Evidence map›Paper›PMID 41741449›Full record

ArticleNature communications2026

Mistranslation suppresses mistranscription in eukaryotes.

Xiaoyi Zhang, Gongwang Yu, Ziyan Guo, Jia Liao, Weiyi Li, Weijie Zhang, Boyang Zheng, Zhuoxing Wu, Shuya Peng, Dahui Tan and 2 more

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

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

12 authors.

Xiaoyi ZhangDepartment of Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
Gongwang YuThe Affiliated Dongguan Songshan Lake Central Hospital, Guangdong Medical University, Dongguan, China.
Ziyan GuoDepartment of Medical Genetics and Bioinformatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
Jia LiaoDepartment of Medical Genetics and Bioinformatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.ORCID http://orcid.org/0009-0007-6097-4674
Weiyi LiDepartment of Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
Weijie ZhangDepartment of Medical Genetics and Bioinformatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
Boyang ZhengDepartment of Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
Zhuoxing WuDepartment of Medical Genetics and Bioinformatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
Shuya PengDepartment of Medical Genetics and Bioinformatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
Dahui TanDepartment of Medical Genetics and Bioinformatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
Jian-Rong YangDepartment of Medical Genetics and Bioinformatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China. yangjianrong@mail.sysu.edu.cn.ORCID http://orcid.org/0000-0002-7807-9455
Xiaoshu ChenDepartment of Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China. chenxshu3@mail.sysu.edu.cn.ORCID http://orcid.org/0000-0002-5779-5065

Funding

National Natural Science Foundation of China (National Science Foundation of China) 32222018National Natural Science Foundation of China (National Science Foundation of China) 32361133555
6 · The paper itself

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.

Indexed as

EukaryotaProtein BiosynthesisAnimalsComputer SimulationEpistasis, GeneticEvolution, MolecularMutation

Identifiers

PMID41741449
PMCPMC13047054

What OpenQuestion holds

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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.