Suppr超能文献

在体可视化内质网自噬和内质网结构。

Visualizing ER-phagy and ER architecture in vivo.

机构信息

International Institutes of Medicine, The Fourth Affiliated Hospital of Zhejiang University School of Medicine , Yiwu, China.

Department of Biochemistry, and Department of Cardiology of Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

出版信息

J Cell Biol. 2024 Dec 2;223(12). doi: 10.1083/jcb.202408061. Epub 2024 Nov 18.

Abstract

ER-phagy is an evolutionarily conserved mechanism crucial for maintaining cellular homeostasis. However, significant gaps persist in our understanding of how ER-phagy and the ER network vary across cell subtypes, tissues, and organs. Furthermore, the pathophysiological relevance of ER-phagy remains poorly elucidated. Addressing these questions requires developing quantifiable methods to visualize ER-phagy and ER architecture in vivo. We generated two transgenic mouse lines expressing an ER lumen-targeting tandem RFP-GFP (ER-TRG) tag, either constitutively or conditionally. This approach enables precise spatiotemporal measurements of ER-phagy and ER structure at single-cell resolution in vivo. Systemic analysis across diverse organs, tissues, and primary cultures derived from these ER-phagy reporter mice unveiled significant variations in basal ER-phagy, both in vivo and ex vivo. Furthermore, our investigation uncovered substantial remodeling of ER-phagy and the ER network in different tissues under stressed conditions such as starvation, oncogenic transformation, and tissue injury. In summary, both reporter models represent valuable resources with broad applications in fundamental research and translational studies.

摘要

内质网自噬是一种进化上保守的机制,对于维持细胞内稳态至关重要。然而,我们对内质网自噬和内质网网络如何在细胞亚型、组织和器官中变化的理解仍然存在很大的差距。此外,内质网自噬的病理生理学相关性仍未得到充分阐明。要解决这些问题,需要开发可量化的方法来在体内可视化内质网自噬和内质网结构。我们生成了两种表达内质网腔靶向串联 RFP-GFP(ER-TRG)标签的转基因小鼠系,要么是组成型的,要么是条件性的。这种方法能够在体内以单细胞分辨率精确地测量内质网自噬和内质网结构的时空变化。对这些内质网自噬报告小鼠的不同器官、组织和原代培养物进行的系统分析揭示了基础内质网自噬在体内和体外都存在显著的差异。此外,我们的研究还发现,在饥饿、致癌转化和组织损伤等应激条件下,不同组织中的内质网自噬和内质网网络会发生大量重塑。总之,这两个报告模型都是非常有价值的资源,在基础研究和转化研究中有广泛的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b50d/11575016/a9e4fd35a254/JCB_202408061_GA.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验