泛素
泛素连接酶
蛋白质降解
蛋白酶体
泛素蛋白连接酶类
细胞生物学
肌肉萎缩
骨骼肌
生物
泛素结合酶
浪费的
生物化学
蛋白质水解
酶
解剖
基因
作者
David C. Hughes,Craig A. Goodman,Leslie M. Baehr,Paul Gregorevic,Sue C. Bodine
出处
期刊:American Journal of Physiology-cell Physiology
[American Physiological Society]
日期:2023-12-01
卷期号:325 (6): C1567-C1582
被引量:3
标识
DOI:10.1152/ajpcell.00457.2023
摘要
Ubiquitination is an important post-translational modification (PTM) for protein substrates, whereby ubiquitin is added to proteins through the coordinated activity of activating (E1), ubiquitin-conjugating (E2), and ubiquitin ligase (E3) enzymes. The E3s provide key functions in the recognition of specific protein substrates to be ubiquitinated and aid in determining their proteolytic or nonproteolytic fates, which has led to their study as indicators of altered cellular processes. MuRF1 and MAFbx/Atrogin-1 were two of the first E3 ubiquitin ligases identified as being upregulated in a range of different skeletal muscle atrophy models. Since their discovery, the expression of these E3 ubiquitin ligases has often been studied as a surrogate measure of changes to bulk protein degradation rates. However, emerging evidence has highlighted the dynamic and complex regulation of the ubiquitin proteasome system (UPS) in skeletal muscle and demonstrated that protein ubiquitination is not necessarily equivalent to protein degradation. These observations highlight the potential challenges of quantifying E3 ubiquitin ligases as markers of protein degradation rates or ubiquitin proteasome system (UPS) activation. This perspective examines the usefulness of monitoring E3 ubiquitin ligases for determining specific or bulk protein degradation rates in the settings of skeletal muscle atrophy. Specific questions that remain unanswered within the skeletal muscle atrophy field are also identified, to encourage the pursuit of new research that will be critical in moving forward our understanding of the molecular mechanisms that govern protein function and degradation in muscle.
科研通智能强力驱动
Strongly Powered by AbleSci AI