Characterizing the Molecular Mechanisms Involved in the Formation of Multidrug-tolerant Persister Cells in Streptococcus mutans
Bibliographic record
Abstract
Bacterial populations contain a small fraction of specialized dormant cells known as persisters that ensure survival from killing by all antibiotics currently in use. These multidrug-tolerant persisters are phenotypic variants that withstand cell death from antibiotics due to inactive cellular processes that most conventional antibiotics target, and are capable of reverting to normal growing cells upon the removal of antibiotics. Thus, persisters are a major contributor in resiliency of chronic microbial infections towards antibiotic therapy. Streptococcus mutans utilizes quorum-sensing for coordinating and regulating bacterial behaviors and adaptive responses at a population-wide level. Previous work has shown that this cariogenic pathogen utilizes its CSP-ComDE quorum-sensing system to direct its stress response for survival. Our objectives were to investigate the nonheritable persister phenotype in S. mutans, and to identify the molecular mechanisms by which it forms these multidrug-tolerant persisters. Persister formation in S. mutans was found to be regulated by multiple mechanisms, including two toxin-antitoxin systems, genes involved in transcription/replication, sugar metabolism, cell wall synthesis, and energy metabolism. Furthermore, we identified that the inducible CSP ‘alarmone’, the peptide pheromone responsible for communicating stress and triggering adaptive responses in the population, is important for the induction of persister formation. We demonstrated that this inducible mechanism is regulated by a CSP-inducible gene encoding a transcriptional repressor that is responsible for regulating DNA damage tolerance and identified novel persister genes that contribute to the formation of quorum-sensing-induced persister cells.
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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) | 0.000 | 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 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".