A VicRK Signal Transduction System in <i>Streptococcus mutans</i> Affects <i>gtfBCD</i> , <i>gbpB</i> , and <i>ftf</i> Expression, Biofilm Formation, and Genetic Competence Development
Bibliographic record
Abstract
Bacteria exposed to transient host environments can elicit adaptive responses by triggering the differential expression of genes via two-component signal transduction systems. This study describes the vicRK signal transduction system in Streptococcus mutans. A vicK (putative histidine kinase) deletion mutant (SmuvicK) was isolated. However, a vicR (putative response regulator) null mutation was apparently lethal, since the only transformants isolated after attempted mutagenesis overexpressed all three genes in the vicRKX operon (Smuvic+). Compared with the wild-type UA159 strain, both mutants formed aberrant biofilms. Moreover, the vicK mutant biofilm formed in sucrose-supplemented medium was easily detachable relative to that of the parent. The rate of total dextran formation by this mutant was remarkably reduced compared to the wild type, whereas it was increased in Smuvic+. Based on real-time PCR, Smuvic+ showed increased gtfBCD, gbpB, and ftf expression, while a recombinant VicR fusion protein was shown to bind the promoter regions of the gtfB, gtfC, and ftf genes. Also, transformation efficiency in the presence or absence of the S. mutans competence-stimulating peptide was altered for the vic mutants. In vivo studies conducted using SmuvicK in a specific-pathogen-free rat model resulted in significantly increased smooth-surface dental plaque (Pearson-Filon statistic [PF], <0.001). While the absence of vicK did not alter the incidence of caries, a significant reduction in SmuvicK CFU counts was observed in plaque samples relative to that of the parent (PF, <0.001). Taken together, these findings support involvement of the vicRK signal transduction system in regulating several important physiological processes 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 | 0.001 |
| 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".