Structurally, therefore, this pathway would appear well situated to monitor and to switch between cortical inputs to the striatum based on changes in well-predicted

external contingencies (Kimura et al., 2004). Indeed, the recent suggestion that striatal CINs may form a recurrent inhibitory network anticipates CT99021 in vitro context- or state-specific plasticity of this kind, with each CIN potentially modulating a distinct region of corticostriatal plasticity under the control of the thalamostriatal pathway (Sullivan et al., 2008). At a formal level, contextual or state cues of this kind have emerged as a critical component of computational models of goal-directed action derived from model-based reinforcement learning (Daw et al., 2005). Such cues are argued to exert conditional control over actions and to produce a state prediction error when changes in such control occur. Model-based reinforcement learning uses experienced state-action-state transitions to build a model of the environment by generating state prediction errors produced by any discrepancy induced by a state transition based on the current estimates of state-action-state transition probabilities ( Gläscher et al., 2010). The notion of state prediction

errors is in contrast with that of reward prediction errors derived from temporal difference Epigenetic phosphorylation models of learning ( Sutton and Barto, 1998) that have been shown to reliably correlate with the phasic action of midbrain dopamine neurons ( Schultz and Dickinson, 2000). However, reward prediction error is negligible, particularly in Bay 11-7085 the reversal experiments in the current series; the

animal is expecting one outcome and receives another, which creates an error signal but one that is unrelated to rewarding prediction per se (the amount of reward earned is unchanged). This kind of signal is consistent with recent suggestions that CINs may participate in a form of prediction error signal in the DMS during reversal of previously learned contingencies ( Apicella et al., 2011). Indeed, similar studies assessing prediction errors in, what are at least nominally, instrumental conditioning tasks have found that TANs (putative CINs) preferentially encode prediction errors to situational events rather than reward ( Apicella et al., 2011; Stalnaker et al., 2012). Taken together, the effect of impaired pDMS CIN function on contingency degradation and the learning of new, but not initial, action-outcome contingencies is consistent with a deficit in computing reductions in state prediction errors that lead to reductions in contingency knowledge (see Supplemental Information). Whatever the role of CINs in conditional control, the current data suggest that the thalamostriatal pathway and its influence on CINs are critical for encoding changes in the instrumental contingency. Although this pathway does not appear to play any direct role in encoding action-outcome associations (this paper) or in striatal LTP (Bonsi et al.

While only ∼10%–30% of the mobile BACE-1 vesicles colocalized VX-770 in vivo with Golgi markers (see below), surprisingly, the vast majority of BACE-1 was conveyed in vesicles that are known markers of neuronal recycling endosomes (Figures 2A–2C). Specifically, we simultaneously

visualized transport of BACE-1:GFP and TfR:mCherry—previously used as a marker for neuronal recycling endosomes (Park et al., 2006 and Wang et al., 2008). The TfR fusion construct faithfully represents a functional recycling pool, as shown in Figure S2. Indeed, the vast majority of the trafficking BACE-1 vesicles colocalized with TfR (Figures 2A–2C) and also syntaxin-13 (Figure 2B, middle)—known markers of dendritic recycling endosomes (Park et al., 2006, Prekeris et al., 1999, Silverman et al., 2001, Wang et al., 2008 and Yap and Winckler, 2012). In contrast, few mobile BACE-1 vesicles colocalized with Rab5, a marker for early endosomes (Figure 2B, bottom). However, unlike mobile vesicles, stationary BACE-1 cargoes colocalized with all tested markers (TfR, syntaxin 13, and Rab5; Figure 2B). The significance of this is unclear, but such stationary particles are commonly seen when imaging vesicle transport in axons SKI-606 concentration (for example, see Tang et al., 2012) and may represent sites where potential intermingling of biosynthetic and recycling organelles occur. Next, we asked whether

