Anatomy and Neuroscience - Theses
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ItemFunctional analysis of sodium channel gene variation in epilepsy( 2013)A genetic etiology of epilepsy is widely accepted in 50-70% of all epilepsy syndromes. With genome sequencing now increasingly efficient and affordable, more and more novel genes and mutations are being discovered that are associated with epilepsy. However, most of the mutations have been discovered in genes that code for ion channels which has led to the theory that the genetic epilepsies are a family of channelopathies. The voltage-gated sodium channel family have been particularly implicated with over 800 variants discovered in this gene family. Given their critical role in regulating neuronal excitability it is not surprising that genetic variations in sodium channels can have functional and potentially devastating consequences. With a focus on the voltage-gated sodium channels, the three chapters in this thesis used high-throughput automated planar patch-clamp technology to try and develop a deeper understanding of genetic risk in epilepsy. Chapter two examines a novel cause in a mouse model of absence epilepsy that harbours a mutation in the Scn8a gene. The phenotype of this mouse is enhanced on the C3H background, as opposed to C57, where the C3H animal also has a mutation in the Scn2a gene. The individual biophysical profiles of these two mutations were examined on the Nanion patchliner, and their potential genetic interaction was investigated in a computer model of a layer 5 pyramidal neuron, to see if this could be explained by a biological interaction at the axon initial segment. The results revealed an overall loss of function of the NaV1.6V752F mutant, and an overall gain of function in the NaV1.2V929F mutant. When these changes were implemented in the computer model, it revealed that the output was dominated by the NaV1.2V929F mutant, which suggests there is not a biological interaction of these two genes at the axon initial segment. Alternative scenarios where there may be an alternative site for biological epistasis will be revealed with future studies using immunohistochemistry and brain slice patch clamp recording in the mice. It may also be the case that the NaV1.2V929F mutant is not a modifier of the NaV1.6V752F mutant, which will be revealed by genetic studies to identify the modifier genes. The third chapter examined the modulation of NaV1.2 and NaV1.1 by the β1 auxiliary subunit. As mutations in the β1-subunit have been detected in patients with epilepsy, understanding their impact on subunits from excitatory and inhibitory neurons is critical for understanding how this variation impacts on risk for epilepsy. There was a differential modulation revealed where β1 had a greater functional effect on the NaV1.2 channel but a greater effect on current density on the NaV1.1 channel. Therefore if a variant in β1 experiences a functional change this suggests differentially altered levels of excitation and inhibition in the brain, which could feasibly result in an epileptic phenotype. The fourth chapter looked at exploiting the high-throughput capabilities of the Nanion patchliner, and examined eight mutations in the β1-subunit co-expressed with NaV1.1 and NaV1.2 that have been associated with epilepsy. With this influx of data we needed to devise a new way to represent this data, and converted all raw measurements to effect size values, and represented them on tornado diagrams. With this measurement we could then more easily directly compare parameters from the individual protocols and calculate averages both across mutations, and across parameters. From this data set it is quite apparent that the β1 mutants modulate the α-subunits quite differently, both comparing α-subunits, and comparing mutations. More importantly however this chapter highlighted a new way of thinking about analysis of high-throughput electrophysiology data. As people continue to look into the genetics of epilepsy and reveal novel genes and novel mutations implicated in the disease, we need to look for new ways to tame the genetic complexity, and look for points of convergence. High-throughput technology allows us to decrease the time lapse between the discovery of the genetic variants and the corresponding functional analysis. And the type of analysis as suggested in chapter four, enables us to start to look for points of convergence in the functional data. This data can then be used to train clustering algorithms to group the variants based on their ‘channelomic’ profile. To do this we need a large volume of functional data obtained from variants that have strong corresponding phenotypic data, and future studies should endeavour to accomplish this.