[论文解读] A Tridomain Model for Potassium Clearance in Optic Nerve

Yi Zhu, Shixin Xu+2 more|arXiv (Cornell University)|Dec 6, 2020

Neuroscience and Neuropharmacology Research参考文献 77被引用 4

一句话总结

本文提出了一种三域电缆模型，用于描述视神经中钾离子的清除过程，该模型综合考虑了胶质细胞、细胞外空间和轴突之间通过扩散、对流和电化学扩散的作用。模型表明，胶质细胞通过渗透压驱动的对流流体主要清除钾离子，突显了在简单模型中常被忽视的纵向流动的关键作用。

ABSTRACT

The accumulation of potassium in the narrow space outside nerve cells is a classical subject of biophysics that has received much attention recently. It may be involved in potassium accumulation include spreading depression, perhaps migraine and some kinds of epilepsy, even (speculatively) learning. Quantitative analysis is likely to help evaluate the role of potassium clearance from the extracellular space after a train of action potentials. Clearance involves three structures that extend down the length of the nerve: glia, extracellular space, and axon and so need to be described in the tradition of the 'cable equations' of nerve used to explain nerve conduction since the work of Hodgkin in 1937. A three-compartment model is proposed here for the optic nerve and is used to study the accumulation of potassium and its clearance. The model allows the convection, diffusion, and electrical migration of water and ions. We depend on the data of Orkand et al to ensure the relevance of our model and align its parameters with the anatomy and properties of membranes, channels, and transporters: our model fits their experimental data quite well. The aligned model shows that glia has an important role in buffering potassium, as expected. The model shows that potassium is cleared mostly by convective flow through the syncytia of glia driven by osmotic pressure differences. A simplified model might be possible, but it must involve flow down the length of the optic nerve. It is easy for compartment models to neglect this flow. Our model can be used for structures quite different from the optic nerve that might have different distributions of channels and transporters in its three compartments. It can be generalized to include the fourth compartment to deal with the glymphatic flow into the circulatory system.

研究动机与目标

• 开发视神经中动作电位活动后钾离子动力学的定量模型。
• 研究扩散、对流和电化学扩散在细胞外钾离子清除中的作用。
• 确定胶质细胞、细胞外空间和轴突在钾离子缓冲和清除中的贡献。
• 将模型参数与Orkand等人提供的实验数据对齐，以确保生物学相关性。
• 探讨纵向流动在准确模拟钾离子清除中的必要性。

提出的方法

• 为视神经构建一个三室电缆模型，分别代表胶质细胞、细胞外空间和轴突。
• 模型包含描述通过扩散、电迁移和对流进行离子传输的偏微分方程。
• 离子通量由能斯特-普朗克方程控制，膜特性与通道电导基于Orkand等人提供的数据推导。
• 渗透压差驱动胶质细胞合胞体中的对流流动，该流动被明确建模为纵向流动分量。
• 通过匹配Orkand等人提供的实验钾离子动力学数据，校准模型参数，以确保解剖学和生理学上的准确性。
• 该模型可推广至其他神经结构，并可通过增加第四个隔室来扩展以包含类淋巴液流入。

实验结果

研究问题

• RQ1 扩散、对流和电化学扩散在视神经钾离子清除中各自贡献如何？
• RQ2 胶质细胞在钾离子缓冲和清除中的相对贡献是什么？
• RQ3 纵向对流在准确模拟钾离子动力学中有多必要？
• RQ4 三域模型在多大程度上能再现实验测得的钾离子积累与清除数据？
• RQ5 该模型能否推广至具有不同离子通道和转运体分布的其他神经组织？

主要发现

• 胶质细胞在钾离子清除中起核心作用，主要通过流动而非被动扩散实现。
• 钾离子主要通过沿视神经长度方向、由渗透压驱动的胶质细胞合胞体对流清除。
• 该模型与Orkand等人提供的实验数据高度吻合，验证了其生理相关性。
• 忽略纵向流动的简化模型可能无法捕捉钾离子清除动力学的关键特征。
• 通过增加第四个隔室，该模型可扩展以包含类淋巴液流入，从而增强其在更广泛脑部清除机制中的适用性。
• 对流的引入对于准确表征钾离子动力学至关重要，挑战了传统隔室模型中的既有假设。