MecA Protein Acts as a Negative Regulator of Genetic Competence in Streptococcus mutans
Bibliographic record
Abstract
Streptococcus mutans develops competence for genetic transformation through a complex network that receives inputs from at least two signaling peptides, competence-stimulating peptide (CSP) and sigX-inducing peptide (XIP). The key step of competence induction is the transcriptional activation of comX, which encodes an alternative sigma factor, SigX (σ(X)), controlling the expression of late competence genes essential for DNA uptake and recombination. In this study, we provide evidence that MecA acts as a negative regulator in the posttranslational regulation of SigX in S. mutans. Using luxAB transcriptional reporter strains, we demonstrate that MecA represses the expression of late competence genes in S. mutans grown in a complex medium that is subpermissive for competence induction by CSP. The negative regulation of competence by MecA requires the presence of a functional SigX. Accordingly, inactivation of MecA results in a prolonged competence state of S. mutans under this condition. We have also found that the AAA+ protease ClpC displays a similar repressing effect on late competence genes, suggesting that both MecA and ClpC function coordinately to regulate competence in the same regulatory circuit in S. mutans. This suggestion is strongly supported by the results of bacterial two-hybrid assays, which demonstrate that MecA interacts with both SigX and ClpC, forming a ternary SigX-MecA-ClpC complex. Western blot analysis also confirms that inactivation of MecA or ClpC results in the intracellular accumulation of the SigX in S. mutans. Together, our data support the notion that MecA mediates the formation of a ternary SigX-MecA-ClpC complex that sequesters SigX and thereby negatively regulates genetic competence in S. mutans.
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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".