Absence epilepsy syndromes are part of the genetic generalized epilepsies, the pathogenesis of which remains poorly understood, although a polygenic architecture is presumed. Current focus on single molecule or gene identification to elucidate epileptogenic drivers is unable to fully capture the complex dysfunctional interactions occurring at a genetic/proteomic/metabolomic level. Here, we employ a multi-omic, network-based approach to characterize the molecular signature associated with absence epilepsy-like phenotype seen in a well validated rat model of genetic generalized epilepsy with absence seizures. Electroencephalographic and behavioral data was collected from Genetic Absence Epilepsy Rats from Strasbourg (GAERS, n = 6) and non-epileptic controls (NEC, n = 6), followed by proteomic and metabolomic profiling of the cortical and thalamic tissue of rats from both groups. The general framework of weighted correlation network analysis (WGCNA) was used to identify groups of highly correlated proteins and metabolites, which were then functionally annotated through joint pathway enrichment analysis. In both brain regions a large protein-metabolite module was found to be highly associated with the GAERS strain, absence seizures and associated anxiety and depressive-like phenotype. Quantitative pathway analysis indicated enrichment in oxidative pathways and a downregulation of the lysine degradation pathway in both brain regions. GSTM1 and ALDH2 were identified as central regulatory hubs of the seizure-associated module in the somatosensory cortex and thalamus, respectively. These enzymes are involved in lysine degradation and play important roles in maintaining oxidative balance. We conclude that the dysregulated pathways identified in the seizure-associated module may be involved in the aetiology and maintenance of absence seizure activity. This dysregulated activity could potentially be modulated by targeting one or both central regulatory hubs.

The challenges in making animal models of complex human epilepsy phenotypes with varied aetiology highlights the need to develop alternative disease models that can address the limitations of animal models by effectively recapitulating human pathophysiology. The advances in stem cell technology provide an opportunity to use human iPSCs to make disease-in-a-dish models. The focus of this review is to report the current information and progress in the generation of epileptic patient-specific iPSCs lines, isogenic control cell lines, and neuronal models. These in vitro models can be used to study the underlying pathological mechanisms of epilepsies, anti-seizure medication resistance, and can also be used for drug testing and drug screening with their isogenic control cell lines.

Objective: Stevens–Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are severe cutaneous adverse drug reactions. Antiseizure medications (ASMs) with aromatic ring structure, including carbamazepine, are among the most common culprits. Screening for human leukocyte antigen (HLA) allele HLA‐B*15:02 is recommended prior to initiating treatment with carbamazepine in Asians, but this allele has low positive predictive value.Methods: We performed whole genome sequencing and analyzed 6 199 696 common variants among 113 aromatic ASM‐induced SJS/TEN cases and 84 tolerant controls of Han Chinese ethnicity.Results: In the primary analysis, nine variants reached genome‐wide significance (p < 5e‐08), one in the carbamazepine subanalysis (85 cases vs. 77 controls) and a further eight identified in HLA‐B*15:02‐negative subanalysis (35 cases and 53 controls). Interaction analysis between each novel variant from the primary analysis found that five increased risk irrespective of HLA‐B*15:02 status or zygosity. HLA‐B*15:02‐positive individuals were found to have reduced risk if they also carried a chromosome 12 variant, chr12.9426934 (heterozygotes: relative risk = .71, p = .001; homozygotes: relative risk = .23, p < .001). All significant variants lie within intronic or intergenic regions with poorly understood functional consequence. In silico functional analysis of suggestive variants (p < 5e‐6) identified through the primary and subanalyses (stratified by HLA‐B*15:02 status and drug exposure) suggests that genetic variation within regulatory DNA may contribute to risk indirectly by disrupting the regulation of pathology‐related genes. The genes implicated were specific either to the primary analysis (CD9), HLA‐B*15:02 carriers (DOCK10), noncarriers (ABCA1), carbamazepine exposure (HLA‐E), or phenytoin exposure (CD24).Significance: We identified variants that could explain why some carriers of HLA‐B*15:02 tolerate treatment, and why some noncarriers develop ASM‐induced SJS/TEN. Additionally, this analysis suggests that the mixing of HLA‐B*15:02 carrier status in previous studies might have masked variants contributing to susceptibility, and that inheritance of risk for ASM‐induced SJS/TEN is complex, likely involving multiple risk variants.

