Evidence map›Paper›PMID 41429784›Full record

ArticleNPJ systems biology and applications2025

Bistability in type I toxin-antitoxin systems may lead to stress-induced persister formation.

Sofija Markovic, Magdalena Djordjevic, Hong-Yu Ou, Marko Djordjevic

Abstract read
In one paragraph

Article in NPJ systems biology and applications, 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

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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. Review
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.

Sofija MarkovicQuantitative Biology Group, Faculty of Biology, University of Belgrade, Studentski trg 16, Belgrade, Serbia.
Magdalena DjordjevicInstitute of Physics Belgrade, University of Belgrade, Pregrevica 118, Belgrade, Serbia.
Hong-Yu OuState Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Marko DjordjevicQuantitative Biology Group, Faculty of Biology, University of Belgrade, Studentski trg 16, Belgrade, Serbia. dmarko@bio.bg.ac.rs.

Funding

National Natural Science Foundation of China 32370186Science Fund of the Republic of Serbia 7750294
6 · The paper itself

Abstract

Antibiotic persistence, characterized by a dormant subpopulation of bacterial cells that causes chronic and recurrent infections, remains poorly understood despite being recognized nearly a century ago. Toxin-antitoxin (TA) systems, which include a toxin and an antitoxin, are promising candidates for elucidating persister formation. We present the first theoretical model of persister formation driven by type I TA systems, in which the antitoxin is a small RNA molecule. Our analyses and simulations reveal two steady states-low toxin (normal growth) and high toxin (persistence)-with stochastic switching between them. Bistability requires both positive and negative feedback mediated by inhibition of antitoxin degradation. We derive stability diagrams that map mechanistic properties to system dynamics. The model suggests that while type I TA systems may not produce persisters under normal conditions, they can enter a bistable regime under stress, such as antibiotic exposure or nutrient limitation, leading to increased toxin expression or slower growth. Moreover, transiently slow-growing cells can be stabilized as long-living persisters through bistable TA dynamics. Using a cusp catastrophe surface, we identify distinct roles for two toxin inhibition mechanisms in modulating steady states and hysteresis. These findings provide a mechanistic basis for experimental observations and a framework for future studies.

Indexed as

AntitoxinsBacterial ToxinsStress, PhysiologicalToxin-Antitoxin SystemsAnti-Bacterial AgentsComputer SimulationModels, BiologicalAnti-Bacterial AgentsAntitoxinsBacterial Toxins

Identifiers

PMID41429784
PMCPMC12848306

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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.