亨廷顿病
细胞应激反应
战斗或逃跑反应
疾病
蛋白质折叠
神经科学
未折叠蛋白反应
蛋白质聚集
仿形(计算机编程)
生物
细胞生物学
化学
生物化学
医学
内科学
计算机科学
内质网
基因
操作系统
作者
Liliana M. Almeida,Ângela Oliveira,Jorge M.A. Oliveira,Brígida R. Pinho
标识
DOI:10.1016/j.abb.2023.109711
摘要
Stress response pathways like the integrated stress response (ISR), the mitochondrial unfolded protein response (UPRmt) and the heat shock response (HSR) have emerged as part of the pathophysiology of neurodegenerative diseases, including Huntington's disease (HD) - a currently incurable disease caused by the production of mutant huntingtin (mut-Htt). Previous data from HD patients suggest that ISR is activated while UPRmt and HSR are impaired in HD. The study of these stress response pathways as potential therapeutic targets in HD requires cellular models that mimic the activation status found in HD patients of such pathways. PC12 cells with inducible expression of the N-terminal fragment of mut-Htt are among the most used cell lines to model HD, however the activation of stress responses remains unclear in this model. The goal of this study is to characterize the activation of ISR, UPRmt and HSR in this HD cell model and evaluate if it mimics the activation status found in HD patients. We show that PC12 HD cell model presents reduced levels of Hsp90 and mitochondrial chaperones, suggesting an impaired activation or function of HSR and UPRmt. This HD model also presents increased levels of phosphorylated eIF2α, the master regulator of the ISR, but overall similar levels of ATF4 and decreased levels of CHOP - transcription factors downstream to eIF2α - in comparison to control, suggesting an initial activation of ISR. These results show that this model mimics the ISR activation and the impaired UPRmt and HSR found in HD patients. This work suggests that the PC12 N-terminal HD model is suitable for studying the role of stress response pathways in the pathophysiology of HD and for exploratory studies investigating the therapeutic potential of drugs targeting stress responses.
科研通智能强力驱动
Strongly Powered by AbleSci AI