APP colocalized not with known markers of the

neuronal biosynthetic pathway. Toward this, we cotransfected neurons with APP:mCherry and the signal sequence of neuropeptide-Y (NPYss) fused to GFP—the latter expected to label the interior of Golgi-derived vesicles (El Meskini et al., 2001 and Kaech et al., 2012). Indeed, the vast majority of APP vesicles colocalized with NPYss (Figure 2D, middle), while there was only ∼20% colocalization of moving BACE-1 particles with NPYss (Figure 2D, right). Notably, <30% of mobile APP vesicles colocalized with TfR (27.92% ± 7.0%/8.33% ± 5.45%, mean ± SEM; APP:GFP anterograde/retrograde particles respectively colocalizing with TfR:mCherry). Finally, P100 density gradients from mouse brains showed that fractions containing endogenous BACE-1 overlapped with a subset of TfR-positive vesicles, though colocalization with other markers were variable (Figure 2E and Figure S3). A schematic view summarizing the above data is presented in Figure 2F. The above experiments suggest that the majority of APP and BACE-1 vesicles are spatially segregated and that APP/BACE-1 colocalization is a low-frequency event under basal conditions. As physical proximity of APP and BACE-1 is an obvious requirement for initiating APP cleavage, we reasoned that conditions triggering Aβ generation (i.e., BACE-1 cleavage) should also increase APP/BACE-1 colocalization.

After each period of nuller presentation (750 ms), a spatial

mask was presented, which was a band-pass spatial frequency filtered noise patch (3°; band-pass frequencies: 1–6 cpd; 23% rms contrast). Once this mask appeared, observers indicated whether or not a grating had been seen. To direct attention toward the competing stimuli (attended condition), we had observers detect orientation changes (10°) that occurred stochastically buy Metformin (0.3 probability of occurrence) to the dominant competitor stimulus (175 ms). To divert attention away from the competing stimuli (unattended condition), we required observers to perform a letter identification task (RSVP task), detecting target letters (“J” or “K”; 1.5° × 1.5°; 0.3 probability of occurrence) within a stream of distractor letters (“X” “L” “V” “H” “B” “A” “C” “F” “Z” “Y” “O” “U” “N” “W” “E”), appearing in the periphery of the inducer eye (3.35° eccentricity) selleckchem every 175 ms. Prior to each block of trials, observers

were told which task to perform throughout the block. In both the Attended and Unattended conditions, the RSVP stream ended, and the fixation point changed color 750 ms prior to the onset of the nuller, providing ample time for observers to prepare for the task in which they would report whether a grating was seen or not. We thank David Heeger, Frank Tong, and the reviewers of this manuscript for valuable comments and discussion. Supported by NIH grants EY13358 and P30-EY008126, and by a grant (R31-10089) from the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology. “
“(Neuron

46, 421–432; May 5, 2005) The figure legend to Figure 2A contained incorrectly calculated SEM values given for disability scores (underlined below). Recalculated SEM values are now given in brackets below. This correction has, however, no impact on statistical analysis and the findings reported in this figure. We apologize to the readers of Neuron for any inconvenience this mistake may have caused. Figure 2. Immunomodulatory Effect and aminophylline Systemic Distribution of Intracisternal DR5:Fc (A) T cells and macrophages/microglia cells were isolated from the brain at the onset of the disease (day 7) (mean disability score ± SEM for DR5:Fc-treated group 2.63 ± 1.31 (correct SEM: 0.47); for Fc-treated control group 0.75 ± 0.38 (correct SEM: 0.32); n = 4 for both groups) and at the time of remission (day 14) (mean disability score ± SEM at the disease peak for DR5:Fc-treated group 1.95 ± 0.81 [n = 5]; for Fc-treated control group 2.92 ± 0.60 [n = 6]). Activation markers determined by FACS analysis are given as means with SEM (open bars, treatment with Fc fragment only; filled bars, treatment with DR5:Fc; ∗p < 0.05, Mann-Whitney U-test).

To increase basal rate of NLG1 cleavage, cultures were incubated with bicuculline (50 μM) and 4AP (25 μM) 2 days prior to imaging (Figures 3A and 3B). Interestingly, terminals apposing synapses with GFP-NLG1-ΔSD3 exhibited faster FM4-64 unloading kinetics (τ = 46.1 ± 1.2 s; Figure 6G) than terminals contacting GFP-NLG1-expressing cells (τ = 60.5 ± 1.5 s), indicating that blocking NLG1 cleavage increases presynaptic

release probability. To address whether cleavage of NLG1 is regulated by activity in vivo, we measured NLG1-NTFs generated during selleck inhibitor pilocarpine-induced status epilepticus (PSE) in mice. Intraperitoneal administration of pilocarpine in P60 mice induced robust seizures and resulted in a 2.2 ± 0.3-fold increase of soluble NLG1-NTFs in the hippocampus after 2 hr PSE (Figures 7A–7C). To test whether MMP9 is involved in PSE-induced NLG1 cleavage, we performed pilocarpine injections in MMP9 KO mice. Notably, 2 hr

