Virtual presynaptic nerve terminal: a computational tool for studying synaptic transmitter release in health and disease

点击翻译按钮获取中文摘要

Synaptic transmission forms the basis of neuronal communication in the brain. When an action potential invades a presynaptic structure (known as a bouton or terminal) it depolarises the presynaptic membrane, which activates Ca2+ channels leading to an influx of Ca2+ ions into the nerve terminal. This Ca2+ influx triggers fast fusion of synaptic vesicles (SV) filled with neurotransmitters. Neurotransmitters quickly diffuse towards the postsynaptic neuron, where they bind to specific receptors and evoke further electrical and/or chemical signalling. The efficiency of the whole process is ensured by the precise timing of the Ca2+ signal and SV fusing. Although the general molecular mechanism of transmitter release is well established, the precise regulation of vesicular release process at different synapses remains incompletely resolved.The main difficulty studying this regulation at the level of single synapses is that the majority of presynaptic boutons in the brain are very small, and as a result the experimental techniques are confronted with serious limitations. Data-constrained realistic computational models of presynaptic structures are therefore essential tools that allow one to complement the limitations of experimental approaches and to quantitatively predict the behaviour of nerve terminals during physiological neuronal activity. At present, the use of computational models is impeded because of the absence of a unified modelling framework of the presynaptic terminal that would allow research laboratories with limited mathematical/computational expertise to implement a realistic model for their experimental data.In this project we propose to develop such a unified computational framework model of a presynaptic terminal, which will allow the neuroscience community to explore mechanisms of Ca2+-driven transmitter release that cannot be directly determined experimentally. We will use the powerful software platform Virtual Cell (http://vcell.org/) for implementing and simulating our three-dimensional computational model. The model will include the key functional presynaptic elements that are known to be important in shaping transmitter release dynamics.Different types of synapses in the central nervous system have diverse structural and molecular organisation that leads to their distinct functional properties. During the project we will apply our implemented framework to investigate a number of scientific questions in collaboration with a group of world-leading experimental laboratories (end-users) both in the UK and abroad. In particular, we will adapt our computational framework to model several canonical synapse-types and then will systematically study how individual presynaptic elements regulate synaptic transmitter release both in health and disease. Models in each project will be constrained and tuned using existing and novel experimental data from the end-user laboratories.We anticipate that our results will provide novel quantitative insights into the regulation of transmitter release and will have an immediate impact in facilitating the experimental work in the end-user laboratories. We will also apply the developed models to investigate in silico the mechanisms of presynaptic bouton dysfunctions in collaboration with clinical and experimental colleagues in UCL Queen Square Institute of Neurology. In particular, we will focus on presynaptic channelopathies - episodic neurological disorders caused by mutations in presynaptic ion channels (including some forms of migraine, epilepsy and ataxias). At the end of the project, our validated computational framework will be released to the public domain for the research community together with a user-friendly manual explaining how individual modelling blocks can be run, linked, and modified to address a particular research question. This will provide a powerful resource for other experimental laboratories, particularly the ones that lack modelling expertise.