The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Regulation of phenotypic variability by a threshold-based mechanism underlies bacterial persistence
Open accessAbstract
In the face of antibiotics, bacterial populations avoid extinction by harboring a subpopulation of dormant cells that are largely drug insensitive. This phenomenon, termed “persistence,” is a major obstacle for the treatment of a number of infectious diseases. The mechanism that generates both actively growing as well as dormant cells within a genetically identical population is unknown. We present a detailed study of the toxin–antitoxin module implicated in antibiotic persistence of Escherichia coli . We find that bacterial cells become dormant if the toxin level is higher than a threshold, and that the amount by which the threshold is exceeded determines the duration of dormancy. Fluctuations in toxin levels above and below the threshold result in coexistence of dormant and growing cells. We conclude that toxin–antitoxin modules in general represent a mixed network motif that can serve to produce a subpopulation of dormant cells and to supply a mechanism for regulating the frequency and duration of growth arrest. Toxin–antitoxin modules thus provide a natural molecular design for implementing a bet-hedging strategy.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Stochastic Gene Expression in a Single Cell
Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441
Bacterial Persistence as a Phenotypic Switch
Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L., & Leibler, S. (2004) · Science · doi:10.1126/science.1099390 · Cited by 2,952 · cited 1×
“Persistence was shown to be due to an inherent bimodality of growth rates in bacterial populations, where the slow growth, or dormancy, of the persister bacteria protects them from the lethal action of antibiotics that are more potent against actively growing cells (16).”
BackgroundSelf-citationPersister cells, dormancy and infectious disease
Lewis, K. (2007) · Nature Reviews Microbiology · doi:10.1038/nrmicro1557 · Cited by 2,052
Growth Rate-Dependent Global Effects on Gene Expression in Bacteria
Klumpp, S., Zhang, Z., & Hwa, T. (2009) · Cell · doi:10.1016/j.cell.2009.12.001 · Cited by 703
Molecular Mechanisms of HipA-Mediated Multidrug Tolerance and Its Neutralization by HipB
Schumacher, M. A., Piro, K. M., Xu, W., Hansen, S., Lewis, K., & Brennan, R. G. (2009) · Science · doi:10.1126/science.1163806 · Cited by 351 · cited 2×
“However, even though the structure of the HipBA complex is known (27) and growth arrest is shown to be due to HipA toxicity, any account of persistence must include an explanation of phenotypic variability: How does the coexistence of two distinct growth phenotypes within a genetically uniform population come about? O…”
Background
Cited by
Distinguishing between resistance, tolerance and persistence to antibiotic treatment
Brauner, A., Fridman, O., Gefen, O., & Balaban, N. Q. (2016) · Nature Reviews Microbiology · doi:10.1038/nrmicro.2016.34 · Cited by 1,612
Self-citationDefinitions and guidelines for research on antibiotic persistence
Balaban, N. Q., Helaine, S., Lewis, K., Ackermann, M., Aldridge, B., Andersson, D. I., Brynildsen, M. P., Bumann, D., Camilli, A., Collins, J. J., et al. (2019) · Nature Reviews Microbiology · doi:10.1038/s41579-019-0196-3 · Cited by 1,313
Self-citationPersistent bacterial infections and persister cells
Fisher, R. A., Gollan, B., & Helaine, S. (2017) · Nature Reviews Microbiology · doi:10.1038/nrmicro.2017.42 · Cited by 1,243
Structure and dynamics of molecular networks: A novel paradigm of drug discovery
Csermely, P., Korcsmáros, T., Kiss, H. J. M., London, G., & Nussinov, R. (2013) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2013.01.016 · Cited by 892
Surviving as a Community: Antibiotic Tolerance and Persistence in Bacterial Biofilms
Yan, J., & Bassler, B. L. (2019) · Cell Host & Microbe · doi:10.1016/j.chom.2019.06.002 · Cited by 747
Metadata
- Authors
- Rotem, E., Loinger, A., Ronin, I., Levin-Reisman, I., Gabay, C., Shoresh, N., Biham, O., & Balaban, N. Q.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2010-06-28
- Volume
- 107
- Issue
- 28
- Pages
- 12541-12546
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W1985002373
- Cited by
- 355
- References
- 5
- FWCI
- 14.42
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Regulation of phenotypic variability by a threshold-based mechanism underlies bacterial persistence. https://app.cochat.ai/papers/10.1073/pnas.1004333107
- The paper
- Rotem, E., Loinger, A., Ronin, I., Levin-Reisman, I., Gabay, C., Shoresh, N., Biham, O., & Balaban, N. Q. (2010). Regulation of phenotypic variability by a threshold-based mechanism underlies bacterial persistence. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1004333107
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.