Evidence map›Paper›PMID 31841128›Full record

ArticleGenome biology and evolution2020

High Transcriptional Error Rates Vary as a Function of Gene Expression Level.

Kendra M Meer, Paul G Nelson, Kun Xiong, Joanna Masel

Open access · goldAbstract read
In one paragraph

Article in Genome biology and evolution, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
0.6field-weighted citation impact, top 32% 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

13 citing papers in PubMed, 16 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. What Has Genomics Taught An Evolutionary Biologist?Genomics, proteomics & bioinformatics · 2023
    Review
  8. Gene product diversity: adaptive or not?Trends in genetics : TIG · 2022
    Review
  9. Phenotypic mutations contribute to protein diversity and shape protein evolution.Protein science : a publication of the Protein Society · 2022
    Review
  10. Article
  11. Pervasive sequence-level variation in the transcriptome ofNAR genomics and bioinformatics · 2022
    Article
  12. Article
  13. 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

4 authors at 1 institution in 1 country.

Kendra M MeerDepartment of Ecology & Evolutionary Biology, University of Arizona.
Paul G NelsonDepartment of Ecology & Evolutionary Biology, University of Arizona.
Kun XiongDepartment of Molecular & Cellular Biology, University of Arizona.
Joanna MaselDepartment of Ecology & Evolutionary Biology, University of Arizona.
University of Arizona · US

Funding

THE CONVERSION OF NONCODING SEQUENCES INTO PROTEINSR01GM104040 · NIGMS · UNIVERSITY OF ARIZONA · PI CORDES, MATTHEW HJ, MASEL, JOANNA · 2013 to 2016
$839k
NIGMS NIH HHS R01 GM104040
6 · The paper itself

Abstract

Errors in gene transcription can be costly, and organisms have evolved to prevent their occurrence or mitigate their costs. The simplest interpretation of the drift barrier hypothesis suggests that species with larger population sizes would have lower transcriptional error rates. However, Escherichia coli seems to have a higher transcriptional error rate than species with lower effective population sizes, for example Saccharomyces cerevisiae. This could be explained if selection in E. coli were strong enough to maintain adaptations that mitigate the consequences of transcriptional errors through robustness, on a gene by gene basis, obviating the need for low transcriptional error rates and associated costs of global proofreading. Here, we note that if selection is powerful enough to evolve local robustness, selection should also be powerful enough to locally reduce error rates. We therefore predict that transcriptional error rates will be lower in highly abundant proteins on which selection is strongest. However, we only expect this result when error rates are high enough to significantly impact fitness. As expected, we find such a relationship between expression and transcriptional error rate for non-C→U errors in E. coli (especially G→A), but not in S. cerevisiae. We do not find this pattern for C→U changes in E. coli, presumably because most deamination events occurred during sample preparation, but do for C→U changes in S. cerevisiae, supporting the interpretation that C→U error rates estimated with an improved protocol, and which occur at rates comparable with E. coli non-C→U errors, are biological.

Indexed as

Transcription, GeneticEscherichia coliEscherichia coli ProteinsSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsSelection, GeneticEscherichia coli ProteinsSaccharomyces cerevisiae ProteinsCir-Seqnearly neutral theoryphenotypic mutationRNA editingweak selection

Identifiers

PMID31841128
PMCPMC6988749
OpenAlexW2995416274

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.