Damage of Streptococcus mutans biofilms by carolacton, a secondary metabolite from the myxobacterium Sorangium cellulosum
Bibliographic record
Abstract
BACKGROUND: Streptococcus mutans is a major pathogen in human dental caries. One of its important virulence properties is the ability to form biofilms (dental plaque) on tooth surfaces. Eradication of such biofilms is extremely difficult. We therefore screened a library of secondary metabolites from myxobacteria for their ability to damage biofilms of S. mutans. RESULTS: Here we show that carolacton, a secondary metabolite isolated from Sorangium cellulosum, has high antibacterial activity against biofilms of S. mutans. Planktonic growth of bacteria was only slightly impaired and no acute cytotoxicity against mouse fibroblasts could be observed. Carolacton caused death of S. mutans biofilm cells, elongation of cell chains, and changes in cell morphology. At a concentration of 10 nM carolacton, biofilm damage was already at 35% under anaerobic conditions. A knock-out mutant for comD, encoding a histidine kinase specific for the competence stimulating peptide (CSP), was slightly less sensitive to carolacton than the wildtype. Expression of the competence related alternate sigma factor ComX was strongly reduced by carolacton, as determined by a pcomX luciferase reporter strain. CONCLUSIONS: Carolacton possibly interferes with the density dependent signalling systems in S. mutans and may represent a novel approach for the prevention of dental caries.
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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".