High-throughput identification of RNA localization elements in neuronal cells

Ankita Arora(University of Colorado Anschutz Medical Campus), Roberto Castro-Gutiérrez(University of Colorado Anschutz Medical Campus), Charlie Moffatt(University of Colorado Anschutz Medical Campus), Davide Eletto(ETH Zurich), Raquel Becker(University of Colorado Anschutz Medical Campus), Maya Brown(University of Colorado Anschutz Medical Campus), Andreas E. Moor(ETH Zurich), Holger A. Russ(University of Colorado Anschutz Medical Campus), J. Matthew Taliaferro(University of Colorado Anschutz Medical Campus)
Nucleic Acids Research
September 15, 2022
Cited by 55Open Access
Full Text

Abstract

Hundreds of RNAs are enriched in the projections of neuronal cells. For the vast majority of them, though, the sequence elements that regulate their localization are unknown. To identify RNA elements capable of directing transcripts to neurites, we deployed a massively parallel reporter assay that tested the localization regulatory ability of thousands of sequence fragments drawn from endogenous mouse 3' UTRs. We identified peaks of regulatory activity within several 3' UTRs and found that sequences derived from these peaks were both necessary and sufficient for RNA localization to neurites in mouse and human neuronal cells. The localization elements were enriched in adenosine and guanosine residues. They were at least tens to hundreds of nucleotides long as shortening of two identified elements led to significantly reduced activity. Using RNA affinity purification and mass spectrometry, we found that the RNA-binding protein Unk was associated with the localization elements. Depletion of Unk in cells reduced the ability of the elements to drive RNAs to neurites, indicating a functional requirement for Unk in their trafficking. These results provide a framework for the unbiased, high-throughput identification of RNA elements and mechanisms that govern transcript localization in neurons.


Related Papers

No related papers found

Powered by citation graph analysis