bioRxivGenomicsPreprint
An homeotic post-transcriptional network controlled by the RNA-binding protein RBMX
Paola Zuccotti, Daniele PeroniiD, Valentina Potrich, Alessandro QuattroneiD, Erik DassiiD
Abstract
Post-transcriptional regulation (PTR) of gene expression is a powerful determinant of protein levels and cellular phenotypes. The 5 and 3 untranslated regions of the mRNA (UTRs) mediate this role through sequence and secondary structure elements bound by RNA-binding proteins (RBPs) and noncoding RNAs. While functional regions in the 3UTRs have been extensively studied, the 5UTRs are still relatively uncharacterized. To fill this gap, here we used a computational approach based on phylogenetic conservation to identify hyper-conserved elements in human 5UTRs (5HCEs). Our assumption, supported by the recovery of functionally characterized elements, was that 5HCEs would represent evolutionarily stable and hence important PTR sites.\n\nWe identified over 5000 short, clustered 5HCEs occurring in approximately 10% of human protein-coding genes. Among these, homeotic genes were highly enriched. Indeed, 52 of the 258 characterized homeotic genes contained at least one 5HCE, including members of all four Hox clusters and several other families. Homeotic genes are essential transcriptional regulators. They drive body plan and neuromuscular development, and the role of PTR in their expression is mostly unknown. By integrating computational and experimental approaches we then identified the RBMX RNA-binding protein as the initiator of a post-transcriptional cascade regulating many such homeotic genes. RBMX is known to control its targets by modulating transcript abundance and alternative splicing. Adding to that, we observed translational control as a novel mode of regulation by this RBP.\n\nThis work thus establishes RBMX as a versatile master controller of homeotic genes and of the developmental processes they drive.
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
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