The Chromosomal Toxin Gene <i>yafQ</i> Is a Determinant of Multidrug Tolerance for <i>Escherichia coli</i> Growing in a Biofilm
Bibliographic record
Abstract
Escherichia coli is refractory to elevated doses of antibiotics when it is growing in a biofilm, and this is potentially due to high numbers of multidrug-tolerant persister cells in the surface-adherent population. Previously, the chromosomal toxin-antitoxin loci hipBA and relBE have been linked to the frequency at which persister cells occur in E. coli populations. In the present study, we focused on the dinJ-yafQ-encoded toxin-antitoxin system and hypothesized that deletion of the toxin gene yafQ might influence cell survival in antibiotic-exposed biofilms. By using confocal laser scanning microscopy and viable cell counting, it was determined that a Delta yafQ mutant produced biofilms with a structure and a cell density equivalent to those of the parental strain. In-depth susceptibility testing identified that relative to wild-type E. coli, the Delta yafQ strain had up to a approximately 2,400-fold decrease in cell survival after the biofilms were exposed to bactericidal concentrations of cefazolin or tobramycin. Corresponding to these data, controlled overexpression of yafQ from a high-copy-number plasmid resulted in up to a approximately 10,000-fold increase in the number of biofilm cells surviving exposure to these bactericidal drugs. In contrast, neither the inactivation nor the overexpression of yafQ affected the tolerance of biofilms to doxycycline or rifampin (rifampicin). Furthermore, deletion of yafQ did not affect the tolerance of stationary-phase planktonic cells to any of the antibacterials tested. These results suggest that yafQ mediates the tolerance of E. coli biofilms to multiple but specific antibiotics; moreover, our data imply that this cellular pathway for persistence is likely different from that of multidrug-tolerant cells in stationary-phase planktonic cell cultures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".