PSE characterized by robust behavioral seizures failed to elevate soluble NLG1-NTFs in MMP9 KO hippocampus (1.1 ± 0.1 relatively to control; Selleckchem Talazoparib Figures 7B and 7C). As a control for epileptic activity, both WT and MMP9 KO mice exhibited upregulation of the activity-regulated protein Arc/Arg3.1 after PSE (Figure 7B). Given the enrichment of NLG1-NTFs during the first postnatal weeks (Figures 2G and 2H), we addressed whether NLG1 cleavage is regulated by sensory experience during development. For this, we subjected mice to 5 days of dark rearing (DR) from P21–P26, a period of heightened sensory-evoked refinement of visual cortical circuits (Hensch, 2004), and subsequently re-exposed them to light for a brief period of 2 hr (DR+2hL, Figure 7D). This protocol induces rapid synaptic remodeling in the visual cortex and results Linifanib (ABT-869) in extensive molecular, functional, and structural synaptic changes (Philpot et al., 2001; Tropea et al., 2010). With this paradigm, 2 hr of re-exposure to light after 5 days of DR caused an increase in NLG1

cleavage in the visual cortex of WT mice (DR: 1.0 ± 0.1; DR+2hL: 1.5 ± 0.2, relatively to light-reared (LR) group; Figures 7E and 7F), but not in MMP9 KO animals (DR: 0.9 ± 0.1; DR+2hL: 1.0 ± 0.1; relative to LR group). Together these findings indicate that increased neuronal activity in vivo triggers MMP9-dependent cleavage of NLG1 in both mature and developing circuits. Although implicated in diverse forms of activity-dependent synaptic maturation and plasticity (Choi et al., 2011; Chubykin et al., 2007; Jung et al., 2010), it has been unclear whether neuroligins acutely regulate synapse function and whether the neuroligin-neurexin transsynaptic complex undergoes dynamic dissociation. Here we have shown that increased neuronal activity decreases synaptic NLG1 in minutes.

2-3′UTR and examined the distribution of endogenous Kv4.2 mRNA in dendrites.

We found similar dendritic localization and punctate pattern of Kv4.2 mRNA in WT and fmr1 KO neurons ( Figure 4C), and similar Kv4.2 mRNA levels along dendrites of neurons with or without FMRP ( Figure 4C). Taken together, these results suggest that FMRP is not essential for Kv4.2 mRNA dendritic targeting or stability in basal conditions. To test for FMRP regulation of Kv4.2 protein expression we compared Torin 1 supplier Kv4.2 levels in the hippocampus from adult WT and fmr1 mutant mice, using Kv4.2 KO mice as control for Kv4.2 antibody specificity ( Figure 5A and 5B). We found ∼1.5–2-fold increase of Kv4.2 immunoreactivity in the CA1 dendritic field of the hippocampus from 3-week-old and 2-month-old fmr1 KO mice ( Figure 5A), and a similar increase of Kv4.2 protein levels in the hippocampus from adult fmr1 KO mice ( Figure 5B). Next, we performed surface biotinylation buy Y-27632 on cultured hippocampal neurons and used actin both as loading control and to confirm that our biotinylation protocol results in biotinylation of surface but not cytosolic proteins. We found higher surface as well as total Kv4.2 levels in DIV14 hippocampal neurons without FMRP (Figure 5C). Using antibody against an extracellular

epitope of Kv4.2 for immunostaining of unpermeabilized DIV14 hippocampal neurons, we found higher levels of Kv4.2 on the dendritic surface of neurons without FMRP (Figure 5D). Because both total Kv4.2 protein levels and the surface expression of Kv4.2 on dendrites are elevated in the absence of FMRP whereas the relative proportions of surface and total Kv4.2 protein levels were not significantly altered, these findings indicate that FMRP suppresses Kv4.2 production. To test whether FMRP binding to Kv4.2-3′UTR could suppress protein production, we performed an in vitro translation assay using Renilla luciferase transcript fused to Kv4.2-3′UTR together with firefly luciferase transcript

for normalization, and included either purified GST as control or purified GST-mouse full-length FMRP, and nuclease-treated rabbit reticulocyte lysates. We found that FMRP suppressed Kv4.2-3′UTR-dependent translation by 60% (p < 0.001, n = 4) ( Figure 5F). Moreover, expression of MS2BS(6X)-Kv4.2-S.3′UTR but not MS2BS(6X) alone led to an increase of surface Kv4.2 expression ( Figure 5E), indicating that FMRP all suppression of Kv4.2 is relieved by disruption of FMRP interaction with the 3′UTR of Kv4.2 mRNA via MS2BS(6X)-Kv4.2-S.3′UTR. Taken together, these findings support the notion that FMRP suppression of Kv4.2 protein expression in neuronal dendrites is due to translational repression via its association with Kv4.2-3′UTR. To look for evidence of Kv4.2 local translation in the dendrites of cultured hippocampal neurons, we expressed Dendra-Kv4.2 in these neurons, severed a dendrite via UV illumination from the 2-photon microscope, photo-converted the existing Dendra-Kv4.

