Common β‐lactamases inhibit bacterial biofilm formation
Bibliographic record
Abstract
Beta-lactamases, which evolved from bacterial penicillin-binding proteins (PBPs) involved in peptidoglycan (PG) synthesis, confer resistance to beta-lactam antibiotics. While investigating the genetic basis of biofilm development by Pseudomonas aeruginosa, we noted that plasmid vectors encoding the common beta-lactamase marker TEM-1 caused defects in twitching motility (mediated by type IV pili), adherence and biofilm formation without affecting growth rates. Similarly, strains of Escherichia coli carrying TEM-1-encoding vectors grew normally but showed reduced adherence and biofilm formation, showing this effect was not species-specific. Introduction of otherwise identical plasmid vectors carrying tetracycline or gentamicin resistance markers had no effect on biofilm formation or twitching motility. The effect is restricted to class A and D enzymes, because expression of the class D Oxa-3 beta-lactamase, but not class B or C beta-lactamases, impaired biofilm formation by E. coli and P. aeruginosa. Site-directed mutagenesis of the catalytic Ser of TEM-1, but not Oxa-3, abolished the biofilm defect, while disruption of either TEM-1 or Oxa-3 expression restored wild-type levels of biofilm formation. We hypothesized that the A and D classes of beta-lactamases, which are related to low molecular weight (LMW) PBPs, may sequester or alter the PG substrates of such enzymes and interfere with normal cell wall turnover. In support of this hypothesis, deletion of the E. coli LMW PBPs 4, 5 and 7 or combinations thereof, resulted in cumulative defects in biofilm formation, similar to those seen in beta-lactamase-expressing transformants. Our results imply that horizontal acquisition of beta-lactamase resistance enzymes can have a phenotypic cost to bacteria by reducing their ability to form biofilms. Beta-lactamases likely affect PG remodelling, manifesting as perturbation of structures involved in bacterial adhesion that are required to initiate biofilm formation.
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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".