Autoinducer 2‐Regulated Genes in<i>Streptococcus mutans</i>and Impact on Oral Bacterial Communities
Bibliographic record
Abstract
This chapter explores the mechanism of interspecies cell-cell communication in the oral biofilm, with emphasis on the cariogenic organism Streptococcus mutans. Bioluminescence in the marine organism Vibrio harveyi was one of the first examples of quorum sensing behavior described to occur in nature. The gene required for autoinducer 2 (AI-2) production encodes the enzyme LuxS, which functions in the S-adenosylmethionine (SAM) utilization pathway. Biofilms are complex communities where close interactions between heterogeneous species are common. Alterations in AI-2 signaling might affect biofilm formation in several ways, and changes in the cells themselves during the physiologically distinct biofilm mode of growth might reciprocally affect AI-2 signaling. Exopolysaccharide (EPS) production occurs after attachment and is involved in the latter stages of biofilm maturation. The genetic basis for altered biofilm structure in S. mutans luxS mutants has been attributed to an overexpression of the general stress response genes encoding GroEL and DnaK or to increased glucosyltransferase expression. The majority of genes found to respond to the AI-2 signal were genes involved in protein synthesis and genes for hypothetical proteins. A convincing demonstration of S. mutans complementing a luxS deletion in another species would be helpful in establishing the true nature of this signal. Interference with AI-2-mediated signaling occurs between competing microorganisms that share the same niche.
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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".