These findings yielded several new insights regarding the functional implications of the unique connectivity pattern of dendritic inhibition. Importantly, although these insights are based on the analytical

solution for the steady-state case and for passive dendrites ( Figures 1, 2, and S1–S3), they nevertheless explain simulated results Anti-diabetic Compound Library order obtained for corresponding nonlinear and transient cases. In particular, we analyzed in detail the case of an MC-to-PC inhibitory connection in layer 5 of the neocortex ( Figures 5 and 6), whereby the MC’s inhibitory synapses contact the distal apical dendrites of the PC. Near the main apical branch of the PC, a powerful Ca2+ spike could be evoked; this spike interacts reciprocally with the soma to generate a burst of Na+ spikes Epigenetics Compound Library price at the soma (BAC firing; Larkum et al., 1999). Although the MC’s synapses are more distal than the Ca2+ spike initiation region, we showed that they do effectively dampen the Ca2+ spike (see Figure S12) and also electrically decouple the apical dendrite from the soma, as expected from our analysis of the corresponding passive

case. The effective spread of SL into the dendritic region surrounded by multiple inhibitory synapses ( Figures 4 and 5) leads to a spatially extended shunted dendritic domain beyond the anatomical domain demarcated by these synapses. This spatial spread of inhibitory shunt implies that in order to dampen excitatory and/or excitable dendritic currents, it is not necessary to match each excitatory synapse with a corresponding adjacent inhibitory synapse. Rather, by surrounding a dendritic region with a few inhibitory contacts,

it is possible to effectively dampen the excitatory and/or excitable current that would be generated in this region ( Figures 5 and 6) and thereby effectively control the neuron’s output. This may explain why in the neocortex and the hippocampus, only ∼20% of the synapses are inhibitory ( DeFelipe Adenosine triphosphate and Fariñas, 1992; Megías et al., 2001; Merchán-Pérez et al., 2009). Due to the extended centripetal spread of the inhibitory shunt, different functional dendritic domains may interact with each other and be formed dynamically by recruiting and/or omitting various combinations of inhibitory synapses at strategic loci. For example, when each of the group of five inhibitory synapses in Figure 4A is individually active, then the functional dendritic subdomain corresponding to each inhibitory subgroup is spatially restricted. However, when all three inhibitory groups of synapses are active together, as in Figure 4A, then the functional dendritic domain that is shunted by the 15 inhibitory synapses expands dramatically, effectively controlling the excitatory and/or excitable charge (output) from a large portion of the postsynaptic dendritic tree.

35%, n = 104 events) were theta independent. In contrast to the prefrontal NG, the phase coupling between hippocampal theta and gamma is absent at this age (Figure S3Bii). As for the adult Hipp (Buzsáki et al., 1992 and Le Van Quyen et al., 2008), the low amplitude (∼25 μV) neonatal ripples appeared in conjunction

with SPWs, are restricted to the Str pyr, and are tightly coupled to MUA discharge (Figure S3C). Similar to neocortical areas, hippocampal activity switched from discontinuous bursts to continuous theta rhythms during early postnatal development (n = 6 pups) (Figure S3D; selleck chemicals llc Table S3). However, this transition occurred 1–2 days earlier in the Hipp than in the PFC (Figure S4) and may reflect the differences in the maturation of cytoarchitecture and connectivity (Angevine, 1975). Several approaches OTX015 price were used to characterize the interactions between prefrontal and hippocampal activity during the first two postnatal weeks. First, we examined the temporal correspondence of discontinuous oscillations

