Triethylene Glycol Up-Regulates Virulence-Associated Genes and Proteins in Streptococcus mutans
Bibliographic record
Abstract
Triethylene glycol dimethacrylate (TEGDMA) is a diluent monomer used pervasively in dental composite resins. Through hydrolytic degradation of the composites in the oral cavity it yields a hydrophilic biodegradation product, triethylene glycol (TEG), which has been shown to promote the growth of Streptococcus mutans, a dominant cariogenic bacterium. Previously it was shown that TEG up-regulated gtfB, an important gene contributing to polysaccharide synthesis function in biofilms. However, molecular mechanisms related to TEG's effect on bacterial function remained poorly understood. In the present study, S. mutans UA159 was incubated with clinically relevant concentrations of TEG at pH 5.5 and 7.0. Quantitative real-time PCR, proteomics analysis, and glucosyltransferase enzyme (GTF) activity measurements were employed to identify the bacterial phenotypic response to TEG. A S. mutans vicK isogenic mutant (SMΔvicK1) and its associated complemented strain (SMΔvicK1C), an important regulatory gene for biofilm-associated genes, were used to determine if this signaling pathway was involved in modulation of the S. mutans virulence-associated genes. Extracted proteins from S. mutans biofilms grown in the presence and absence of TEG were subjected to mass spectrometry for protein identification, characterization and quantification. TEG up-regulated gtfB/C, gbpB, comC, comD and comE more significantly in biofilms at cariogenic pH (5.5) and defined concentrations. Differential response of the vicK knock-out (SMΔvicK1) and complemented strains (SMΔvicK1C) implicated this signalling pathway in TEG-modulated cellular responses. TEG resulted in increased GTF enzyme activity, responsible for synthesizing insoluble glucans involved in the formation of cariogenic biofilms. As well, TEG increased protein abundance related to biofilm formation, carbohydrate transport, acid tolerance, and stress-response. Proteomics data was consistent with gene expression findings for the selected genes. These findings demonstrate a mechanistic pathway by which TEG derived from commercial resin materials in the oral cavity promote S. mutans pathogenicity, which is typically associated with secondary caries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".