Final Report Summary - ARTIFICIAL-NEURON (Action Potential Dynamics in a Lipid Nanotube - A Minimal Model of the Neuron) Biological organisms achieve rapid and efficient transmission of information via the active propagation of electrochemical signals such as action potentials in neurons. While great progress has been made through experiments on actual neurons, there remain many open questions about signaling because biological neurons have very complex structures and intricate feedback. The goal of the artificial neuron project was to develop a minimal model of the neuron in which the active propagation of electrochemical signals could be readily studied. The structure of a neuron was to be mimicked using Giant Uni-lamellar Vesicles (GUVs). The GUV would then serve as the "soma" (body) of the synthetic neuron, while a long, membrane tube would be drawn out from this GUV to form an "axon". Finally, electrophysiology and fluorescence techniques were to be adapted to study signal propagation along the membrane tube.Results :Several important new techniques were developed during the Artificial Neuron project.* Incorporation of Voltage-Gated Potassium Channels into GUVsVoltage-gated ion channels had not previously been incorporated into the membrane of GUVs, and so it was thus necessary to develop a new technique to achieve this. Key steps in developing this technique included - o Purification of KvAP, a voltage-gated potassium channel (thereby transferring expertise from the MacKinnon Lab (Rockefeller University) to Europe)o Fluorescent labeling of the protein for bulk and single molecule measurements. o Development of a reliable protocol to incorporate the channel into GUVs (with the help of the Bagatolli lab (MEMPHYS, University of Southern Denmark) and the Gonzalez-Ros Lab (University of Alicante, Spain)o Use of quantitative confocal microscopy to confirm that the GUVs contained a high density of channels.o Testing of ion channel function in GUVs via Patch-Clamp. The successful development of a technique to incorporate voltage-gated ion channels into GUVs is of general interest as it can be used to address many important questions about membrane proteins. For example, recent studies suggest that the voltage-dependent gating of ion channels can depend strongly on the composition and state of the surrounding membrane. This could easily be studied using this reconstituted system.* Effects of Membrane Geometry on Protein Behavior Theoretical studies suggest that the behavior of proteins may be quite sensitive to the curvature of the membrane. We tested these ideas by studying the behavior of KvAP in membrane nanotubes. Firstly, with confocal fluorescence microscopy , we showed that protein concentration did indeed depend on membrane curvature, and studies continue to understand what drives this sorting. Secondly, the diffusion of quantum-dot labeled ion channels was measured using single-molecule particle tracking. The dependence of protein diffusion on membrane curvature was found to be consistent with theory. * Adaptation of Electrophysiology Techniques to GUVsTo measure electrical signaling requires the insertion of micro-electrodes into the interior of GUVs to both control the membrane voltage and measure current. While this is routinely achieved with cells via the patch-clamp technique, it had not previously been applied to GUVs and proved to be a major obstacle in the project. However, after much work, we have found a method to control adhesion and continue to test and develop this technique. This technique should allow the first measurements of ion channel activity in membranes where the tension can be truly controlled, and will be very important for unraveling the effect of tension on ion channel function.To conclude, the Artificial Neuron project has developed techniques, which can greatly advance our understanding protein-membrane interactions and biological signaling. Ion channels underpin a huge number of diseases and so this fundamental research should contribute indirectly to improvements in health and medicine.