Conservation of alternative splicing in sodium channels reveals evolutionary focus on release from inactivation and structural insights into gating

Voltage-gated sodium channels are critical for neuronal activity, and highly intolerant to variation. Even mutations that cause subtle changes in the activity these channels are sufficient to cause devastating inherited neurological diseases, such as epilepsy and pain. However, these channels do vary in healthy tissue. Alternative splicing modifies sodium channels, but the functional relevance and adaptive significance of this splicing remain poorly understood. Here we use a conserved alternate exon encoding part of the first domain of sodium channels to compare how splicing modifies different channels, and to ask whether the functional consequences of this splicing have been preserved in different genes. Although the splicing event is highly conserved, one splice variant has been selectively removed from Nav1.1 in multiple mammalian species, suggesting that the functional variation in Nav1.1 is less well-tolerated. We show for three human channels (Nav1.1, Nav1.2 and Nav1.7) splicing modifies the return from inactivated to deactivated states, and the differences between splice variants are occluded by antiepileptic drugs that bind to and stabilize inactivated states. A model based on structural data can replicate these changes, and indicates that splicing may exploit a distinct role of the first domain to change channel availability, and that the first domain of all three sodium channels plays a role in determining the rate at which the inactivation domain dissociates. Taken together, our data suggest that the stability of inactivated states is under tight evolutionary control, but that in Nav1.1 faster recovery from inactivation is associated with negative selection in mammals

Alternative splicing in sodium channels: biophysical and functional effects in NaV1.1, NaV1.2 & NaV1.7

Alternative splicing in voltage-gated sodium channels can affect pathophysiological conditions, including epilepsy and pain. A conserved alternative splicing event in sodium channel genes, including SCN1A, SCN2A and SCN9A, gives rise to the neonatal (5N) and adult (5A) isoforms. Differences in the ratio of 5A/5N in Nav1.1 (encoded by SCN1A) in patients may lead to different predisposition to epilepsy or response to antiepileptic drugs (AED). Previous HEK293T whole-cell voltage-clamp recordings showed that Nav1.1-5N channels recover more quickly from fast inactivation than 5A. However it was unknown whether this effect is conserved in Nav1.2 (encoded by SCN2A) and Nav1.7 (SCN9A) channels, or what the functional consequences of this splicing event are for neurons. This project used whole-cell voltage-clamp recordings on heterologously expressed neonatal and adult channels to compare the biophysical properties of the splice isoforms for all three channel types and their modulation by AEDs. It also used current-clamp and dynamic-clamp recordings on transfected hippocampal cultured neurons to assess the effect of splicing on neuronal properties during epileptiform activity. Biophysical analysis in HEK293T cells revealed that splicing profoundly regulates fast inactivation and channel availability during fast, repetitive stimulation, with neonatal channels showing higher availability compared to adult channels and this difference was conserved among Nav1.1, Nav1.2 and Nav1.7. The change in inactivation imposed by splicing can be modeled as a modification of the stability of the inactivation statein resting channels. This change can be eradicated by administration of the AEDs phenytoin and carbamazepine. Current-clamp recordings in transfected neurons showed that the alternatively spliced variantmodifies the rising phase of action potentials for Nav1.1 and Nav1.2 at high firing frequencies, implying a consistent splice-dependent modulation of channel availability. For Nav1.1 in interneurons, this translated to higher firing frequency for the neonatal isoform, which also conferred a higher maximal firing rate during epileptiform events imposed under dynamic-clamp recordings

Novel mutations in human and mouse SCN4A implicate AMPK in myotonia and periodic paralysis

Mutations in the skeletal muscle channel (SCN4A), encoding the Nav1.4 voltage-gated sodium channel, are causative of a variety of muscle channelopathies, including non-dystrophic myotonias and periodic paralysis. The effects of many of these mutations on channel function have been characterized both in vitro and in vivo. However, little is known about the consequences of SCN4A mutations downstream from their impact on the electrophysiology of the Nav1.4 channel. Here we report the discovery of a novel SCN4A mutation (c.1762A>G; p.I588V) in a patient with myotonia and periodic paralysis, located within the S1 segment of the second domain of the Nav1.4 channel. Using N-ethyl-N-nitrosourea mutagenesis, we generated and characterized a mouse model (named draggen), carrying the equivalent point mutation (c.1744A>G; p.I582V) to that found in the patient with periodic paralysis and myotonia. Draggen mice have myotonia and suffer from intermittent hind-limb immobility attacks. In-depth characterization of draggen mice uncovered novel systemic metabolic abnormalities in Scn4a mouse models and provided novel insights into disease mechanisms. We discovered metabolic alterations leading to lean mice, as well as abnormal AMP-activated protein kinase activation, which were associated with the immobility attacks and may provide a novel potential therapeutic target