Background: There is increased prevalence of epilepsy in patients with Alzheimer’s disease (AD). Although shared pathological and clinical features have been identified, the underlying pathophysiology and cause-effect relationships are poorly understood. We aimed to identify commonly dysregulated groups of genes between these two disorders.Methods: Using publicly available transcriptomic data from hippocampal tissue of patients with temporal lobe epilepsy (TLE), late onset AD and non-AD controls, we constructed gene coexpression networks representing all three states. We then employed network preservation statistics to compare the density and connectivity-based preservation of functional gene modules between TLE, AD and controls and used the difference in significance scores as a surrogate quantifier of module preservation.Results: The majority (>90%) of functional gene modules were highly preserved between all coexpression networks, however several modules identified in the TLE network showed various degrees of preservation in the AD network compared to that of control. Of note, two synaptic signalling-associated modules and two metabolic modules showed substantial gain of preservation, while myelination and immune system-associated modules showed significant loss of preservation. The genes SCN3B and EPHA4 were identified as central regulatory hubs of the highly preserved synaptic signalling-associated module. GABRB3 and SCN2A were identified as central regulatory hubs of a smaller neurogenesis-associated module, which was enriched for multiple epileptic activity and seizure-related human phenotype ontologies.Conclusion: We conclude that these hubs and their downstream signalling pathways are common modulators of synaptic activity in the setting of AD and TLE, and may play a critical role in epileptogenesis in AD.

Epilepsy is a common neurologic disorder. Although antiseizure medications (ASMs) are the first-line treatment, identifying the most effective ASM for each individual remains a trial-and-error process. Genetic variation may influence treatment response. We aimed to develop and validate a multimodal deep learning model that integrates clinical and genomic features to predict response to the initial ASM in people with newly diagnosed epilepsy. We used data from individuals with newly diagnosed epilepsy in Australia as the development cohort and participants from the Human Epilepsy Project 1 (recruited in the United States, Europe, and Australia) as the external validation cohort. All participants initiated ASM treatment and were followed prospectively for at least 1 year. We included 16 clinical factors and constructed 4 genomic feature types related to epilepsy and ASM pharmacogenomics, with and without functional impact annotations. We evaluated various machine learning architectures and multimodal fusion strategies to predict seizure freedom while taking the initial ASM at 1 year. In the development cohort (n = 286, median age 39 years, 47.2% seizure free), combining clinical and genomic features in our proposed multimodal deep learning model improved predictive performance. The highest area under the receiver operating characteristic curve (AUC) of 0.74 (95% CI 0.70-0.78) was achieved using clinical factors and genomic variants affecting transcription factor binding, significantly outperforming the clinical-only model (AUC 0.67, 95% CI 0.62-0.72; p < 0.05). In the external validation cohort (n = 219, median age 31 years, 20.5% seizure free), the same feature combination achieved an AUC of 0.69 (95% CI 0.67-0.71), higher than the clinical-only model (AUC 0.62, 95% CI 0.60-0.64; p < 0.05). Applying this model to the development cohort, if all participants took the highest ranked ASMs, the mean predicted seizure-free probability would be 68.05% (95% CI 65.79%-70.35%) compared with the observed seizure-free rate of 47.2% (95% CI 41.3%-53.2%). Integrating genomic data with clinical features enhances the ability of deep learning models in predicting ASM response in newly diagnosed epilepsy. This approach may support personalized treatment selection and improve clinical outcomes.