Michael F. Hammer

Objectives: Genotype–phenotype studies in rare epilepsies typically relate a pathogenic DNA sequence change to clinical outcome, without considering the broader molecular context of a given variant. Here we ask whether clinical heterogeneity in SCN8A-related disorders (SCN8A-RD) is patterned along molecular dimensions that go beyond the specific genetic alteration, to include position in the linear channel topology, proximity to post-translational modification (PTM) and structural sites, and predicted effects on mRNA splicing. Using the International SCN8A Registry, we map these features against seizure, developmental, and treatment-response phenotypes to identify the regions and molecular feature classes with which clinical variability is associated. Methods: We identify “hot-spots” across the coding sequence for seizure types and severity of developmental disability (DD). We then test for enrichment of clinical features near sites related to PTM and protein structure, identify variants that may alter mRNA splicing, and evaluate the interaction between these features and clinical subgroups. Results: We expand on previous work identifying regions of coding DNA sequence that are pathogenic or benign “hot-spots”. We show regional enrichment of numerous seizure types and severities of DD. Enrichment analysis of PTM and protein structural sites shows distinct enrichment profiles for developmental, seizure, and medication-response features. Finally, we identify 14 variants that are likely to alter RNA splicing. Significance: We provide a higher-resolution map of pathogenic variation across SCN8A and show that seizure types, developmental severity, and medication response are regionally organized along the coding sequence. Proximity to structural and glycosylation sites distinguishes phenotypic subgroups is associated with differential drug response, including a glycosylation–gabapentin relationship, and 14 missense variants are flagged as likely splice-altering, nominating targets for splice-directed therapy and providing candidates for experimental validation of dual pathogenic mechanisms. Key Points: Pathogenic variants associated with SCN8A-Related Disorders are regionally enriched and cluster in pathogenic or benign “hot spots” that may inform the classification of variants of uncertain significance. Molecular and structural landmarks in SCN8A associate with different phenotypic and therapeutic profiles, providing potential insight into developmental prognosis, treatment selection, and underlying seizure mechanism. Some pathogenic missense variants may result in aberrant RNA splicing, causing dual mechanisms of pathogenicity. Heterogeneity between patients with identical variants may be explained by differential splicing behavior.

Gain–of–function variants in SCN8A, encoding NaV1.6, cause developmental and epileptic encephalopathy with early-onset, drug-resistant seizures. SCN8A is a Goldilocks gene: both excess and deficit of NaV1.6 are deleterious, so therapies that lower NaV1.6 must operate within a narrow window. Here we characterize a congenic C3H/HeJ.C57BL/6J colony carrying Scn8a–N1768D and identify a tightly linked, cis–acting modifier (L) that converts a severe phenotype into graded outcomes. Among N1768D homozygotes on a 98.4% C3H/HeJ.C57BL/6J background, three phenotypes segregate: short–lived (SL) mice with early lethal tonic–clonic seizures (TCS), long–lived (LL) mice with delayed onset and reduced seizure burden, and hindlimb–paralysis (HP) mice with early death. Heterozygotes separate into D/+ SL mice, which develop spontaneous TCS and die of epilepsy, and D/+ LL mice, which lack spontaneous TCS and epilepsy–related death. Genetic analysis places a second locus (L) tightly linked to Scn8a and co–segregating with phenotype. Hippocampal RNA–seq shows dose-dependent Scn8a mRNA reduction: none in SL (D+/D+), 30% in LL (DL/D+), and 70% in HP (DL/DL). Long–read trio DNA sequencing identifies a de novo 2-bp frameshift in coding exon 15 of Scn8a, in cis with N1768D, that introduces a premature stop codon ~26 kb upstream of the exon—exon junction, predicted to trigger nonsense–mediated decay. Allele-specific read counting confirms selective, cis–acting loss of DL transcripts with preserved D+ output. Thus, a de novo, intragenic frameshift variant titrates NaV1.6 through a Goldilocks window in which intermediate reduction rescues Gain-of-function epilepsy, whereas insufficient or excessive reduction remains lethal, validating Scn8a–lowering and its therapeutic dose window.

