1. Genetics and Genomics

Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning

  1. Gemma E May
  2. Christina Akirtava
  3. Matthew Agar-Johnson
  4. Jelena Micic
  5. John Woolford
  6. Joel McManus  Is a corresponding author
  1. Carnegie Mellon University, United States
https://doi.org/10.7554/eLife.69611
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  1. Gemma E May
  2. Christina Akirtava
  3. Matthew Agar-Johnson
  4. Jelena Micic
  5. John Woolford
  6. Joel McManus
(2023)
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
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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.

The following data sets were generated

Article and author information

Author details

  1. Gemma E May

    Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.

  2. Christina Akirtava

    Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. Matthew Agar-Johnson

    Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Jelena Micic

    Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. John Woolford

    Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.

  6. Joel McManus

    Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, United States
    For correspondence
    mcmanus@andrew.cmu.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6605-2642

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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  1. Gemma E May
  2. Christina Akirtava
  3. Matthew Agar-Johnson
  4. Jelena Micic
  5. John Woolford
  6. Joel McManus
(2023)
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

Share this article

https://doi.org/10.7554/eLife.69611

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