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In several types of neurons, firing is an intrinsic property produced by specific classes of ion channels. Low-voltage-activated T-type calcium channels (T-channels), which activate with small membrane depolarizations, can generate burst firing and pacemaker activity. Here we have investigated the specific contribution to neuronal excitability of cloned human T-channel subunits. Using HEK-293 cells transiently transfected with the human alpha(1G) (Ca(V)3.1), alpha(1H) (Ca(V)3.2) and alpha(1I) (Ca(V)3.3) subunits, we describe significant differences among these isotypes in their biophysical properties, which are highlighted in action potential clamp studies. Firing activities occurring in cerebellar Purkinje neurons and in thalamocortical relay neurons used as voltage clamp waveforms revealed that alpha(1G) channels and, to a lesser extent, alpha(1H) channels produced large and transient currents, while currents related to alpha(1I) channels exhibited facilitation and produced a sustained calcium entry associated with the depolarizing after-potential interval. Using simulations of reticular and relay thalamic neuron activities, we show that alpha(1I) currents contributed to sustained electrical activities, while alpha(1G) and alpha(1H) currents generated short burst firing. Modelling experiments with the NEURON model further revealed that the alpha(1G) channel and alpha(1I) channel parameters best accounted for T-channel activities described in thalamocortical relay neurons and in reticular neurons, respectively. Altogether, the data provide evidence for a role of alpha(1I) channel in pacemaker activity and further demonstrate that each T-channel pore-forming subunit displays specific gating properties that account for its unique contribution to neuronal firing.
Pubmed ID: 11927664 RIS Download
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NEURON is a simulation environment for modeling individual neurons and networks of neurons. It provides tools for conveniently building, managing, and using models in a way that is numerically sound and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties. NEURON has benefited from judicious revision and selective enhancement, guided by feedback from the growing number of neuroscientists who have used it to incorporate empirically-based modeling into their research strategies. NEURON's computational engine employs special algorithms that achieve high efficiency by exploiting the structure of the equations that describe neuronal properties. It has functions that are tailored for conveniently controlling simulations, and presenting the results of real neurophysiological problems graphically in ways that are quickly and intuitively grasped. Instead of forcing users to reformulate their conceptual models to fit the requirements of a general purpose simulator, NEURON is designed to let them deal directly with familiar neuroscience concepts. Consequently, users can think in terms of the biophysical properties of membrane and cytoplasm, the branched architecture of neurons, and the effects of synaptic communication between cells. * helps users focus on important biological issues rather than purely computational concerns * has a convenient user interface * has a user-extendable library of biophysical mechanisms * has many enhancements for efficient network modeling * offers customizable initialization and simulation flow control * is widely used in neuroscience research by experimentalists and theoreticians * is well-documented and actively supported * is free, open source, and runs on (almost) everything
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