ArticleCommunications biology2019
Forecasting cell fate during antibiotic exposure using stochastic gene expression.
Article in Communications biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.
What it found
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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.
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.
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Who cites it
13 citing papers in PubMed, 31 citations in OpenAlex.
- Dynamic heterogeneity in an E. coli stress response regulon mediates gene activation and antimicrobial peptide tolerance.Cell reports · 2026Article
- Evaluating the predictive power of combined gene expression dynamics from single cells on antibiotic survival.mSystems · 2025Article
- Stochastic gene expression in proliferating cells: Differing noise intensity in single-cell and population perspectives.PLoS computational biology · 2025Article
- Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives.bioRxiv : the preprint server for biology · 2024Article
- Accurate Prediction of Antimicrobial Susceptibility for Point-of-Care Testing of Urine in Less than 90 Minutes via iPRISM Cassettes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Previous Antibiotic Exposure Reshapes the Population Structure of Infecting Uropathogenic Escherichia coli Strains by Selecting for Antibiotic Resistance over Urovirulence.Microbiology spectrum · 2023Article
- Gene regulation in Escherichia coli is commonly selected for both high plasticity and low noise.Nature ecology & evolution · 2022Article
- Dynamic gene expression and growth underlie cell-to-cell heterogeneity inProceedings of the National Academy of Sciences of the United States of America · 2022Article
- DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics.PLoS computational biology · 2022Article
- Article
- Review
- Functional roles of microbial cell-to-cell heterogeneity and emerging technologies for analysis and control.Current opinion in microbiology · 2020Review
- Salicylate Increases Fitness Cost Associated with MarA-Mediated Antibiotic Resistance.Biophysical journal · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Antibiotic killing does not occur at a single, precise time for all cells within a population. Variability in time to death can be caused by stochastic expression of genes, resulting in differences in endogenous stress-resistance levels between individual cells in a population. Here we investigate whether single-cell differences in gene expression prior to antibiotic exposure are related to cell survival times after antibiotic exposure for a range of genes of diverse function. We quantified the time to death of single cells under antibiotic exposure in combination with expression of reporters. For some reporters, including genes involved in stress response and cellular processes like metabolism, the time to cell death had a strong relationship with the initial expression level of the genes. Our results highlight the single-cell level non-uniformity of antibiotic killing and also provide examples of key genes where cell-to-cell variation in expression is strongly linked to extended durations of antibiotic survival.
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Registered trials
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.