Microelectrode Array Modeling of Genetic Neurological Disorders in the Era of Next Generation Sequencing

Advances in next-generation sequencing (NGS) and the ability to sequence the entire genome of many individuals in a cost-effective manner has led to the revelation of the genetic etiologies of a number of neurological disorders. Parallel advancements in predictive software, for example, have allowed for the annotation of potentially pathogenic variants. However, the development of appropriate systems to functionally interpret variants and identify pathogenic mechanisms has lagged behind. Understanding pathogenic mechanisms is crucial to the development of targeted therapeutics. Therefore, the main challenge to translating genetic findings into targeted therapeutics is functional modeling.

Increased understanding of the genetic architecture of epilepsy and the hyperexcitability that results from many epilepsy-causing variants makes the disease particularly well-suited for the development of model systems for functional interpretation of genetic variation. To capture the effects of genetic variation in neurological diseases, like epilepsy, complex cellular systems are crucial. In my thesis I describe a paradigm that addresses the need for complex cellular systems. The paradigm utilizes cultured neural networks (CNNs) that can be collected either from mouse models or derived from human induced stem cell models (hIPSCs). CNNs retain much of the electrical and network forming capabilities of the intact brain. CNNs plated onto multi-well microelectrode arrays (CNN-MEAs), which capture extracellular activity of electrically active cells, therefore offer a particularly appealing cellular system for the investigation of genetic variants that cause neurological disorders.

In chapter one I review the history of genetics and epilepsy. I discuss how studies of genetic variants that cause epilepsy give insights into the mechanisms of a wide scope of neurological disorders. I suggest that epilepsy is therefore a good place to start in the development of cellular models of disease and targeted therapeutic options. I next introduce the MEA as a platform capable of capturing important electrophysiological data from CNNs, creating the foundations for chapters two and three.

Chapter two describes one application of the CNN-MEA paradigm in which we inhibited microRNA (miRNA) expression in vitro and evaluated the resulting activity profiles. MiRNAs are increasingly linked to epileptogenesis. We show that small differences in miRNA expression can have large effects on network activity. Chapter two offers a proof-of-principle of the utility of the CNN-MEA paradigm in capturing pathogenic hyperexcitability.

Chapter three discusses a second application in which mutations in the ATP1A3 gene were evaluated. Mutations in ATP1A3 cause at least four distinct disorders and it is not yet fully understood how mutations mediate pathophysiologic consequences. We first investigate ATP1A3 mutations in COS7 cells and observe no clear differences. We next evaluate the effect of two mutations that cause the most severe ATP1A3-associated disorder, Alternating Hemiplegia of Childhood (AHC), on network dynamics. We show that mutant cultures demonstrate hypersynchronous activity and distorted bursting properties when compared to wild-type. Using strategic pharmacological manipulation, we illustrate the role of GABA neurotransmission on aberrant network dynamics and further show the partial rescue of activity phenotypes using adenosine triphosphate (ATP) and an anti-epileptic drug. Chapter three illustrates the shortcomings of heterologous cell modeling and provides additional support for the use of CNN-MEAs to study genetic variation.

The CNN-MEA paradigm provides a promising method to evaluate the effect of mutations that cause neurological disorders. Furthermore, with the use of multi-well MEAs, this paradigm provides a scalable option to evaluate multiple parameters simultaneously. Understanding the functional impact of genetic variation using the CNN-MEA paradigm is a crucial step to developing targeted therapeutics.