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SCN1A (NaV1.1 sodium channel) mutations cause Dravet syndrome (DS) and GEFS+ (which is in general milder), and are risk factors in other epilepsies. Phenotypic variability limits precision medicine in epilepsy, and it is important to identify factors that set phenotype severity and their mechanisms. It is not yet clear whether SCN1A mutations are necessary for the development of severe phenotypes or just for promoting seizures. A relevant example is the pleiotropic R1648H mutation that can cause either mild GEFS+ or severe DS.We used a R1648H knock-in mouse model (Scn1aRH/+) with mild/asymptomatic phenotype to dissociate the effects of seizures and of the mutation per se. The induction of short repeated seizures, at the age of disease onset for Scn1a mouse models (P21), had no effect in WT mice, but transformed the mild/asymptomatic phenotype of Scn1aRH/+ mice into a severe DS-like phenotype, including frequent spontaneous seizures and cognitive/behavioral deficits. In these mice, we found no major modifications in cytoarchitecture or neuronal death, but increased excitability of hippocampal granule cells, consistent with a pathological remodeling.Therefore, we demonstrate for our model that an SCN1A mutation is a prerequisite for a long term deleterious effect of seizures on the brain, indicating a clear interaction between seizures and the mutation for the development of a severe phenotype generated by pathological remodeling. Applied to humans, this result suggests that genetic alterations, even if mild per se, may increase the risk of second hits to develop severe phenotypes.

Age-dependent accumulation of amyloid-β, provoking increasing brain amyloidopathy, triggers abnormal patterns of neuron activity and circuit synchronization in Alzheimer’s disease (AD) as observed in human AD patients and AD mouse models. Recent studies on AD mouse models, mimicking this age-dependent amyloidopathy, identified alterations in CA1 neuron excitability. However, these models generally also overexpress mutated amyloid precursor protein (APP) and presenilin 1 (PS1) and there is a lack of a clear correlation of neuronal excitability alterations with progressive amyloidopathy. The active development of computational models of AD points out the need of collecting such experimental data to build a reliable disease model exhibiting AD-like disease progression. We therefore used the feature extraction tool of the Human Brain Project (HBP) Brain Simulation Platform to systematically analyze the excitability profile of CA1 pyramidal neuron in the APPPS1 mouse model. We identified specific features of neuron excitability that best correlate either with over-expression of mutated APP and PS1 or increasing Aβ amyloidopathy. Notably, we report strong alterations in membrane time constant and action potential width and weak alterations in firing behavior. Also, using a CA1 pyramidal neuron model, we evidence amyloidopathy-dependent alterations in Ih. Finally, cluster analysis of these recordings showed that we could reliably assign a trace to its correct group, opening the door to a more refined, less variable analysis of AD-affected neurons. This inter-disciplinary analysis, bringing together experimentalists and modelers, helps to further unravel the neuronal mechanisms most affected by AD and to build a biologically plausible computational model of the AD brain.

Summary: There is a growing consensus that Alzheimer's disease (AD) involves failure of the homeostatic machinery, which underlies the firing stability of neural circuits. What are the culprits leading to neuron firing instability? The amyloid precursor protein (APP) is central to AD pathogenesis, and we recently showed that its intracellular domain (AICD) could modify synaptic signal integration. We now hypothesize that AICD modifies neuron firing activity, thus contributing to the disruption of memory processes. Using cellular, electrophysiological, and behavioral techniques, we show that pathological AICD levels weaken CA1 neuron firing activity through a gene-transcription-dependent mechanism. Furthermore, increased AICD production in hippocampal neurons modifies oscillatory activity, specifically in the γ-frequency range, and disrupts spatial memory task. Collectively, our data suggest that AICD pathological levels, observed in AD mouse models and in human patients, might contribute to progressive neuron homeostatic failure, driving the shift from normal aging to AD. : Pousinha et al. report that increased AICD levels, a peptide representing amyloid precursor protein (APP) C-terminal domain, render CA1 pyramidal neurons hypoexcitable by altering Ca+ and K+ channel conductances. Modeling this alteration predicts decreased gamma oscillations. They show that increasing AICD levels in vivo weakens gamma-oscillation-dependent spatial memory. Keywords: AICD, APP, Alzheimer’s disease, neuron firing, brain oscillations, memory encoding