I–II Loop Structural Determinants in the Gating and Surface Expression of Low Voltage-Activated Calcium Channels

The intracellular loops that interlink the four transmembrane domains of Ca2+- and Na+-channels (Cav, Nav) have critical roles in numerous forms of channel regulation. In particular, the intracellular loop that joins repeats I and II (I–II loop) in high voltage-activated (HVA) Ca2+ channels possesses the binding site for Cavβ subunits and plays significant roles in channel function, including trafficking the α1 subunits of HVA channels to the plasma membrane and channel gating. Although there is considerable divergence in the primary sequence of the I–II loop of Cav1/Cav2 HVA channels and Cav3 LVA/T-type channels, evidence for a regulatory role of the I–II loop in T-channel function has recently emerged for Cav3.2 channels. In order to provide a comprehensive view of the role this intracellular region may play in the gating and surface expression in Cav3 channels, we have performed a structure-function analysis of the I–II loop in Cav3.1 and Cav3.3 channels using selective deletion mutants. Here we show the first 60 amino acids of the loop (post IS6) are involved in Cav3.1 and Cav3.3 channel gating and kinetics, which establishes a conserved property of this locus for all Cav3 channels. In contrast to findings in Cav3.2, deletion of the central region of the I–II loop in Cav3.1 and Cav3.3 yielded a modest increase (+30%) and a reduction (−30%) in current density and surface expression, respectively. These experiments enrich our understanding of the structural determinants involved in Cav3 function by highlighting the unique role played by the intracellular I–II loop in Cav3.2 channel trafficking, and illustrating the prominent role of the gating brake in setting the slow and distinctive slow activation kinetics of Cav3.3

Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells

Significant inroads have been made to understand cerebellar cortical processing but neural coding at the output stage of the cerebellum in the deep cerebellar nuclei (DCN) remains poorly understood. The DCN are unlikely to just present a relay nucleus because Purkinje cell inhibition has to be turned into an excitatory output signal, and DCN neurons exhibit complex intrinsic properties. In particular, DCN neurons exhibit a range of rebound spiking properties following hyperpolarizing current injection, raising the question how this could contribute to signal processing in behaving animals. Computer modeling presents an ideal tool to investigate how intrinsic voltage-gated conductances in DCN neurons could generate the heterogeneous firing behavior observed, and what input conditions could result in rebound responses. To enable such an investigation we built a compartmental DCN neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than −70 mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum

Physiological modes of action of fluoxetine and its human metabolites in algae

Fluoxetine, the active ingredient of many antidepressants, was identified as specifically toxic toward algae in a quantitative structure−activity relationship (QSAR) analysis with literature data for algae, daphnia, and fish. The goal of this study was to elucidate the mode of action in algae and to evaluate the toxicity of the major human metabolites of fluoxetine using two different algae tests. The time dependence and sensitivity of the different effect endpoints yield information on the physiological mode of action. Baseline toxicity was predicted with QSARs based on measured liposome-water partition coefficients. The ratio of predicted baseline toxicity to experimental toxicity (toxic ratio TR) gives information on the intrinsic potency (extent of specificity of effect). The metabolite p-trifluoromethylphenol was classified to act as baseline toxicant. Fluoxetine (TR 60−150) and its pharmacologically active metabolite norfluoxetine (TR 10−80) exhibited specific toxicity. By comparison with reference compounds we conclude that fluoxetine and norfluoxetine have an effect on the energy budget of algal cells since the time pattern of these two compounds is most similar to that observed for norflurazon, but they act less specifically as indicated by lower TR values and the similarity of the effect pattern to baseline toxicants. The mixture toxicity of fluoxetine and its human metabolites norfluoxetine and p-TFMP can be predicted using the model of concentration addition for practical purposes of risk assessment despite small deviations from this model for the specific endpoints like PSII inhibition because the integrative endpoints like growth rate and reproduction in all cases gave agreement with the predictions for concentration addition