BMC GenomicsMolecular BiologyPeer-reviewed journal article
Translation of upstream open reading frames in a model of neuronal differentiation
Caitlin M. RodrigueziD, S.Y. Chun, Ryan E. MillsiD, Peter K. ToddiD
Abstract
Upstream open reading frames (uORFs) initiate translation within mRNA 5 leaders, and have the potential to alter main coding sequence (CDS) translation on transcripts in which they reside. Ribosome profiling (RP) studies suggest that translating ribosomes are pervasive within 5 leaders across model systems. However, the significance of this observation remains unclear. To explore a role for uORF usage in neuronal differentiation, we performed RP on undifferentiated and differentiated human neuroblastoma cells. Using a spectral coherence algorithm (SPECtre), we identify 4,954 uORFs across 31% of all neuroblastoma transcripts. These uORFs predominantly utilize non-AUG initiation codons and exhibit translational efficiencies (TE) comparable to annotated coding regions. Usage of both AUG initiated uORFs and a conserved and consistently translated subset of non-AUG initiated uORFs correlates with repressed CDS translation. Ribosomal protein transcripts are enriched in uORFs, and select uORFs on such transcripts were validated for expression. With neuronal differentiation, we observed an overall positive correlation between translational shifts in uORF/CDS pairs. However, a subset of transcripts exhibit inverse shifts in translation of uORF/CDS pairs. These uORFs are enriched in AUG initiation sites, non-overlapping, and shorter in length.\n\nCumulatively, CDSs downstream of uORFs characterized by persistent translation show smaller shifts in TE with neuronal differentiation relative to CDSs without a predicted uORF, suggesting that fluctuations in CDS translation are buffered by uORF translation. In sum, this work provides insights into the dynamic relationships and potential regulatory functions of uORF/CDS pairs in a model of neuronal differentiation.
- organism: human
- assay: Ribo-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 40
- FWCI
- 1.99
- Field percentile
- top 13%
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