Neuronal mechanisms of mutations in SCN8A causing epilepsy or intellectual disability.

Ion channel mutations can cause distinct neuropsychiatric diseases. We first studied the biophysical and neurophysiological consequences of four mutations in the human Na+ channel gene SCN8A causing either mild (E1483K) or severe epilepsy (R1872W), or intellectual disability and autism without epilepsy (R1620L, A1622D). Only combined electrophysiological recordings of transfected wild-type or mutant channels in both neuroblastoma cells and primary cultured neurons revealed clear genotype-phenotype correlations. The E1483K mutation causing mild epilepsy showed no significant biophysical changes, whereas the R1872W mutation causing severe epilepsy induced clear gain-of-function biophysical changes in neuroblastoma cells. However, both mutations increased neuronal firing in primary neuronal cultures. In contrast, the R1620L mutation associated with intellectual disability and autism-but not epilepsy-reduced Na+ current density in neuroblastoma cells and expectedly decreased neuronal firing. Interestingly, for the fourth mutation, A1622D, causing severe intellectual disability and autism without epilepsy, we observed a dramatic slowing of fast inactivation in neuroblastoma cells, which induced a depolarization block in neurons with a reduction of neuronal firing. This latter finding was corroborated by computational modelling. In a second series of experiments, we recorded three more mutations (G1475R, M1760I, G964R, causing intermediate or severe epilepsy, or intellectual disability without epilepsy, respectively) that revealed similar results confirming clear genotype-phenotype relationships. We found intermediate or severe gain-of-function biophysical changes and increases in neuronal firing for the two epilepsy-causing mutations and decreased firing for the loss-of-function mutation causing intellectual disability. We conclude that studies in neurons are crucial to understand disease mechanisms, which here indicate that increased or decreased neuronal firing is responsible for distinct clinical phenotypes.

A single mechanism driving both inactivation and adaptation in rapidly adapting currents of DRG neurons?

Rapidly adapting (RA) currents expressed in dorsal root ganglia somatosensory neurons reduce their amplitude in response to prolonged and/or repeated mechanical stimulation. Both inactivation of mechanotransducer channels and adaptation of the force acting on the channels have been suggested to independently decrease RA currents. However, these two mechanisms have similar kinetics and dependence on calcium and voltage. These experimental findings suggest that a single mechanism might underlie both. We constructed a simple Hodgkin-Huxley-type model with a single gating variable driving both inactivation and adaptation to test this hypothesis. Predictions of the model successfully describe key features of mechanical activation as well as inactivation, adaptation, and recovery. The model thus supports the possibility of a single mechanism driving inactivation and adaptation in RA currents. On its own, the model can be integrated into higher-order models of touch receptors because of its accurate simulation of RA currents.

How noisy adaptation of neurons shapes interspike interval histograms and correlations.

Channel noise is the dominant intrinsic noise source of neurons causing variability in the timing of action potentials and interspike intervals (ISI). Slow adaptation currents are observed in many cells and strongly shape response properties of neurons. These currents are mediated by finite populations of ionic channels and may thus carry a substantial noise component. Here we study the effect of such adaptation noise on the ISI statistics of an integrate-and-fire model neuron by means of analytical techniques and extensive numerical simulations. We contrast this stochastic adaptation with the commonly studied case of a fast fluctuating current noise and a deterministic adaptation current (corresponding to an infinite population of adaptation channels). We derive analytical approximations for the ISI density and ISI serial correlation coefficient for both cases. For fast fluctuations and deterministic adaptation, the ISI density is well approximated by an inverse Gaussian (IG) and the ISI correlations are negative. In marked contrast, for stochastic adaptation, the density is more peaked and has a heavier tail than an IG density and the serial correlations are positive. A numerical study of the mixed case where both fast fluctuations and adaptation channel noise are present reveals a smooth transition between the analytically tractable limiting cases. Our conclusions are furthermore supported by numerical simulations of a biophysically more realistic Hodgkin-Huxley type model. Our results could be used to infer the dominant source of noise in neurons from their ISI statistics.