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Modelling the Role of UCH-L1 on Protein Aggregation in Age-Related Neurodegeneration

机译:模拟UCH-L1在与年龄相关的神经变性中蛋白质聚集中的作用

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摘要

Overexpression of the de-ubiquitinating enzyme UCH-L1 leads to inclusion formation in response to proteasome impairment. These inclusions contain components of the ubiquitin-proteasome system and α-synuclein confirming that the ubiquitin-proteasome system plays an important role in protein aggregation. The processes involved are very complex and so we have chosen to take a systems biology approach to examine the system whereby we combine mathematical modelling with experiments in an iterative process. The experiments show that cells are very heterogeneous with respect to inclusion formation and so we use stochastic simulation. The model shows that the variability is partly due to stochastic effects but also depends on protein expression levels of UCH-L1 within cells. The model also indicates that the aggregation process can start even before any proteasome inhibition is present, but that proteasome inhibition greatly accelerates aggregation progression. This leads to less efficient protein degradation and hence more aggregation suggesting that there is a vicious cycle. However, proteasome inhibition may not necessarily be the initiating event. Our combined modelling and experimental approach show that stochastic effects play an important role in the aggregation process and could explain the variability in the age of disease onset. Furthermore, our model provides a valuable tool, as it can be easily modified and extended to incorporate new experimental data, test hypotheses and make testable predictions.
机译:去泛素化酶UCH-L1的过表达导致对蛋白酶体损伤的包涵体形成。这些内含物包含泛素-蛋白酶体系统和α-突触核蛋白的成分,这证实了泛素-蛋白酶体系统在蛋白质聚集中起重要作用。所涉及的过程非常复杂,因此我们选择采用系统生物学方法来检查系统,从而在迭代过程中将数学建模与实验结合起来。实验表明,细胞在夹杂物形成方面非常不均一,因此我们使用随机模拟。该模型表明,变异性部分是由于随机效应,也取决于细胞内UCH-L1的蛋白质表达水平。该模型还表明,甚至在没有任何蛋白酶体抑制作用出现之前,聚集过程就可以开始,但是蛋白酶体抑制作用大大加快了聚集进程。这导致蛋白质降解效率降低,因此聚集更多,表明存在恶性循环。但是,蛋白酶体抑制不一定是引发事件。我们的组合建模和实验方法表明,随机效应在聚集过程中起着重要作用,并且可以解释疾病发作年龄的变异性。此外,我们的模型提供了有价值的工具,因为可以轻松地对其进行修改和扩展,以纳入新的实验数据,检验假设并做出可检验的预测。

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