five

Selection and Characterization of Pre-mRNA Splicing Enhancers: Identification of Novel SR Protein-Specific Enhancer Sequences

收藏
PubMed Central2026-05-16 收录
下载链接:
https://pmc.ncbi.nlm.nih.gov/articles/PMC83964/
下载链接
链接失效反馈
官方服务:
资源简介:
Splicing enhancers are RNA sequences required for accurate splice site recognition and the control of alternative splicing. In this study, we used an in vitro selection procedure to identify and characterize novel RNA sequences capable of functioning as pre-mRNA splicing enhancers. Randomized 18-nucleotide RNA sequences were inserted downstream from a Drosophila doublesex pre-mRNA enhancer-dependent splicing substrate. Functional splicing enhancers were then selected by multiple rounds of in vitro splicing in nuclear extracts, reverse transcription, and selective PCR amplification of the spliced products. Characterization of the selected splicing enhancers revealed a highly heterogeneous population of sequences, but we identified six classes of recurring degenerate sequence motifs five to seven nucleotides in length including novel splicing enhancer sequence motifs. Analysis of selected splicing enhancer elements and other enhancers in S100 complementation assays led to the identification of individual enhancers capable of being activated by specific serine/arginine (SR)-rich splicing factors (SC35, 9G8, and SF2/ASF). In addition, a potent splicing enhancer sequence isolated in the selection specifically binds a 20-kDa SR protein. This enhancer sequence has a high level of sequence homology with a recently identified RNA-protein adduct that can be immunoprecipitated with an SRp20-specific antibody. We conclude that distinct classes of selected enhancers are activated by specific SR proteins, but there is considerable sequence degeneracy within each class. The results presented here, in conjunction with previous studies, reveal a remarkably broad spectrum of RNA sequences capable of binding specific SR proteins and/or functioning as SR-specific splicing enhancers.
提供机构:
Taylor & Francis
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作