Mistranslation suppresses mistranscription in eukaryotes

Phenotypic mutations include non-heritable sequence changes arising from synthetic errors such as mistranscription or mistranslation. Despite wide potential impact of both error types, their relationship remains underexplored. Using Circ-Seq and mass spectrometry data, we perform a genome-wide analysis of mistranscriptions and mistranslations in five model organisms, and find that genes with frequent mistranslations exhibit lower mistranscription rates. We hypothesize that this pattern is explainable by a negative epistasis between the two error types. We assess our hypothesis with systematic experimental measurements that confirm the prevalence of negative epistasis, as well as in silico evolutionary simulations that suggest the extra deleterious effects resulting from negative epistasis are sufficient to create selection against mistranscription in highly mistranslated genes. Moreover, empirical genomic analyses indicate that genes with frequent mistranslation purge nonsynonymous mistranscriptions more efficiently, and that highly translated transcripts show fewer mistranscriptions. Combined, our results reveal an unrecognized interaction between mistranscription and mistranslation.

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Publication Details

Journal
Nature Communications
Published
2026-02-25
DOI
https://doi.org/10.1038/s41467-026-69969-x
Primary Topic
Genomics and Chromatin Dynamics
Type
article
Field-Weighted Citation Impact
0.00

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article

Mistranslation suppresses mistranscription in eukaryotes

Xiaoshu Chen, Shuya Peng, Gongwang Yu, Zhuoxing Wu et al.
Nature Communications
Genomics and Chromatin Dynamics
article

Mistranslation suppresses mistranscription in eukaryotes

Xiaoshu Chen, Shuya Peng, Gongwang Yu, Zhuoxing Wu, Weiyi Li, Dahui Tan, Xiaoyi Zhang, Boyang Zheng, Weijie Zhang, Jian-Rong Yang, Jia Liao
article en

Abstract

Phenotypic mutations include non-heritable sequence changes arising from synthetic errors such as mistranscription or mistranslation. Despite wide potential impact of both error types, their relationship remains underexplored. Using Circ-Seq and mass spectrometry data, we perform a genome-wide analysis of mistranscriptions and mistranslations in five model organisms, and find that genes with frequent mistranslations exhibit lower mistranscription rates. We hypothesize that this pattern is explainable by a negative epistasis between the two error types. We assess our hypothesis with systematic experimental measurements that confirm the prevalence of negative epistasis, as well as in silico evolutionary simulations that suggest the extra deleterious effects resulting from negative epistasis are sufficient to create selection against mistranscription in highly mistranslated genes. Moreover, empirical genomic analyses indicate that genes with frequent mistranslation purge nonsynonymous mistranscriptions more efficiently, and that highly translated transcripts show fewer mistranscriptions. Combined, our results reveal an unrecognized interaction between mistranscription and mistranslation.

Nature Communications
Sun Yat-sen University (CN), Guangdong Medical College (CN), Songshan Lake Materials Laboratory (CN), Guangdong Provincial Center for Disease Control and Prevention (CN)
National Natural Science Foundation of China
Zero hunger
Openalex Percentile: Top 43%
Genomics and Chromatin Dynamics
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