Role of PBPD1 in Stimulation of Listeria monocytogenes Biofilm Formation by Subminimal Inhibitory β-Lactam Concentrations
Bibliographic record
Abstract
Disinfectant-tolerant Listeria monocytogenes biofilms can colonize surfaces that come into contact with food, leading to contamination and, potentially, food-borne illnesses. To better understand the process of L. monocytogenes biofilm formation and dispersal, we screened 1,120 off-patent FDA-approved drugs and identified several that modulate Listeria biofilm development. Among the hits were more than 30 β-lactam antibiotics, with effects ranging from inhibiting (≤50%) to stimulating (≥200%) biofilm formation compared to control. Most β-lactams also dispersed a substantial proportion of established biofilms. This phenotype did not necessarily involve killing, as >50% dispersal could be achieved with concentrations as low as 1/20 of the MIC of some cephalosporins. Penicillin-binding protein (PBP) profiling using a fluorescent penicillin analogue showed similar inhibition patterns for most β-lactams, except that biofilm-stimulatory drugs did not bind PBPD1, a low-molecular-weight d,d-carboxypeptidase. Compared to the wild type, a pbpD1 mutant had an attenuated biofilm response to stimulatory β-lactams. The cephalosporin-responsive CesRK two-component regulatory system, whose regulon includes PBPs, was not required for the response. The requirement for PBPD1 activity for β-lactam stimulation of L. monocytogenes biofilms shows that the specific set of PBPs that are inactivated by a particular drug dictates whether a protective biofilm response is provoked.
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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".