Evidence map›Paper›PMID 8190621›Full record

ArticleNucleic acids research1994

Mutations to nonsense codons in human genetic disease: implications for gene therapy by nonsense suppressor tRNAs.

J Atkinson, R Martin

Open access · greenAbstract read
In one paragraph

Article in Nucleic acids research, 1994. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
0.7field-weighted citation impact, top 34% of its field
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

25 citing papers in PubMed, 58 citations in OpenAlex.

  1. Review
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  7. Review
  8. Suppression of Nonsense Mutations by New Emerging Technologies.International journal of molecular sciences · 2020
    Review
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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

2 authors at 1 institution in 1 country.

J AtkinsonKrebs Institute for Biomolecular Research, University of Sheffield, Western Bank, UK.
R Martin
University of Sheffield · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nonsense suppressor tRNAs have been suggested as potential agents for human somatic gene therapy. Recent work from this laboratory has described significant effects of 3' codon context on the efficiency of human nonsense suppressors. A rapid increase in the number of reports of human diseases caused by nonsense codons, prompted us to determine how the spectrum of mutation to either UAG, UAA or UGA codons and their respective 3' contexts, might effect the efficiency of human suppressor tRNAs employed for purposes of gene therapy. This paper presents a survey of 179 events of mutations to nonsense codons which cause human germline or somatic disease. The analysis revealed a ratio of approximately 1:2:3 for mutation to UAA, UAG and UGA respectively. This pattern is similar, but not identical, to that of naturally occurring stop codons. The 3' contexts of new mutations to stop were also analysed. Once again, the pattern was similar to the contexts surrounding natural termination signals. These results imply there will be little difference in the sensitivity of nonsense mutations and natural stop codons to suppression by nonsense suppressor tRNAs. Analysis of the codons altered by nonsense mutations suggests that efforts to design human UAG suppressor tRNAs charged with Trp, Gln, and Glu; UAA suppressors charged with Gln and Glu, and UGA suppressors which insert Arg, would be an essential step in the development of suppressor tRNAs as agents of human somatic gene therapy.

Indexed as

CodonGene Expression RegulationGenetic TherapyPoint MutationGenetic Diseases, InbornHumansRNA, TransferTerminator Regions, GeneticCodonRNA, Transfer

Identifiers

PMID8190621
PMCPMC307985
OpenAlexW1995616762

What OpenQuestion holds

Textmetadata
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.