The tripartite synapse during metabolic stress

Z项目将最先进的实验方法与高级计算建模相结合。基于在RU获得的实验结果，我们已经建立了代谢应激下的三方突触的生物物理模型。该模型包括一个突触前神经元和一个嵌入有限细胞外空间(ECS)的星形胶质细胞。各种跨膜电流及其能量依赖关系被表示为耦合的微分方程组，并用实验数据进行了校准。该模型忠实地再现了关键的实验观察，包括离子、体积和电生理动力学。我们发现，对瞬时能量剥夺的反应是双稳态的：神经元可以从短暂的ATP耗竭中恢复，并随着突触传递的恢复而重新极化，或者保持去极化状态，这取决于NKA泵的强度和ECS的大小。在第二个资助期，我们将通过加入重要的新元素来扩展这一模型，包括Na+依赖的酸碱转运体(NHE1和NBCe1)以及星形胶质细胞和神经元的动态水通透性，以探索容量调节。为了更详细地描述细胞内离子分布，将增加内体隔室，并增加突触后隔室，以更真实地模拟能量剥夺期间突触传递故障。此外，我们还将实现谷氨酸-谷氨酰胺并包括线粒体功能。为了模拟相互作用神经元的能量依赖活动，我们将添加抑制性突触，并随后基于我们的生物物理模型用神经质量描述种群活动。这一神经质量模型使我们能够模拟大脑节律(EEG)及其在代谢应激期间的变化。根据新的实验数据和先进的分支分析，我们将深入了解抑制性和兴奋性突触的选择性脆弱性，包括神经网络中三方突触的各种能量依赖过程。我们的计算模型还提供了一个平台来测试硅胶中的干预，从而为新的实验生成假设。最终，我们的模型和财团的实验数据将促进我们对许多神经疾病的临床现象学的理解，这些疾病中ATP的生成减少，包括中风、缺氧缺血性脑病、癫痫发作和线粒体疾病。

The Z-project connects state-of-the-art experimental approaches to advanced computational modelling. Based on experimental findings obtained in the RU, we have generated a biophysical model of the tripartite synapse under metabolic stress. The model includes a presynaptic neuron and an astrocyte embedded in a finite extracellular space (ECS). The various transmembrane currents and their energy dependency are expressed as coupled differential equations calibrated with experimental data. The model faithfully reproduces key experimental observations, including ion, volume and electrophysiological dynamics. We found that the response to transient energy deprivation is bi-stable: neurons can either recover from transient ATP depletion and repolarise with the restoration of synaptic transmission or remain in a depolarised state, depending on NKA pump strength and the size of the ECS. In the second Funding Period, we will extend this model by incorporating important new elements, including Na+-dependent acid-base transporters (NHE1 and NBCe1) and dynamic water permeabilities for astrocytes and neurons to explore volume regulation. Endosomal compartments will be added for a more detailed description of intracellular ion distributions and a postsynaptic compartment to simulate synaptic transmission failure during energy deprivation more faithfully. Moreover, we will implement the glutamate-glutamine and include mitochondrial function. To simulate the energy-dependent activity of interacting neurons, we will add inhibitory synapses and subsequently describe the population activity with a neural mass based on our biophysical model. This neural mass model enables us to simulate brain rhythms (EEG) and their changes during metabolic stress. Calibrating to new experimental data and using advanced bifurcation analysis, we will gain insight into the selective vulnerability of inhibitory and excitatory synapses, including the various energy-dependent processes at the tripartite synapse, within a neural network. Our computational model also provides a platform to test interventions in silico, thus generating hypotheses for new experiments. Ultimately, our model and the consortiums' experimental data will advance our understanding of the clinical phenomenology of many neurological diseases where ATP generation is reduced, including stroke, hypoxic-ischemic encephalopathy, seizures and mitochondriopathies.