndrome is a severe developmental and epileptic encephalopathy characterized by the onset of prolonged febrile and afebrile seizures in infancy and SCN1A gene mutations. In some cases, non-SCN1A gene mutations can present with a phenotype very similar to that of Dravet syndrome. The aim of this study was to compare phenotypes of patients with SCN1A and non-SCN1A gene mutation-related Dravet syndrome.

METHODS

Thirty-six patients with Dravet syndrome-like phenotypes were followed from July 2017 to December 2019. We retrospectively analyzed their clinical profiles and genetic surveys.

RESULTS

Of the 36 enrolled patients, 15 (41.7%) had SCN1A mutations, one (2.8%) had an SCN8A mutation, one (2.8%) had an STX1B mutation, and five females (13.9%) had PCDH 19 mutations. The median age at first seizure onset was 7 months in those with SCN1A mutations, 1.3 years in those with PCDH19 mutations, and 10 months for the remaining patients. The majority of the patients with SCN1A mutations had status epilepticus (80% vs. 20%) and fever-sensitive seizures (76% vs. 31%) compared to those with PCDH19 mutations. The patients with SCN1A-related seizures had a higher rate of focal seizures as first seizure type than those without SCN1A mutations. Three of five (60%) patients with PCDH19 mutations had brain magnetic resonance imaging abnormalities. The three most commonly used antiseizure medications were sodium valproate, levetiracetam, and clobazam. Seven of the 15 patients with SCN1A mutations used stiripentol. The median time from seizure onset to genetic diagnosis was 6.6 years (range 4 months-22.3 years).

CONCLUSION

The patients with SCN1A mutations in this study had high rates of fever-sensitive seizures, status epilepticus, seizure onset with focal seizure type, and relatively young age at seizure onset. The patients with PCDH19 mutations had a relatively high rate of abnormal brain magnetic resonance imaging findings.

A Wide Spectrum of Genetic Disorders Causing Severe Childhood Epilepsy in Taiwan: A Case Series of Ultrarare Genetic Cause and Novel Mutation Analysis in a Pilot Study

BACKGROUND

Pediatric epileptic encephalopathy and severe neurological disorders comprise a group of heterogenous diseases. We used whole-exome sequencing (WES) to identify genetic defects in pediatric patients.

METHODS

Patients with refractory seizures using ≥2 antiepileptic drugs (AEDs) receiving one AED and having neurodevelopmental regression or having severe neurological or neuromuscular disorders with unidentified causes were enrolled, of which 54 patients fulfilled the inclusion criteria, were enrolled, and underwent WES.

RESULTS

Genetic diagnoses were confirmed in 24 patients. In the seizure group, KCNQ2, SCN1A, TBCID 24, GRIN1, IRF2BPL, MECP2, OSGEP, PACS1, PIGA, PPP1CB, SMARCA4, SUOX, SZT2, UBE3A, 16p13.11 microdeletion, [4p16.3p16.1(68,345-7,739,782)X1, 17q25.1q25.3(73,608,322-81,041,938)X3], and LAMA2 were identified. In the nonseizure group, SCN2A, SPTBN2, DMD, and FBN1 were identified. Ten novel mutations were identified. The recurrent genes included SCN1A, KCNQ2, and TBCID24. Male pediatric patients had a significantly higher (57% vs. 29%; p < 0.05, odds ratio = 3.18) yield than their female counterparts. Seventeen genes were identified from the seizure groups, of which 82% were rare genetic etiologies for childhood seizure and did not appear recurrently in the case series.

CONCLUSIONS

Wide genetic variation was identified for severe childhood seizures by WES. WES had a high yield, particularly in male infantile patients.

A propositional AI system for supporting epilepsy diagnosis based on the 2017 epilepsy classification: Illustrated by Dravet syndrome

PURPOSE

The 2017 epilepsy and seizure diagnosis framework emphasizes epilepsy syndromes and the etiology-based approach. We developed a propositional artificial intelligence (AI) system based on the above concepts to support physicians in the diagnosis of epilepsy.

METHODS

We analyzed and built ontology knowledge for the classification of seizure patterns, epilepsy, epilepsy syndrome, and etiologies. Protégé ontology tool was applied in this study. In order to enable the system to be close to the inferential thinking of clinical experts, we classified and constructed knowledge of other epilepsy-related knowledge, including comorbidities, epilepsy imitators, epilepsy descriptors, characteristic electroencephalography (EEG) findings, treatments, etc. We used the Ontology Web Language with Description Logic (OWL-DL) and Semantic Web Rule Language (SWRL) to design rules for expressing the relationship between these ontologies.

RESULTS

Dravet syndrome was taken as an illustration for epilepsy syndromes implementation. We designed an interface for the physician to enter the various characteristics of the patients. Clinical data of an 18-year-old boy with epilepsy was applied to the AI system. Through SWRL and reasoning engine Drool's execution, we successfully demonstrate the process of differential diagnosis.

CONCLUSION

We developed a propositional AI system by using the OWL-DL/SWRL approach to deal with the complexity of current epilepsy diagnosis. The experience of this system, centered on the clinical epilepsy syndromes, paves a path to construct an AI system for further complicated epilepsy diagnosis.