Evidence map›Paper›PMID 41123205›Full record

ArticleNucleic acids research2025

MazF endoribonuclease promotes resolution of transcription-replication conflicts at ribosomal RNA genes in Escherichia coli.

Sebastien Fleurier, Ivan Matic

Abstract read
In one paragraph

Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Sebastien FleurierUniversité Paris Cité, CNRS, Inserm, Institut Cochin, F-75014 Paris, France.ORCID 0000-0003-2334-7835
Ivan MaticUniversité Paris Cité, CNRS, Inserm, Institut Cochin, F-75014 Paris, France.ORCID 0000-0003-0393-1241

Funding

Agence Nationale de la Recherches ANR-21-CE12-006-01Agence Nationale de la Recherches ANR-24-CE12-7386-01Agence Nationale de la Recherches DREAM ANR-20-PAMR-0002
6 · The paper itself

Abstract

Toxins from toxin-antitoxin (TA) systems, widely distributed genetic elements in prokaryotic genomes, are traditionally viewed as growth inhibitors. This study challenges this view by uncovering a growth-promoting function for MazF, the toxin component of the Escherichia coli MazF/MazE TA system. MazF is a sequence-specific endoribonuclease that cleaves single-stranded RNA, including ribosomal RNA (rRNA) precursors. While ectopic overexpression of MazF causes growth arrest, we show here that, when expressed at its native expression levels, it alleviates both co-directional and head-on transcription-replication conflicts at highly transcribed rRNA operons. By resolving these conflicts, MazF mitigates DNA damage, reduces cell mortality, and ultimately facilitates growth resumption. This function is particularly critical during the exit from stationary phase, when the rRNA copy operon number is low or when ribosome integrity is compromised. We further demonstrate that MazF is active, indicating its release from MazE antitoxin control, during regrowth from stationary phase, heat shock at 50°C, and colistin treatment. Together, these findings reveal an unrecognized role of TA systems in promoting cellular resilience, maintaining genome stability, and enabling recovery from stress, thereby providing new insights into their complex biological functions.

Indexed as

DNA-Binding ProteinsDNA ReplicationEndoribonucleasesEscherichia coliEscherichia coli ProteinsRNA, RibosomalTranscription, GeneticDNA DamageGene Expression Regulation, BacterialOperonrRNA OperonToxin-Antitoxin SystemsDNA-Binding ProteinsEndoribonucleasesEscherichia coli ProteinsMazE protein, E coliMazF protein, E coliRNA, Ribosomal

Identifiers

PMID41123205
PMCPMC12541390

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Registered trials

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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.