Computational modelling of the glycinergic synapse
Glycine is the simplest and smallest amino acid present in human proteins. It has many important roles in human as an intermediate of biosynthetic one carbon metabolism pathways, component of structural proteins, and in the brain acting as the second major inhibitory neurotransmitter via the activation of the glycine receptor (GlyR). Glycinergic neurons are present in various brain regions including the forebrain and the retina, although highest densities can be found in the lower brain regions of the central nervous system (CNS) such as the brain stem, where glycine is important in the regulation of muscle contraction, sensory inputs, and others. Given its diverse role, dysregulation in glycine biosynthesis and neurotranmission is implicated in numerous conditions detrimental to human health such as some subtypes of glioma, as well as genetic diseases caused by mutations to the components of the glycinergic synapse, for instance nonketotic hyperglycinemia (NHK) which leads to severe neurological problems. Moreover, the control of glycine concentration time course via the inhibition of glycine reuptake has been suggested as a potential novel treatment for several conditions, including schizophrenia, alcoholism, and others. In order to advance the understanding of glycine regulation in the human CNS, computational modelling of the various aspects of the glycinergic synapse were deployed. A metabolic model of the CNS was constructed with the use of publicly available databases as a derivation of a generic model of human metabolism, Recon2.2. The experimentally determined value of the human oxygen to glucose index (OGI), indicating glucose brain consumption was used to validate the model via the estimation of the background ATP demand which was similar to that reported in the scientic literature. In order to further contextualise the model to represent CNS metabolism, a method for the integration of single cell RNA-sequencing dataset containing the expression values of different types of CNS cells was developed. Flux Balance Analysis (FBA) methods were applied to the model in order to establish the most likely pathways of glycine and ATP production across dierent CNS cell types, suggesting the compartmentalization of the serine/glycine metabolism in CNS cells. Furthermore, FBA was used to assess whether serine or glycine can be used as main substrates in increasing cellular ATP and biosynthetic demands, with the results suggesting that serine can be used for ATP formation by the mitochondria via its breakdown to glycine, producing NADH for use in the Electron Transport Chain (ETC). Interactions between proteins composing the glycinergic synapse, including the glycine transporter proteins 1 and 2 (GlyT1, GlyT2) which are responsible for the termination of signal via glycine reuptake determine the time course of glycine released upon stimulation, and therefore the inhibitory post-synaptic currents (IPSC) Cl- currents via GlyR. For the purpose of increasing the understanding of the events shaping the glycine time course, a kinetic model of the synapse was developed integrating previous experimental observations. The model was analysed by looking at the effects of changing its parameters on the resulting amplitudes and decay time constants of glycine and the resulting IPSC. In addition, electrophysiological two electrode voltage clamp (TEVC) techniques were used in order to characterize the effects of a newly identied NKH-causing mutation at position Ser 407 on GlyT1 expressed in Xenopus laevis oocytes. The mutation lead to the inhibition of transport via the GlyT1 as compared to wild type transporter, and the result was integrated into the kinetic synapse model suggesting that the loss of GlyT1 function leads to a higher maximum concentration of glycine which persists in the synapse for longer, leading to an increased time course of IPSC at the postsynaptic neuron.
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