across the PFC and CA1 area of the intermediate Hipp in simultaneous recordings from both areas. The majority of hippocampal theta bursts occurred within a narrow time window (<3 s) with SB or NG in the Cg (87.7%, n = 203 events from 6 pups) or PL (93.6%, n = 296 events from 9 pups) (Figure 4A), their onset either preceding (Cg: 20.7% of events, delay 0.5 ± 0.006 s, PL: 13.9% of events, delay 1 ± 0.14 s), succeeding (Cg: 3.9% of events, delay 0.14 ± 0.05 s, PL: 3.4% of events, delay 0.63 ± 0.24 s) or matching Calpain (Cg: 63.1% of events, PL: 73.4% of events) the onset of prefrontal events. Second, the temporal synchrony between hippocampal and prefrontal activity was assessed by spectral coherence analysis (Figures 4B and S5A). Due to the predominance of distinct activity patterns with different spatial organization in the Cg and PL, we separately analyzed the cingulate-hippocampal and the prelimbic-hippocampal synchronization. Coherence coefficients for simultaneously occurring oscillations in the Cg and Hipp (0.52 ± 0.02, n = 59 events from 6 pups) as well as

in the PL and Hipp (0.53 ± 0.02, n = 90 events from 9 pups) were relatively high. To decide whether the strong prefrontal-hippocampal coupling was restricted to simultaneously occurring oscillatory events, the coherence analysis was performed also for oscillations at shuffled time windows. The resulting coherence coefficients (Cg: 0.39 ± 0.02, n = 59 events from 6 pups, PL: 0.47 ± 0.002, n = 90 events from 9 pups) were significantly lower than for original time windows of oscillatory activity. To verify that this high level of coherence was a genuine feature of the prefrontal-hippocampal interactions, we additionally assessed the synchronization between the Hipp and V1 as well as between different neocortical areas. The level of coherence for oscillatory events in the CA1 area and V1 was significantly (p < 0.01) lower (0.37 ± 0.

Our analysis enabled us to study the entire time course of cortical processes underlying decision making, outcome evaluation, and learning (i.e., updating) value representations. Selleckchem KU 55933 Upon stimulus presentation, retrieval of learnt values activates cortical value representations

reflected in early midfrontal EEG activity. Decision certainty is reflected in P3b-like parietal EEG activity around response latency, and mapping of the selected action to the motor response is reflected in lateralized activity from (pre)motor cortices (Figure S5C). After feedback, initially outcomes are processed separately depending on whether their consequences are real or fictive, presumably in order to convert feedback information into a common value currency allowing for efficient learning of stimulus values. Then the information about necessary value updates converges on common parietal P3b-like activity modulated by whether the action was successful or not. Given the probabilistic nature of the instrumental learning task, several parameters need to be used to weight the impact of single-trial outcomes. Over the course of multiple trials, learning rate indicates the learning success and downweights the single-feedback information at later learning stages. Moreover, when a choice Selleckchem Epigenetics Compound Library is made with high certainty, perseveration of this behavior is favorable. This means that already at the time of the response (and thus before

feedback), high certainty might be used to strengthen the current value representation, thereby shielding it from potentially misleading feedback. Interestingly, the stimulus- and feedback-locked late parietal P3b-like activity is consistent with the notion of certainty- and learning-rate-weighted value strengthening and updates at different time points: high response Adenosine certainty, which should be associated with re-encoding (strengthening) of the stimulus value to assure perseveration,

is associated with high stimulus-locked P3b amplitudes. In contrast, after feedback, high learning rates and unfavorable outcomes commonly give rise to high feedback-locked P3b amplitudes, presumably reflecting value updating and storage, thereby increasing the likelihood to change future choice behavior. To put it briefly, lower stimulus-related P3b and higher feedback-related P3b amplitudes should be associated with an increased likelihood to switch choice on the next encounter with the same stimulus. This notion that feedback- and stimulus-related P3b amplitudes are inversely related to switch behavior was tested at electrode Pz, which was identified via a conjunction analysis of all relevant stimulus- and feedback-locked effects in the P3b time window (Figure 4D). A discrimination threshold was iteratively estimated in one half of randomly chosen trials that was then used to predict switching in the second half of trials.

Another possibility is that a small fraction of the NCA channels could remain localized and functional in the absence of NLF-1.

We favor the second possibility because expressing either IOX1 NLF-1 or NCA-1 in the GABAergic motor neurons did not result in any noticeable improvement in the locomotion deficit in nlf-1 or nca(lf) mutants (data not shown). Moreover, this small behavioral difference coincided with a subtle, yet also consistent, reduction in Na+-dependent change in background leak current (ΔI; Figure 4C) and RMP (ΔV; Figure 4F) in the AVA premotor interneurons in nlf-1 when compared to that in nca(lf) mutants. We provide the first direct, physiological evidence for the NCA channel’s role in maintaining neuronal RMP. Thus, in both the nonspiking Entinostat mw C. elegans and spiking vertebrate neurons, this Na+ leak channel constitutes a conserved mechanism that modulates neuronal excitability. Despite a modest sequence homology between NLF-1 and mNLF-1, and the differences in neuronal properties between vertebrate and C. elegans interneurons, mNLF-1 fully substitutes

