Cloning and characterization of a P2X receptor expressed in the central nervous system of Lymnaea stagnalis.

P2X receptors are membrane ion channels gated by extracellular ATP. Mammals possess seven distinct P2X subtypes (P2X1-7) that have important functions in a wide array of physiological processes including roles in the central nervous system (CNS) where they have been linked to modulation of neurotransmitter release. We report here the cloning and functional characterization of a P2X receptor from the mollusc Lymnaea stagnalis. This model organism has a relatively simple CNS consisting of large readily identifiable neurones, a feature which together with a well characterized neuronal circuitry for important physiological processes such as feeding and respiration makes it an attractive potential model to examine P2X function. Using CODEHOP PCR we identified a single P2X receptor (LymP2X) in Lymnaea CNS which was subsequently cloned by RT-PCR. When heterologously expressed in Xenopus oocytes, LymP2X exhibited ATP evoked inward currents (EC(50) 6.2 µM) which decayed during the continued presence of agonist. UTP and ADP did not activate the receptor whereas αßmeATP was a weak agonist. BzATP was a partial agonist with an EC(50) of 2.4 µM and a maximal response 33% smaller than that of ATP. The general P2 receptor antagonists PPADS and suramin both inhibited LymP2X currents with IC(50) values of 8.1 and 27.4 µM respectively. LymP2X is inhibited by acidic pH whereas Zn(2+) and Cu(2+) ions exhibited a biphasic effect, potentiating currents up to 100 µM and inhibiting at higher concentrations. Quantitative RT-PCR and in situ hybridization detected expression of LymP2X mRNA in neurones of all CNS ganglia suggesting this ion channel may have widespread roles in Lymnaea CNS function.

A self-organizing state-space-model approach for parameter estimation in hodgkin-huxley-type models of single neurons.

Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm), often in combination with a local search method (such as gradient descent) in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a potentially useful tool in the construction of biophysical neuron models.

A P2X receptor from the tardigrade species Hypsibius dujardini with fast kinetics and sensitivity to zinc and copper.

BACKGROUND: Orthologs of the vertebrate ATP gated P2X channels have been identified in Dictyostelium and green algae, demonstrating that the emergence of ionotropic purinergic signalling was an early event in eukaryotic evolution. However, the genomes of a number of animals including Drosophila melanogaster and Caenorhabditis elegans, both members of the Ecdysozoa superphylum, lack P2X-like proteins, whilst other species such as the flatworm Schistosoma mansoni have P2X proteins making it unclear as to what stages in evolution P2X receptors were lost. Here we describe the functional characterisation of a P2X receptor (HdP2X) from the tardigrade Hypsibius dujardini demonstrating that purinergic signalling is preserved in some ecdysozoa. RESULTS: ATP (EC50 approximately 44.5 microM) evoked transient inward currents in HdP2X with millisecond rates of activation and desensitisation. HdP2X is antagonised by pyridoxal-phosphate-6-azophenyl-2',4' disulfonic acid (IC50 15.0 microM) and suramin (IC50 22.6 microM) and zinc and copper inhibit ATP-evoked currents with IC50 values of 62.8 microM and 19.9 microM respectively. Site-directed mutagenesis showed that unlike vertebrate P2X receptors, extracellular histidines do not play a major role in coordinating metal binding in HdP2X. However, H306 was identified as playing a minor role in the actions of copper but not zinc. Ivermectin potentiated responses to ATP with no effect on the rates of current activation or decay. CONCLUSION: The presence of a P2X receptor in a tardigrade species suggests that both nematodes and arthropods lost their P2X genes independently, as both traditional and molecular phylogenies place the divergence between Nematoda and Arthropoda before their divergence from Tardigrada. The phylogenetic analysis performed in our study also clearly demonstrates that the emergence of the family of seven P2X channels in human and other mammalian species was a relatively recent evolutionary event that occurred subsequent to the split between vertebrates and invertebrates. Furthermore, several characteristics of HdP2X including fast kinetics with low ATP sensitivity, potentiation by ivermectin in a channel with fast kinetics and distinct copper and zinc binding sites not dependent on histidines make HdP2X a useful model for comparative structure-function studies allowing a better understanding of P2X receptors in higher organisms.

Dynamic control of a central pattern generator circuit: a computational model of the snail feeding network.

Central pattern generators (CPGs) are networks underlying rhythmic motor behaviours and they are dynamically regulated by neuronal elements that are extrinsic or intrinsic to the rhythmogenic circuit. In the feeding system of the pond snail, Lymnaea stagnalis, the extrinsic slow oscillator (SO) interneuron controls the frequency of the feeding rhythm and the N3t (tonic) has a dual role; it is an intrinsic CPG interneuron, but it also suppresses CPG activity in the absence of food, acting as a decision-making element in the feeding circuit. The firing patterns of the SO and N3t neurons and their synaptic connections with the rest of the CPG are known, but how these regulate network function is not well understood. This was investigated by building a computer model of the feeding network based on a minimum number of cells (N1M, N2v and N3t) required to generate the three-phase motor rhythm together with the SO that was used to activate the system. The intrinsic properties of individual neurons were represented using two-compartment models containing currents of the Hodgkin-Huxley type. Manipulations of neuronal activity in the N3t and SO neurons in the model produced similar quantitative effects to food and electrical stimulation in the biological network indicating that the model is a useful tool for studying the dynamic properties of the feeding circuit. The model also predicted novel effects of electrical stimulation of two CPG interneurons (N1M and N2v). When tested experimentally, similar effects were found in the biological system providing further validation of our model.