In vitro Manganese-Dependent Cross-Talk between Streptococcus mutans VicK and GcrR: Implications for Overlapping Stress Response Pathways
Bibliographic record
Abstract
Streptococcus mutans, a major acidogenic component of the dental plaque biofilm, has a key role in caries etiology. Previously, we demonstrated that the VicRK two-component signal transduction system modulates biofilm formation, oxidative stress and acid tolerance responses in S. mutans. Using in vitro phosphorylation assays, here we demonstrate for the first time, that in addition to activating its cognate response regulator protein, the sensor kinase, VicK can transphosphorylate a non-cognate stress regulatory response regulator, GcrR, in the presence of manganese. Manganese is an important micronutrient that has been previously correlated with caries incidence, and which serves as an effector of SloR-mediated metalloregulation in S. mutans. Our findings supporting regulatory effects of manganese on the VicRK, GcrR and SloR, and the cross-regulatory networks formed by these components are more complex than previously appreciated. Using DNaseI footprinting we observed overlapping DNA binding specificities for VicR and GcrR in native promoters, consistent with these proteins being part of the same transcriptional regulon. Our results also support a role for SloR as a positive regulator of the vicRK two component signaling system, since its transcription was drastically reduced in a SloR-deficient mutant. These findings demonstrate the regulatory complexities observed with the S. mutans manganese-dependent response, which involves cross-talk between non-cognate signal transduction systems (VicRK and GcrR) to modulate stress response pathways.
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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".