Regulation of Bacteriocin Production and Cell Death by the VicRK Signaling System in Streptococcus mutans
Bibliographic record
Abstract
The VicRK two-component signaling system modulates biofilm formation, genetic competence, and stress tolerance in Streptococcus mutans. We show here that the VicRK modulates bacteriocin production and cell viability, in part by direct modulation of competence-stimulating peptide (CSP) production in S. mutans. Global transcriptome and real-time transcriptional analysis of the VicK-deficient mutant (SmuvicK) revealed significant modulation of several bacteriocin-related loci, including nlmAB, nlmC, and nlmD (P < 0.001), suggesting a role for the VicRK in producing mutacins IV, V, and VI. Bacteriocin overlay assays revealed an altered ability of the vic mutants to kill related species. Since a well-conserved VicR binding site (TGTWAH-N(5)-TGTWAH) was identified within the comC coding region, we confirmed VicR binding to this sequence using DNA footprinting. Overexpression of the vic operon caused growth-phase-dependent repression of comC, comDE, and comX. In the vic mutants, transcription of nlmC/cipB encoding mutacin V, previously linked to CSP-dependent cell lysis, as well as expression of its putative immunity factor encoded by immB, were significantly affected relative to the wild type (P < 0.05). In contrast to previous reports that proposed a hyper-resistant phenotype for the VicK mutant in cell viability, the release of extracellular genomic DNA was significantly enhanced in SmuvicK (P < 0.05), likely as a result of increased autolysis compared with the parent. The drastic influence of VicRK on cell viability was also demonstrated using vic mutant biofilms. Taken together, we have identified a novel regulatory link between the VicRK and ComDE systems to modulate bacteriocin production and cell viability of 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) | 0.000 | 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".