Pathogenic variants in SCN8A, encoding the voltage-gated sodium channel NaV1.6, cause disease through opposing gain-of-function (GoF) and loss-of-function (LoF) mechanisms, yet the downstream cellular programs that distinguish these directions have not been resolved within a single genetic system. We used an isogenic Scn8a allelic series in which a cis-acting modifier produces a stepwise reduction of NaV1.6 on the N1768D background, isolating GoF, rescued, and LoF states on a shared background, and profiled the hippocampal transcriptome with pathway-level inference. The gain- and loss-of-function extremes engaged mechanistically opposite programs: an active fibro-inflammatory injury cascade in the seizing brain versus suppression of neuronal-signaling and growth programs, including cAMP Response Element-Binding signaling (CREB) in neurons, in the LoF state. The injury core was conserved across genetic backgrounds, appeared only after seizures rather than tracking channel dose, and was durably suppressed in the rescued genotype without erosion after seizure onset. Genetic rescue and the repurposed angiotensin-receptor blocker candesartan converged on the same injury program through distinct routes, and the same program was engaged in an α-synuclein model of Parkinson's disease and in human epileptic tissue. These findings show that the two clinical directions of SCN8A disease have distinct tissue-level correlates and call for opposite therapeutic logic, channel or injury-cascade suppression for GoF and restoration of channel output for LoF, and identify a conserved, seizure-driven injury core as a tractable cross-disease target.

Background: Sleep architecture fragmentation is common in Long COVID and dysautonomia, yet the relationship between block-level deep sleep consolidation and next-day functional wellbeing has not been characterized longitudinally in this population. We tested the hypothesis that block-level deep sleep architecture predicts next-day wellness better than aggregate stage duration. Methods: A prospective N-of-1 longitudinal study was conducted across 78 nights between January 1 and March 25, 2026 using a consumer wearable (Garmin Index Sleep Monitor). Next-day wellbeing was assessed using the Overall Feeling Score (OFS; 0–10 scale). A five-tier Architecture Group (AG) classification operationalized consolidation quality. A custom predictive model (Pendulum v5.2) quantified consolidation through block-level weighting and contextual penalties. Space weather metrics (Kp index, daily electron fluence, Load Score) were correlated with sleep architecture and OFS across same-day and lagged alignments. A secondary hierarchical regression examined illness as an independent covariate across the full dataset. Results: Architecture Group explained 56.7% of next-day OFS variance (r = 0.753, p < 0.001), compared with 17.0% for total deep sleep duration (r = 0.412) and 4.8% for the Garmin Sleep Score (r = 0.220). Pendulum v5.2 explained 43.0% of variance (r = 0.656). The deep sleep fragmentation phenotype occurred on 25.6% of nights despite adequate total deep sleep. Space weather explained 14.2% of OFS variance through architecture degradation; Load Score at 1-day lag was the strongest space weather predictor (r = −0.328, p = 0.004). A binary illness indicator explained an additional 5.7% of OFS variance beyond architecture and space weather (full-model R² = 55.2%), with a mean illness-night residual of −0.56 (SD = 0.36; 95% CI [−0.72, −0.40]), consistent with immune/viral flares reducing OFS through pathways independent of sleep architecture. Conclusions: Block-level deep sleep consolidation quality is a substantially stronger predictor of next-day wellbeing than total stage duration in Long COVID with dysautonomia. Space weather constitutes a measurable environmental modifier operating through architecture degradation. Immune and viral flares constitute a significant architecture-independent confound consistent with direct neuroinflammatory effects on functional capacity. This framework may be applicable across conditions where sleep architecture fragmentation plays a pathophysiological role. Author Summary: Sleep fragmentation is common in Long COVID and dysautonomia, but the relationship between block-level deep sleep consolidation and next-day functional outcome has not been characterized longitudinally. Consumer wearables can capture block-level architecture at ecological scale, yet commercial sleep scores show poor predictive validity in this population. This study demonstrates that deep sleep consolidation quality —not total stage duration—is the dominant predictor of next-day wellbeing in Long COVID with dysautonomia (Architecture Group r = 0.753, explaining 56.7% of variance). Space weather constitutes a measurable environmental modifier operating through architecture degradation via dissociable Kp and electron fluence pathways. The block-level framework and Pendulum Model may generalize to other conditions where ANS dysregulation impairs sleep stage consolidation.

