Pole of Activity Data Sciences and Data Management, Institut de Recherches Servier (IdRS), Croissy-sur-Seine, France.
Pole of Activity Cellular Sciences, Institut de Recherches Servier (IdRS), Croissy-sur-Seine, France.
PLoS Comput Biol. 2022 Jun 27;18(6):e1010236. doi: 10.1371/journal.pcbi.1010236. eCollection 2022 Jun.
Microtubules and their post-translational modifications are involved in major cellular processes. In severe diseases such as neurodegenerative disorders, tyrosinated tubulin and tyrosinated microtubules are in lower concentration. We present here a mechanistic mathematical model of the microtubule tyrosination cycle combining computational modeling and high-content image analyses to understand the key kinetic parameters governing the tyrosination status in different cellular models. That mathematical model is parameterized, firstly, for neuronal cells using kinetic values taken from the literature, and, secondly, for proliferative cells, by a change of two parameter values obtained, and shown minimal, by a continuous optimization procedure based on temporal logic constraints to formalize experimental high-content imaging data. In both cases, the mathematical models explain the inability to increase the tyrosination status by activating the Tubulin Tyrosine Ligase enzyme. The tyrosinated tubulin is indeed the product of a chain of two reactions in the cycle: the detyrosinated microtubule depolymerization followed by its tyrosination. The tyrosination status at equilibrium is thus limited by both reaction rates and activating the tyrosination reaction alone is not effective. Our computational model also predicts the effect of inhibiting the Tubulin Carboxy Peptidase enzyme which we have experimentally validated in MEF cellular model. Furthermore, the model predicts that the activation of two particular kinetic parameters, the tyrosination and detyrosinated microtubule depolymerization rate constants, in synergy, should suffice to enable an increase of the tyrosination status in living cells.
微管及其翻译后修饰参与了主要的细胞过程。在神经退行性疾病等严重疾病中，酪氨酸化微管和酪氨酸化微管的浓度较低。在这里，我们提出了一个微管酪氨酸化循环的机制数学模型，该模型结合了计算建模和高内涵图像分析，以了解控制不同细胞模型中酪氨酸化状态的关键动力学参数。该数学模型首先使用文献中获得的动力学值对神经元细胞进行参数化，其次对增殖细胞进行参数化，通过连续优化程序改变两个参数值，该程序基于时间逻辑约束来形式化实验高内涵成像数据。在这两种情况下，数学模型都解释了通过激活微管酪氨酸连接酶无法增加酪氨酸化状态的原因。实际上，酪氨酸化微管是循环中两个反应的产物：去酪氨酸化微管解聚后再酪氨酸化。因此，酪氨酸化状态在平衡时受到两个反应速率的限制，仅激活酪氨酸化反应是无效的。我们的计算模型还预测了抑制微管羧肽酶酶的效果，我们在 MEF 细胞模型中进行了实验验证。此外，该模型还预测，两个特定的动力学参数（酪氨酸化和去酪氨酸化微管解聚速率常数）的协同激活，应该足以使活细胞中的酪氨酸化状态增加。
