Back

Cis-Attenuation of Pathogenic Scn8a Variant Causing Childhood Epilepsy Reveals Opposing Transcriptional Programs Driving NaV1.6 Gain and Loss of Function Phenotypes

Hammer, M. F.; Bahramnejad, E.

2026-09-10 genetics
10.64898/2026.09.08.750173 bioRxiv
Show abstract

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 -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.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.