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Cav1.3 channels control D2-autoreceptor responses via NCS-1 in substantia nigra dopamine neurons
(2014)
Dopamine midbrain neurons within the substantia nigra are particularly prone to degeneration in Parkinson's disease. Their selective loss causes the major motor symptoms of Parkinson's disease, but the causes for the high vulnerability of SN DA neurons, compared to neighbouring, more resistant ventral tegmental area dopamine neurons, are still unclear. Consequently, there is still no cure available for Parkinson's disease. Current therapies compensate the progressive loss of dopamine by administering its precursor l-DOPA and/or dopamine D2-receptor agonists. D2-autoreceptors and Cav1.3-containing L-type Ca2+ channels both contribute to Parkinson’s disease pathology. L-type Ca2+ channel blockers protect SN DA neurons from degeneration in Parkinson's disease and its mouse models, and they are in clinical trials for neuroprotective Parkinson's disease therapy. However, their physiological functions in SN DA neurons remain unclear. D2-autoreceptors tune firing rates and dopamine release of SN DA neurons in a negative feedback loop through activation of G-protein coupled potassium channels (GIRK2, or KCNJ6). Mature SN DA neurons display prominent, non-desensitizing somatodendritic D2-autoreceptor responses that show pronounced desensitization in PARK-gene Parkinson’s disease mouse models. We analysed surviving human SN DA neurons from patients with Parkinson's disease and from controls, and detected elevated messenger RNA levels of D2-autoreceptors and GIRK2 in Parkinson's disease. By electrophysiological analysis of postnatal juvenile and adult mouse SN DA neurons in in vitro brain-slices, we observed that D2-autoreceptor desensitization is reduced with postnatal maturation. Furthermore, a transient high-dopamine state in vivo, caused by one injection of either l-DOPA or cocaine, induced adult-like, non-desensitizing D2-autoreceptor responses, selectively in juvenile SN DA neurons, but not ventral tegmental area dopamine neurons. With pharmacological and genetic tools, we identified that the expression of this sensitized D2-autoreceptor phenotype required Cav1.3 L-type Ca2+ channel activity, internal Ca2+, and the interaction of the neuronal calcium sensor NCS-1 with D2-autoreceptors. Thus, we identified a first physiological function of Cav1.3 L-type Ca2+ channels in SN DA neurons for homeostatic modulation of their D2-autoreceptor responses. L-type Ca2+ channel activity however, was not important for pacemaker activity of mouse SN DA neurons. Furthermore, we detected elevated substantia nigra dopamine messenger RNA levels of NCS-1 (but not Cav1.2 or Cav1.3) after cocaine in mice, as well as in remaining human SN DA neurons in Parkinson's disease. Thus, our findings provide a novel homeostatic functional link in SN DA neurons between Cav1.3- L-type-Ca2+ channels and D2-autoreceptor activity, controlled by NCS-1, and indicate that this adaptive signalling network (Cav1.3/NCS-1/D2/GIRK2) is also active in human SN DA neurons, and contributes to Parkinson's disease pathology. As it is accessible to pharmacological modulation, it provides a novel promising target for tuning substantia nigra dopamine neuron activity, and their vulnerability to degeneration.
Poster presentation: Introduction The ability of neurons to emit different firing patterns is considered relevant for neuronal information processing. In dopaminergic neurons, prominent patterns include highly regular pacemakers with separate spikes and stereotyped intervals, processes with repetitive bursts and partial regularity, and irregular spike trains with nonstationary properties. In order to model and quantify these processes and the variability of their patterns with respect to pharmacological and cellular properties, we aim to describe the two dimensions of burstiness and regularity in a single model framework. Methods We present a stochastic spike train model in which the degree of burstiness and the regularity of the oscillation are described independently and with two simple parameters. In this model, a background oscillation with independent and normally distributed intervals gives rise to Poissonian spike packets with a Gaussian firing intensity. The variability of inter-burst intervals and the average number of spikes in each burst indicate regularity and burstiness, respectively. These parameters can be estimated by fitting the model to the autocorrelograms. This allows to assign every spike train a position in the two-dimensional space described by regularity and burstiness and thus, to investigate the dependence of the firing patterns on different experimental conditions. Finally, burst detection in single spike trains is possible within the model because the parameter estimates determine the appropriate bandwidth that should be used for burst identification. Results and Discussion We applied the model to a sample data set obtained from dopaminergic substantia nigra and ventral tegmental area neurons recorded extracellularly in vivo and studied differences between the firing activity of dopaminergic neurons in wildtype and K-ATP channel knock-out mice. The model is able to represent a variety of discharge patterns and to describe changes induced pharmacologically. It provides a simple and objective classification scheme for the observed spike trains into pacemaker, irregular and bursty processes. In addition to the simple classification, changes in the parameters can be studied quantitatively, also including the properties related to bursting behavior. Interestingly, the proposed algorithm for burst detection may be applicable also to spike trains with nonstationary firing rates if the remaining parameters are unaffected. Thus, the proposed model and its burst detection algorithm can be useful for the description and investigation of neuronal firing patterns and their variability with cellular and experimental conditions.
Poster presentation: Introduction Dopaminergic neurons in the midbrain show a variety of firing patterns, ranging from very regular firing pacemaker cells to bursty and irregular neurons. The effects of different experimental conditions (like pharmacological treatment or genetical manipulations) on these neuronal discharge patterns may be subtle. Applying a stochastic model is a quantitative approach to reveal these changes. ...