Objectives: Despite providing significant insights into the phenotypic spectrum and genotype–phenotype correlations in SCN8A ‐related disorders (SCN8A‐RD), cohort and registry‐based studies typically lack sufficient longitudinal data to characterize disease progression or treatment‐response trajectories. Here we utilize data from electronic medical records (EMRs) of 80 patients as a complementary approach to advance understanding of the natural history of SCN8A‐RD. Methods: Longitudinal profiles were constructed by mining text from EMRs and writing code to visualize timelines for individual patients. Age‐dependent unsupervised clustering analysis was performed on developmental skills, seizure profiles, electroencephalography (EEG) results, comorbidities, hospitalizations, genetic variant, and sex. Skill acquisition data in four domains (gross motor, fine motor, language, and academic) were used to infer developmental trajectories for the overall cohort and phenotypic subgroups. Features that distinguished clusters at each age group were identified using penalized regression. Antiseizure medication (ASM) effectiveness was inferred by correlating prescriptions taken during periods of seizure reduction. Results: Cluster analysis identified dynamic phenotypic groupings peaking at four clusters at Year 5, suggesting that the middle childhood years may represent a critical developmental window when phenotypic divergence is most apparent. Developmental trajectories varied significantly by phenotypic subgroup: Patients in the loss‐of‐function (LoF) subgroup achieved highest gross motor and language skills, whereas patients with developmental and epileptic encephalopathy (DEE) remained the most impaired across all domains. Oxcarbazepine emerged as the most common ASM monotherapy during the seizure‐free periods. Significance: This EMR analysis represents the first integration of long‐term developmental trajectories that encompasses both gain‐of‐function (GoF) and LoF subgroups. Patients carrying both types of variants cluster together at different ages in early childhood. Several sodium channel blockers were effective as monotherapies during seizure‐freedom periods. Despite intermittent periods of reduced seizure activity, persistent developmental delays across all subgroups highlight the need for neurodevelopmental support beyond seizure control. EMR analysis reveals an evolving phenotypic landscape with distinct developmental outcomes for SCN8A‐RD patients in different subgroups.

Brain-expressed voltage-gated sodium (Nav) and potassium (Kv) channels are essential for maintaining the balance of neuronal excitability, each having opposing effects on membrane potential and neuronal firing. Genetic alterations in these channels can disrupt this balance, leading to epilepsy and/or developmental impairments through gain-of-function (GoF) or loss-of-function (LoF) mechanisms. This review catalogs 48 transgenic mouse models involving sodium channels (SCN1A, SCN2A, SCN3A, SCN8A) and potassium channels (KCNQ2, KCNQ3, KCNT1, KCNA1, KCNB1, KCND2), detailing the effects of genetic alterations in terms of channel function, affected cell types, and phenotypic manifestations. Mechanistic insights from these models reveal that initial channel dysfunction triggers cascading pathological processes including glutamate excitotoxicity, oxidative stress, gliosis, neuroinflammation, and blood–brain barrier disruption. Therapeutic approaches include antisense oligonucleotides to enhance functional allele expression or reduce pathogenic channel expression, viral-mediated gene therapy, gene editing, and small molecule modulators that target persistent sodium currents or that stabilize channel inactivation. The timing of intervention appears to be critical, with early treatment showing greater efficacy in preventing pathological cascades. Strain-specific background effects and compensatory ion channel expression affect phenotypic severity and treatment response, complicating translation of model results. Importantly, transgenic models offer opportunities to better understand mechanisms underlying comorbidities commonly suffered by patients, including behavioral disorders, motor impairments, and sleep disturbances. The integration of these findings suggests that effective treatment strategies may require combinations of channel-directed therapies and interventions targeting downstream pathological processes, particularly for established disease. This comprehensive examination of channelopathy models provides a framework for developing transformative therapeutics for genetic epilepsies.

Summary Paragraph: Variants in ion channel genes are common causes of pediatric epilepsy, often leading to intractable seizures, developmental delay and other comorbidities, which increases risk of death. Pathogenic variants in the SCN8A gene, which encodes a voltage-gated sodium channel critical for action potential generation in the brain, account for ∼1% of genetic epilepsies. The voltage sensor in SCN8A domain 1 is encoded by one of two developmentally-regulated mutually exclusive alternative exons, 5N and 5A. We observe that variants in these exons are more likely to cause infantile spasms, a severe seizure type, than variants elsewhere in SCN8A , and that some pathogenic variants affect exon 5 splicing, impacting patient phenotype. Molecular and evolutionary analyses implicate the exon sequences of these and other voltage-gated ion channel alternative exons in splicing regulation. We identified antisense oligonucleotides (ASOs) that shift splicing of SCN8A exon 5N to 5A or vice versa. These ASOs normalize neuronal activity in patient-derived iPSC neurons, and reduce seizures and motor impairment and extend lifespan in a new exon 5N mutant mouse model. Our results demonstrate that splice-switching ASOs can effectively reduce the expression of pathogenic isoforms and rescue both seizure and non-seizure phenotypes. Similar approaches should be applicable to pediatric genetic epilepsies caused by mutations in other ion channel alternative exons.

