Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA.
Genes Dev. 2010 Apr 1;24(7):625-35. doi: 10.1101/gad.1907710.
Post-transcriptional regulatory mechanisms have emerged as a critical component underlying the diversification and spatiotemporal control of the proteome during the establishment of precise neuronal connectivity. These mechanisms have been shown to be important for virtually all stages of assembling a neural network, from neurite guidance, branching, and growth to synapse morphogenesis and function. From the moment a gene is transcribed, it undergoes a series of post-transcriptional regulatory modifications in the nucleus and cytoplasm until its final deployment as a functional protein. Initially, a message is subjected to extensive structural regulation through alternative splicing, which is capable of greatly expanding the protein repertoire by generating, in some cases, thousands of functionally distinct isoforms from a single gene locus. Then, RNA packaging into neuronal transport granules and recognition by RNA-binding proteins and/or microRNAs is capable of restricting protein synthesis to selective locations and under specific input conditions. This ability of the post-transcriptional apparatus to expand the informational content of a cell and control the deployment of proteins in both spatial and temporal dimensions is a feature well adapted for the extreme morphological properties of neural cells. In this review, we describe recent advances in understanding how post-transcriptional regulatory mechanisms refine the proteomic complexity required for the assembly of intricate and specific neural networks.
转录后调控机制已成为在建立精确神经元连接过程中蛋白质组多样化和时空控制的关键组成部分。这些机制对于组装神经网络的几乎所有阶段都很重要，包括神经突导向、分支和生长，以及突触形态发生和功能。从基因转录的那一刻起，它就在细胞核和细胞质中经历一系列转录后调控修饰，直到最终作为功能性蛋白质发挥作用。最初，mRNA 通过可变剪接经历广泛的结构调控，这能够通过从单个基因座产生数千种具有不同功能的异构体，极大地扩展蛋白质组。然后，RNA 包装到神经元运输颗粒中，并被 RNA 结合蛋白和/或 microRNA 识别，从而能够将蛋白质合成限制在特定位置和特定输入条件下。转录后装置扩展细胞信息内容并控制蛋白质在空间和时间维度上的部署的这种能力，非常适合神经细胞的极端形态特性。在这篇综述中，我们描述了理解转录后调控机制如何细化组装复杂和特定神经网络所需的蛋白质组复杂性的最新进展。
