Escape from the competence state in <i>Streptococcus mutans</i> is governed by the bacterial population density
Bibliographic record
Abstract
Horizontal gene transfer through natural DNA transformation is an important evolutionary mechanism among bacteria. Transformation requires that the bacteria are physiologically competent to take and incorporate free DNA directly from the environment. Although natural genetic transformation is a remarkable feature of many naturally competent bacteria, the process is energetically expensive for the cells. Consequently, a tight control of the competence state is necessary. The objective of the present work was to help decipher the molecular mechanisms regulating the escape from the competence state in Streptococcus mutans, the principal etiological agent responsible for tooth decay in humans. Our results showed that the cessation of competence in S. mutans was abrupt, and did not involve the accumulation of a competence inhibitor nor the depletion of a competence activator in the extracellular environment. The competence state was repressed at high cell population density via concomitant repression of sigX gene encoding the master regulator of the competence regulon. Co-culture experiments performed with oral and non-oral bacteria showed that S. mutans assesses its own population density and also the microbial density of its surroundings to regulate its competence escape. Interestingly, neither the intra-species and extra-species quorum-sensing systems nor the other 13 two-component regulatory systems identified in S. mutans were involved in the cell-density-dependent escape of the competence state. Altogether, our results suggest a complex mechanism regulating the competence shut-off involving cell-density-dependent repression of sigX through an as yet undefined system, and possibly SigX protein stability.
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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 | 0.001 | 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".