Objective: Despite rapid advances in understanding the disease spectrum and its progression, little is known about which antiseizure medications (ASMs) are likely to be beneficial or detrimental as first‐line therapies for patients with SCN8A‐related epilepsy (SCN8A‐RE). This is a critical issue given low rates of seizure freedom and treatment resistance rates that exceed 75%. In this study, we test the hypothesis that machine learning (ML) algorithms can improve selection of ASMs that are likely to benefit patients with SCN8A‐RE. Methods: We leverage comprehensive medical data in the International SCN8A Patient Registry to construct a neural network that recommends ASMs based on a caregiver‐centered composite measure incorporating improvements in seizure control, alertness, and side effects. We directly compare the recommendations of the algorithm to preferences of clinician experts through a follow‐up survey and evaluate how ASM selection is influenced when informed by the ML algorithm. Results: Despite challenges resulting from the prevalent use of polypharmacy and frequent suboptimal treatment responses, the algorithm identified ASMs likely to be beneficial in 76% ± 3% of cases while never recommending a detrimental ASM in 1100 trials. Clinician experts independently recommended beneficial ASMs in 22% (16/72) of cases, a rate that increased to 46% (11/24) when choices were given based on algorithm recommendations. Significance: The results indicate that ML algorithms can improve selection of ASMs that are likely to be beneficial in the early treatment of SCN8A‐RE patients, with little risk of recommending ASMs with detrimental effects—a particular hazard for patient populations requiring long‐term maintenance on polypharmacy. The results also expand the number of recommended ASMs from two sodium channel blockers (SCBs) identified in a recent consensus process to five SCBs and a γ‐aminobutyric acidergic drug. The algorithm lays the groundwork for incorporating composite measures that include both seizure control and quality of life metrics.

Background: Despite significant scientific progress since the 2012 discovery that variants in the SCN8A gene can cause human epilepsy, disease mechanisms and best practices for management of SCN8A-related disorders (SCN8A-RD) remain incompletely understood. To accelerate the rate of progress, the International SCN8A Alliance sponsored a conference in Boston, Massachusetts, on August 16–18, 2024. The goals were to identify core knowledge gaps and research priorities, and to establish a collaborative research strategy to improve quality of life. In addition to a number of family leaders representing caregiver priorities, the meeting included laboratory scientists, clinicians, and representatives from the biopharmaceutical industry. Main body: The scientific literature and requests for proposals from epilepsy funding agencies were reviewed prior to the meeting. Stakeholder-specific surveys were conducted focusing on knowledge gaps, research priorities, and scientific roadblocks. Interviews with biotechnology leaders were conducted to identify their priorities. These data were analyzed to assess responsiveness to caregiver concerns and to identify top research priorities for advancing the field. The Caregiver survey (n = 175) revealed top challenges and identified novel therapeutics and management of non-seizure phenotypes/comorbidities as top priorities. Clinician (n = 46) and scientist (n = 23) surveys identified a number of common research priorities, partially overlapping with caregiver concerns. Five core areas emerged from integrated analysis of all four stakeholder surveys and became the focus areas of five Working Groups: (1) Transformative Therapeutics, (2) Non-Seizure Outcomes, (3) Current Therapeutics, (4) Biomarkers, and (5) Whole Brain/Whole Body. Conclusions: Taking account of the concerns and priorities of the caregiver community, the five working groups identified research directions to address knowledge gaps that include both short- and long-term priorities to improve understanding of disease mechanisms and management for the spectrum of SCN8A-RD phenotypes. Challenges included identification of suitable funding mechanisms and the lack of expertise in certain methodologies and research areas. This Research Roadmap is expected to accelerate progress toward the goals of improved quality of life and transformative care for all those with SCN8A-RD.

Background/Objectives: For patients with medically refractory temporal lobe epilepsy (TLE), surgery is an effective strategy. However, post-operative seizure recurrence occurs in 20–30% of patients, and it remains challenging to predict outcomes solely based on clinical variables. Here, we ask to what extent differences in gene expression in epileptic tissue can predict the outcome after resective epilepsy surgery. Methods: We performed RNAseq on hippocampal tissue resected from eight patients who underwent anterior temporal lobectomy with amygalohippocampectomy (ATL/AH), half of whom became seizure free (SF) or non-seizure free (NSF). Results: Bioinformatic analyses revealed 1548 differentially expressed genes and statistical enrichment analyses identified a distinct set of pathways in NSF and SF cohorts that were associated with neuroinflammation, neurotransmission, synaptic plasticity, and extracellular matrix (ECM) reorganization. Resected tissue exhibiting strong pro-inflammatory processes are associated with better post-surgery seizure outcomes than patients exhibiting cellular signaling processes related to ECM reorganization, autoantibody production, and neural circuit formation. Conclusions: The results suggest that post-operative targeting of both inhibitory aspects of the ECM remodeling and the autoimmune/inflammatory components may be helpful in promoting repair and preventing the recurrence of seizures.