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<div><div><div><p>Calcium-dependent protein kinase II alpha (CaMKIIα) is a highly abundant protein within the hippocampus, the region of the brain responsible for memory and learning. CaMKII has both structural and signaling roles in the regulation of the connective strength of synapses in excitatory neurons. It has a unique structure comprised of twelve subunits that form a dynamic assembly and is highly flexible. Its structural behavior has been shown to affect its activity, and a comprehensive mechanism of structure and function is still not fully understood. The determination of the quaternary structure of the CaMKII holoenzyme has been attempted for nearly 20 years by a variety of methods, with no one method giving a definitive structure. Problems in obtaining a structure originated with observation methods that estimated quaternary shape from low-resolution ensemble averages or required significant alteration of the protein to enforce a particular conformation. In this work, experiments were conducted to remove these limitations and provide a path towards the quaternary structure of CaMKIIα. Different expression and purification methods were evaluated to produce an optimal protocol for the generation of samples of concentrated, monodisperse, autoinhibited full-length wild-type CaMKIIα for study with cryo-electron microscopy. Strategies for microscopy sample preparation were investigated, including affinity girds, graphene-coated grids, and holey carbon grids. Lastly, experiments using negative stain electron microscopy, cryo-electron microscopy with single particle analysis, and cryo-electron tomography with subtomogram averaging were conducted to reveal the conditions required to produce an unambiguous three-dimensional structure. It was found that the assembly of the hexameric hub rings appeared to have flexible orientation, and superposition problems inherent in two-dimensional projection averaging requires the use of cryo-electron tomography to unravel the ambiguity in both hub orientation and catalytic module placement within the reconstructed volume. A subtomogram average of a limited number of particles revealed a hub domain that matched the morphology of prior reports, but the determination of catalytic module placement was not resolved. The cumulative result of this work establishes a strategy for the large-scale data collection needed to fully elucidate the structure of this challenging and fascinating protein.</p></div></div></div>

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第18835号 / 理博第4093号 / 新制||理||1588(附属図書館) / 31786 / 京都大学大学院理学研究科生物科学専攻 / (主査)教授 平野 丈夫, 教授 七田 芳則, 教授 高田 彰二 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

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The basal ganglia form an important structure centrally placed in the brain. They receive input from motor, associative and limbic areas, and produce output mainly to the thalamus and the brain stem. The basal ganglia have been implied in cognitive and motor functions. One way to understand the basal ganglia is to take a look at the diseases that affect them. Both Parkinson's disease and Huntington's disease with their motor problems are results of malfunctioning basal ganglia. There are also indications that these diseases affect cognitive functions. Drug addiction is another example that involves this structure, which is also important for motivation and selection of behaviour. In this licentiate thesis I am laying the groundwork for a detailed model of the striatum, which is the input stage of the basal ganglia. The striatum receives glutamatergic input from the cortex and thalamus, as well as dopaminergic input from substantia nigra. The majority of the neurons in the striatum are medium spiny (MS) projection neurons that project mainly to globus pallidus but also to other neurons in the striatum and to both dopamine producing and GABAergic neurons in substantia nigra. In addition to the MS neurons there are fast spiking (FS) interneurons that are in a position to regulate the firing of the MS neurons. These FS neurons are few, but connected into large networks through electrical synapses that could synchronise their effect. By forming strong inhibitory synapses on the MS neurons the FS neurons have a powerful influence on the striatal output. The inhibitory output of the basal ganglia on the thalamus is believed to keep prepared motor commands on hold, but once one of them is disinhibited, then the selected motor command is executed. This disinhibition is initiated in the striatum by the MS neurons. Both MS and FS neurons are active during so called up-states, which are periods of elevated cortical input to striatum. Here I have studied the FS neurons and their ability to detect such up-states. This is important because FS neurons can delay spikes in MS neurons and the time between up-state onset and the first spike in the MS neurons is correlated with the amount of calcium entering the MS neuron, which in turn might have implications for plasticity and learning of new behaviours. The effect of different combinations of electrical couplings between two FS neurons has been tested, where the location, number and strength of these gap junctions have been varied. I studied both the ability of the FS neurons to fire action potentials during the up-state, and the synchronisation between neighbouring FS neurons due to electrical coupling. I found that both proximal and distal gap junctions synchronised the firing, but the distal gap junctions did not have the same temporal precision. The ability of the FS neurons to detect an up-state was affected by whether the neighbouring FS neuron also received up-state input or not. This effect was more pronounced for distal gap junctions than proximal ones, due to a stronger shunting effect of distal gap junctions when the dendrites were synaptically activated. We have also performed initial stochastic simulations of the Ca2+-calmodulin-dependent protein kinase II (CaMKII). The purpose here is to build the knowledge as well as the tools necessary for biochemical simulations of intracellular processes that are important for plasticity in the MS neurons. The simulated biochemical pathways will then be integrated into an existing model of a full MS neuron. Another venue to explore is to build striatal network models consisting of MS and FS neurons and using experimental data of the striatal microcircuitry. With these different approaches we will improve our understanding of striatal information processing. / QC 20101116

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