Suppr超能文献

树突中 mRNP 运输的统计建模:β-actin 和 Arc mRNP 动力学的比较分析。

Statistical modeling of mRNP transport in dendrites: A comparative analysis of β-actin and Arc mRNP dynamics.

机构信息

Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minneapolis, USA.

Department of Physics, Pohang University of Science and Technology, Pohang, Republic of Korea.

出版信息

Traffic. 2023 Nov;24(11):522-532. doi: 10.1111/tra.12913. Epub 2023 Aug 6.

Abstract

Localization of messenger RNA (mRNA) in dendrites is crucial for regulating gene expression during long-term memory formation. mRNA binds to RNA-binding proteins (RBPs) to form messenger ribonucleoprotein (mRNP) complexes that are transported by motor proteins along microtubules to their target synapses. However, the dynamics by which mRNPs find their target locations in the dendrite have not been well understood. Here, we investigated the motion of endogenous β-actin and Arc mRNPs in dissociated mouse hippocampal neurons using the MS2 and PP7 stem-loop systems, respectively. By evaluating the statistical properties of mRNP movement, we found that the aging Lévy walk model effectively describes both β-actin and Arc mRNP transport in proximal dendrites. A critical difference between β-actin and Arc mRNPs was the aging time, the time lag between transport initiation and measurement initiation. The longer mean aging time of β-actin mRNP (100 s) compared with that of Arc mRNP (30 s) reflects the longer half-life of constitutively expressed β-actin mRNP. Furthermore, our model also permitted us to estimate the ratio of newly generated and pre-existing β-actin mRNPs in the dendrites. This study offers a robust theoretical framework for mRNP transport, which provides insight into how mRNPs locate their targets in neurons.

摘要

信使 RNA(mRNA)在树突中的定位对于调节长期记忆形成过程中的基因表达至关重要。mRNA 与 RNA 结合蛋白(RBPs)结合形成信使核糖核蛋白(mRNP)复合物,该复合物由运动蛋白沿微管运输到其靶突触。然而,mRNP 如何在树突中找到其目标位置的动态过程尚未得到很好的理解。在这里,我们使用 MS2 和 PP7 茎环系统分别研究了内源性 β-肌动蛋白和 Arc mRNP 在分离的小鼠海马神经元中的运动。通过评估 mRNP 运动的统计特性,我们发现衰老的 Lévy 游走模型有效地描述了β-肌动蛋白和 Arc mRNP 在近端树突中的运输。β-肌动蛋白和 Arc mRNP 之间的一个关键区别是衰老时间,即运输起始和测量起始之间的时间滞后。β-肌动蛋白 mRNP 的平均衰老时间(100s)比 Arc mRNP 的长(30s),这反映了组成型表达的β-肌动蛋白 mRNP 的半衰期更长。此外,我们的模型还允许我们估计树突中新生和预先存在的β-肌动蛋白 mRNP 的比例。这项研究为 mRNP 运输提供了一个强大的理论框架,为 mRNPs 在神经元中定位其靶标提供了深入了解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6833/10946522/d5153300e9b3/TRA-24-522-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验