Evidence map›Paper›PMID 38902240›Full record

ArticleNature communications2024

Optimizing 5'UTRs for mRNA-delivered gene editing using deep learning.

Sebastian Castillo-Hair, Stephen Fedak, Ban Wang, Johannes Linder, Kyle Havens, Michael Certo, Georg Seelig

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

56 citing papers in PubMed.

  1. Review
  2. Review
  3. Towards mRNA therapeutics 2.0.Nature reviews. Drug discovery · 2026
    Review
  4. Transgene sequence codon optimization and composition determines replication competence of self-amplifying RNA.Molecular therapy : the journal of the American Society of Gene Therapy · 2026
    Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. Review
  10. Article
  11. Review
  12. Enhancement of Therapeutic mRNA Translation in Cellular Stress Conditions.International journal of molecular sciences · 2026
    Review
  13. Article
  14. Review
  15. Article
  16. Review
  17. Review
  18. Designing 5' UTR sequences improves the capacity of mRNA therapeutics in preclinical models of aging and obesity.Molecular therapy : the journal of the American Society of Gene Therapy · 2026
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sebastian Castillo-Hair *Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA.
Stephen Fedak *2seventy bio, Cambridge, MA, USA.
Ban Wang *Department of Biology, Stanford University, Stanford, CA, USA.ORCID 0000-0003-2907-6132
Johannes LinderPaul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.ORCID 0000-0003-2134-7292
Kyle Havens2seventy bio, Cambridge, MA, USA.
Michael Certo2seventy bio, Cambridge, MA, USA.
Georg SeeligDepartment of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA. gseelig@uw.edu.ORCID 0000-0002-3163-8782

Funding

Engineering cell type-specific splicing regulationR01GM149631 · NIGMS · UNIVERSITY OF WASHINGTON · PI Georg Seelig · 2023 to 2026
$1.5M
National Science Foundation (NSF) 2021552NIGMS NIH HHS R01 GM149631U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R01GM149631
6 · The paper itself

Abstract

mRNA therapeutics are revolutionizing the pharmaceutical industry, but methods to optimize the primary sequence for increased expression are still lacking. Here, we design 5'UTRs for efficient mRNA translation using deep learning. We perform polysome profiling of fully or partially randomized 5'UTR libraries in three cell types and find that UTR performance is highly correlated across cell types. We train models on our datasets and use them to guide the design of high-performing 5'UTRs using gradient descent and generative neural networks. We experimentally test designed 5'UTRs with mRNA encoding megaTAL

Indexed as

5' Untranslated RegionsDeep LearningGene EditingRNA, MessengerCell LineHEK293 CellsHumansPolyribosomesProtein Biosynthesis5' Untranslated RegionsRNA, Messenger

Identifiers

PMID38902240
PMCPMC11189900

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.