Evidence map›Paper›PMID 39777459›Full record

ArticleNucleic acids research2025

Defining serine tRNA knockout as a strategy for effective repression of gene expression in organisms with a recoded genome.

Peter J Voorhees, Xinyou Chang, Samuel K Lai

Abstract read
In one paragraph

Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Peter J VoorheesDivision of Pharmacoengineering and Molecular Pharmaceutics, The University of North Carolina at Chapel Hill, 125 Mason Farm Rd. Chapel Hill, NC 27599, USA.
Xinyou ChangDivision of Pharmacoengineering and Molecular Pharmaceutics, The University of North Carolina at Chapel Hill, 125 Mason Farm Rd. Chapel Hill, NC 27599, USA.
Samuel K LaiDivision of Pharmacoengineering and Molecular Pharmaceutics, The University of North Carolina at Chapel Hill, 125 Mason Farm Rd. Chapel Hill, NC 27599, USA.ORCID 0000-0003-4721-528X

Funding

David and Lucile Packard Foundation 2013-39274UNC Eshelman School of Pharmacy
6 · The paper itself

Abstract

Whole genome codon compression-the reassignment of all instances of a specific codon to synonymous codons-can generate organisms capable of tolerating knockout of otherwise essential transfer RNAs (tRNAs). As a result, such knockout strains enable numerous unique applications, such as high-efficiency production of DNA encoding extremely toxic genes or non-canonical proteins. However, achieving stringent control over protein expression in these organisms remains challenging, particularly with proteins where incomplete repression results in deleterious phenotypes. One platform enjoying increasing popularity utilizes serine TCA codon compression, relying on the prevailing understanding that deletion of tRNASer(UGA) (serT) would render the serine codon compressed organism incapable of translating any genes containing TCA codons. Here, we report evidence that tRNASer(CGA) (serU) can, surprisingly, also decode TCA, thereby precluding complete control over expression of TCA-containing genes in organisms with serT deletion. We then demonstrate the conditions necessary, including the precise modifications to the GRO and codon usage within the transgene, to overcome this interaction and achieve exceptionally stringent control over protein expression. Our findings provide critical insights and corresponding methods for guiding future use of serine codon compression for absolute control over protein expression, as well as a general strategy for optimizing repression via compression of other codons.

Indexed as

Codon UsageRNA, Transfer, SerCodonEscherichia coliGene Knockout TechniquesProtein BiosynthesisSaccharomyces cerevisiaeSerineCodonRNA, Transfer, SerSerine

Identifiers

PMID39777459
PMCPMC11705081

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.