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A quantitative RNA code for mRNA target selection by the germline fate determinant GLD‐1
Author(s) -
Wright Jane E,
Gaidatzis Dimos,
Senften Mathias,
Farley Brian M,
Westhof Eric,
Ryder Sean P,
Ciosk Rafal
Publication year - 2011
Publication title -
the embo journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.484
H-Index - 392
eISSN - 1460-2075
pISSN - 0261-4189
DOI - 10.1038/emboj.2010.334
Subject(s) - biology , rna binding protein , rna , transcriptome , caenorhabditis elegans , messenger rna , microrna , computational biology , regulation of gene expression , genetics , regulator , microbiology and biotechnology , gene expression , gene
RNA‐binding proteins (RBPs) are critical regulators of gene expression. To understand and predict the outcome of RBP‐mediated regulation a comprehensive analysis of their interaction with RNA is necessary. The signal transduction and activation of RNA (STAR) family of RBPs includes developmental regulators and tumour suppressors such as Caenorhabditis elegans GLD‐1, which is a key regulator of germ cell development. To obtain a comprehensive picture of GLD‐1 interactions with the transcriptome, we identified GLD‐1‐associated mRNAs by RNA immunoprecipitation followed by microarray detection. Based on the computational analysis of these mRNAs we generated a predictive model, where GLD‐1 association with mRNA is determined by the strength and number of 7‐mer GLD‐1‐binding motifs (GBMs) within UTRs. We verified this quantitative model both in vitro , by competition GLD‐1/GBM‐binding experiments to determine relative affinity, and in vivo , by ‘transplantation’ experiments, where ‘weak’ and ‘strong’ GBMs imposed translational repression of increasing strength on a non‐target mRNA. This study demonstrates that transcriptome‐wide identification of RBP mRNA targets combined with quantitative computational analysis can generate highly predictive models of post‐transcriptional regulatory networks.