for NLF-1 when expressed in C. elegans. mNLF-1 exhibits enriched, broad expression in the mouse brain (http://mouse.brain-map.org). shRNA-mediated knockdown of mNLF-1 effectively, albeit partially reduces the Na+ leak currents in primary cortical neuron cultures. Thus, while mNLF-1’s physiological function awaits further investigation, our current studies imply its role in the folding/trafficking of either the NALCN Na+ leak channel. The ability of mNLF-1 to substitute NLF-1 in the C. elegans nervous system further highlights the conservation of machineries that modulate membrane physiology. Removing extracellular Na+ leads to a further hyperpolarization of RMP in both nca(lf) and nlf-1 mutants, indicating the presence of additional Na+ channels in modulating neuronal excitability. As the C. elegans genome does not encode voltage-gated Na+

channels, machineries that carry out the remaining Na+ conductance remain to be identified. How the Na+ leak channel affects intact neural circuit activity remains to be explored. Intriguingly, nalcn−/− mice cannot generate respiratory rhythm ( Lu et al., 2007); na flies fail to exhibit motor patterns associated with circadian cycles ( Lear et al., 2005; Nash et al., 2002; Zhang et al., 2010); the knockdown of a snail NALCN homolog compromises respiratory function ( Lu and Feng, 2011); the loss of NCA leads to disrupted rhythmicity in C. elegans locomotion ( Pierce-Shimomura et al., 2008; this study). Collectively, these phenotypes imply a requirement of this channel in neural networks generating rhythmic behaviors. Despite a broad expression in the C. elegans motor circuit, NLF-1 activity in motor neurons was neither necessary, nor sufficient to restore the continuity of locomotion.

How does the β1 subunit accelerate pore opening in Nav channels? A possible mechanism could be a modulation of the kinetics of the rearrangements of the VS by the β1 subunits. We tested this hypothesis by measuring gating currents that directly report VS movement. Figure 1A shows gating current traces recorded HTS assay in Xenopus oocytes using activation protocols for both muscular (Nav1.4) and neuronal (Nav1.2) Nav

channels with or without coexpressed β1 subunits. In both channels, the kinetics of activating gating currents (see Figure S1 available online for a detailed fitting procedure) are accelerated approximately 2-fold in the presence of β1 subunits ( Figure 1B, open versus full Selleckchem VE821 symbols), in good agreement with the moderate acceleration of pore opening. These results constitute evidence for a direct modulation of the VS movement in Nav channels by the β1 subunits and provide a general

molecular basis to explain the modulatory role of these subunits on Nav channel function. The mechanism by which the β1 subunit accelerates VS kinetics in Nav channels is presently unknown to us. In the presence of the β1 subunit, the rearrangement of the VS exhibits positive cooperativity (Campos et al., 2007a and Chanda et al., 2004), which leads to accelerated VS kinetics (Chanda et al., 2004). Hence, it is tempting to speculate that the β1 subunit may act by coupling the movement of VS in adjacent domains of the Nav channel. Yet, even in the absence of the β1 subunit, the gating currents develop up to 3-fold faster in Nav channels relative to prototypical

Shaker-type Kv channels for voltages near the threshold of activation of action potentials (i.e., around −40 mV, Figures 1B and 1C). What are the molecular determinants and mechanism underlying this intrinsic kinetics difference? It is now well established that the activation of the four VSs in the α subunit of Nav channels is asynchronous: the VSs in the first three domains (DI–DIII) rearranges rapidly and controls pore opening, while the VSs in DIV rearranges with slow kinetics comparable to those of VSs found in Shaker-type Kv channels and controls fast inactivation of the sodium conductance (Chanda and Bezanilla, 2002, too Goldschen-Ohm et al., 2013 and Gosselin-Badaroudine et al., 2012). Hence, these observations suggest that the rapid VSs of Nav DI–DIII may possess specific molecular determinants that are absent in the slow VSs of Nav DIV and of Shaker-type Kv channels. In order to identify such determinants, we compared the amino acid sequence of the VSs from Nav1.4 DI–DIII to the slow VSs from Nav1.4 DIV, from Shaker-type Kv channels and also from slow-activating bacterial Nav channels (Kuzmenkin et al., 2004). Two positions bear either hydrophilic residues in rapid VSs or hydrophobic residues in slow VSs.