Effects of the twin-arginine translocase on the structure and antimicrobial susceptibility of<i>Escherichia coli</i>biofilms
Bibliographic record
Abstract
In this descriptive study, we used Escherichia coli twin-arginine translocase (tat) mutants to distinguish antibiotic tolerance from the formation of mature biofilm structure. Biofilm formation by wild-type and deltatat strains of E. coli was evaluated using viable cell counts, scanning electron microscopy, and confocal laser-scanning microscopy. Escherichia coli deltatat mutants had an impaired ability to form biofilms when grown in rich or minimal media. These mutants produced disorganized layers and cell aggregates with significantly decreased cell density relative to the wild-type strain. In contrast, wild-type E. coli grown under similar test conditions formed highly structured, surface-adherent communities. We thus determined if this decreased biofilm formation by E. coli deltatat mutants may result in lowered tolerance to antimicrobials. When grown in rich media, planktonic deltatat mutants were hypersensitive to some metals, detergents, and antibiotics. However, the corresponding biofilms were about as resilient as the wild-type strain. In contrast, both planktonic cells and biofilms of the deltatatABC strain grown in minimal media were hypersensitive to many antimicrobials. Remarkably, these biofilms remained up to 365 times more tolerant to beta-lactams than corresponding planktonic cells. Our data suggest that the twin-arginine translocase may play a contributing role in the antimicrobial tolerance, structural organization, and formation of mature E. coli biofilms under nutrient-limited conditions. However, the high tolerance of the deltatatABC strain to bactericidal concentrations of antimicrobials indicates that mature biofilm structure may not be required for surface-adherent E. coli to survive exposure to these lethal factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".