ArticleGenome biology and evolution2020
High Transcriptional Error Rates Vary as a Function of Gene Expression Level.
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.
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Who cites it
13 citing papers in PubMed, 16 citations in OpenAlex.
- Functionality versus Adaptation: Insights from Transcriptional Adaptation.Journal of molecular evolution · 2026Review
- Wanted: A population genetic theory of biological noise regulation.PLoS genetics · 2026Article
- A narrow range of transcript-error rates across the Tree of Life.Science advances · 2025Article
- NMR measurements of transient low-populated tautomeric and anionic Watson-Crick-like G·T/U in RNA:DNA hybrids: implications for the fidelity of transcription and CRISPR/Cas9 gene editing.Nucleic acids research · 2024Article
- Half a Century of Controversy: The Neutralist/Selectionist Debate in Molecular Evolution.Genome biology and evolution · 2024Article
- Evolutionary conservation of the fidelity of transcription.Nature communications · 2023Article
- What Has Genomics Taught An Evolutionary Biologist?Genomics, proteomics & bioinformatics · 2023Review
- Gene product diversity: adaptive or not?Trends in genetics : TIG · 2022Review
- Phenotypic mutations contribute to protein diversity and shape protein evolution.Protein science : a publication of the Protein Society · 2022Review
- Stop Codon Usage as a Window into Genome Evolution: Mutation, Selection, Biased Gene Conversion and the TAG Paradox.Genome biology and evolution · 2022Article
- Pervasive sequence-level variation in the transcriptome ofNAR genomics and bioinformatics · 2022Article
- Effective Population Size Predicts Local Rates but Not Local Mitigation of Read-through Errors.Molecular biology and evolution · 2021Article
- Readthrough Errors Purge Deleterious Cryptic Sequences, Facilitating the Birth of Coding Sequences.Molecular biology and evolution · 2020Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
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.
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