A computational model of loss of dopaminergic cells in Parkinson’s disease due to glutamate-induced excitotoxicity

Abstract

AbstractParkinson’s disease (PD) is a neurodegenerative disease associated with progressive and inexorable loss of dopaminergic cells in Substantia Nigra pars compacta (SNc). A full understanding of the underlying pathogenesis of this cell loss is unavailable, though a number of mechanisms have been indicated in the literature. A couple of these mechanisms, however, show potential for the development of radical and promising PD therapeutics. One of these mechanisms is the peculiar metabolic vulnerability of SNc cells by virtue of their excessive energy demands; the other is the excitotoxicity caused by excessive glutamate release onto SNc by an overactive Subthalamic Nucleus (STN). To investigate the latter hypothesis computationally, we developed a spiking neuron network model of the SNc-STN-GPe system. In the model, prolonged stimulation of SNc cells by an overactive STN leads to an increase in a ‘stress’ variable; when the stress in a SNc neuron exceeds a stress threshold the neuron dies. The model shows that the interaction between SNc and STN involves a positive feedback due to which, an initial loss of SNc cells that crosses a threshold causes a runaway effect that leads to an inexorable loss of SNc cells, strongly resembling the process of neurodegeneration. The model further suggests a link between the two aforementioned PD mechanisms: metabolic vulnerability and glutamate excitotoxicity. Our simulation results show that the excitotoxic cause of SNc cell loss in PD might be initiated by weak excitotoxicity mediated by energy deficit, followed by strong excitotoxicity, mediated by a disinhibited STN. A variety of conventional therapies are simulated in the model to test their efficacy in slowing down or arresting SNc cell loss. Among the current therapeutics, glutamate inhibition, dopamine restoration, subthalamotomy and deep brain stimulation showed superior neuroprotective effects in the proposed model.