A DNA-Damage Inducible Gene Promotes the Formation of Antibiotic Persisters in Response to the Quorum Sensing Signaling Peptide in Streptococcus mutans
Bibliographic record
Abstract
Bacteria use quorum sensing (QS) to communicate with each other via secreted small autoinducers produced by individuals. QS allows bacteria to display a unified response that benefits the species during adaptation to environment, colonization, and defense against competitors. In oral streptococci, the CSP-ComDE QS is an inducible DNA damage repair system that is pivotal for bacterial survival. In the oral pathogen Streptococcus mutans, the QS system positively influences the formation of antibiotic persisters, cells that can survive antibiotic attack by entering a non-proliferative state. We recently identified a novel gene, pep299, that is activated in the persister cell fraction induced by QS. In this study, we focused our investigation on the role of pep299, a gene encoding a bacteriocin-like peptide, in the formation of antibiotic persisters. Mutant Δ299, unable to produce Pep299, showed a dramatic reduction in the number of stress-induced persisters. Using a co-culture assay, we showed that cells overproducing pep299 induced the formation of persisters in the mutant, suggesting that Pep299 was actively secreted and detected by neighboring cells. Cells exposed to DNA damage conditions activated the gene expression of pep299. Interestingly, our results suggested that the pep299 gene was also involved in the regulation of a QS-inducible toxin−antitoxin system. Our study suggests that the pep299 gene is at the core of the triggered persistence phenotype in S. mutans, allowing cells to transition into a state of reduced metabolic activity and antibiotic tolerance.
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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".