PHYSIOLOGICAL ROLES OF ION CHANNELS IN A SMALL NETWORK

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DESCRIPTION: (Adapted from the Investigator's Abstract) Simple behaviors are generated by networks of neurons, interacting in complex and nonlinear ways. The output from a behavioral network is determined by the pattern and strength of synaptic interactions within the network as well as the unique intrinsic firing properties of the different network neurons. These synaptic and firing properties are generated in turn by the differing ratios of ion channels and receptor genes that are expressed by each neuron in the network. Our goal is to bridge the span from gene expression to neural network function, to show how single genes contribute to shaping the behavioral output from a neural network. To accomplish this, we are studying the roles of a set of potassium and calcium channel genes within the 14-neuron pyloric network in the lobster stomatogastric ganglion. This network is one of the best-described neural networks: all of the synaptic connections are known and the firing properties of all the neurons are well understood. We propose to manipulate gene expression for ion channels in single identified neurons in this network to better understand the rules of network function. There are 3 specific aims to accomplish this goal. First, we will complete our ongoing project to clone the genes for and analyze the biophysical properties of the Shaker family of voltage dependent K+ channels as well as two calcium channels. Second, we will determine which neurons in the pyloric network express these genes, and where in the neuron the proteins are targeted. Third, we will increase the expression of these genes in single neurons, using injections of sense RNA, or decrease expression with injections of dominant negative, antisense or double-stranded RNA, and determine how this alters the firing and synaptic properties of the neuron and the pattern of activity in the pyloric network. This "molecular neuroethological" approach will allow us to study the effects of one gene and thus one channel at a time on the behavioral output of a network, which in the past has only been possible with theoretical mathematical models.