Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning
- Carnegie Mellon University, United States
Abstract
Upstream open reading frames (uORFs) are potent cis-acting regulators of mRNA translation and nonsense-mediated decay (NMD). While both AUG- and non-AUG initiated uORFs are ubiquitous in ribosome profiling studies, few uORFs have been experimentally tested. Consequently, the relative influences of sequence, structural, and positional features on uORF activity have not been determined. We quantified thousands of yeast uORFs using massively parallel reporter assays in wildtype and ∆upf1 yeast. While nearly all AUG uORFs were robust repressors, most non-AUG uORFs had relatively weak impacts on expression. Machine learning regression modeling revealed that both uORF sequences and locations within transcript leaders predict their effect on gene expression. Indeed, alternative transcription start sites highly influenced uORF activity. These results define the scope of natural uORF activity, identify features associated with translational repression and NMD, and suggest that the locations of uORFs in transcript leaders are nearly as predictive as uORF sequences.
Data availability
Sequencing data have been deposited in NCBI SRA under accession PRJNA721222.
Article and author information
Author details
Funding
National Institutes of Health (R01GM121895)
- Gemma E May
- Christina Akirtava
- Matthew Agar-Johnson
- Joel McManus
National Institutes of Health (R35GM145317)
- Gemma E May
- Christina Akirtava
- Joel McManus
National Institutes of Health (R01GM028301)
- Jelena Micic
- John Woolford
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, May et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning
eLife 12:e69611.
https://doi.org/10.7554/eLife.69611