An Empirical Comparison of Information-Theoretic Criteria in Estimating the Number of Independent Components of fMRI Data

BACKGROUND: Independent Component Analysis (ICA) has been widely applied to the analysis of fMRI data. Accurate estimation of the number of independent components of fMRI data is critical to reduce over/under fitting. Although various methods based on Information Theoretic Criteria (ITC) have been used to estimate the intrinsic dimension of fMRI data, the relative performance of different ITC in the context of the ICA model hasn't been fully investigated, especially considering the properties of fMRI data. The present study explores and evaluates the performance of various ITC for the fMRI data with varied white noise levels, colored noise levels, temporal data sizes and spatial smoothness degrees. METHODOLOGY: Both simulated data and real fMRI data with varied Gaussian white noise levels, first-order auto-regressive (AR(1)) noise levels, temporal data sizes and spatial smoothness degrees were carried out to deeply explore and evaluate the performance of different traditional ITC. PRINCIPAL FINDINGS: Results indicate that the performance of ITCs depends on the noise level, temporal data size and spatial smoothness of fMRI data. 1) High white noise levels may lead to underestimation of all criteria and MDL/BIC has the severest underestimation at the higher Gaussian white noise level. 2) Colored noise may result in overestimation that can be intensified by the increase of AR(1) coefficient rather than the SD of AR(1) noise and MDL/BIC shows the least overestimation. 3) Larger temporal data size will be better for estimation for the model of white noise but tends to cause severer overestimation for the model of AR(1) noise. 4) Spatial smoothing will result in overestimation in both noise models. CONCLUSIONS: 1) None of ITC is perfect for all fMRI data due to its complicated noise structure. 2) If there is only white noise in data, AIC is preferred when the noise level is high and otherwise, Laplace approximation is a better choice. 3) When colored noise exists in data, MDL/BIC outperforms the other criteria

Real-time brain computer interface using imaginary movements

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Numerical Stability Issues Of The Conventional Recursive Least Squares Algorithm

The continuous use of adaptive algorithms is strongly dependent on their behavior in finite-precision environments. We study the nonlinear round-off error accumulation system of the conventional RLS algorithm and we derive bounds for the relative precision of the computations and the accumulated round-off error, which guarantee the numerical stability of the finite-precision implementation of the algorithm. The bounds depend on the conditioning of the problem and the exponential forgetting factor. Simulations agree with our theoretical results. 1. INTRODUCTION A very important "real-life" problem, inherent in the continuous use of adaptive algorithms, is their behavior in finite precision environments. This problem contains the following subproblems: round-off error generation, round-off error propagation, and round-off error accumulation. For the conventional RLS algorithms the round-off error propagation is the best studied of the three aforementioned subproblems [1], [2]. Such stud..

Performance Assessments Of Fir Versus Iir Models In Acoustic Echo Cancellation

The adequateness of IIR models for acoustic echo cancellation is a long standing question and the answers found in the literature are conflicting. We use results from rational Hankel norm and least-squares approximation and we recall a test which provides a priori performance levels for FIR and IIR models. We apply this test to measured acoustic impulse responses. Upon comparing the performance levels of equal complexity FIR and IIR models, we do not observe any significant gain from the use of IIR models. We attribute this phenomenon to the shape of the energy spectra of the acoustic impulse responses, so tested, which possess many strong and sharp peaks. Faithful modelling of these peaks requires many parameters irrespective of the type of the model. 1. INTRODUCTION The use of adaptive FIR filters for Acoustic Echo Cancellation leads to filters with very high orders and consequently the adaptive adjustment of their coefficients leads to very high computational complexity [1]. Ther..