DNA adenine methylation influences gene expression and biofilm formation in <i>Streptococcus mutans</i>
Bibliographic record
Abstract
ABSTRACT Streptococcus mutans , a key oral pathogen, utilizes quorum sensing to regulate biofilm formation—a major virulence factor in the development of dental caries (tooth decay). Our recent research uncovered a complex interplay between the CSP-ComDE quorum sensing pathway and the Type II DpnII restriction-modification (R-M) system in S. mutans . The DpnII R-M system methylates adenine at 5′-GATC-3′ sites and cleaves unmethylated DNA, significantly influencing foreign DNA acquisition and gene expression. In this study, we investigated the impact of a ΔRM mutant, which lacks adenine methylation, on biofilm formation. The ΔRM mutant formed fragile biofilms that easily detach from surfaces, with significantly reduced exopolysaccharide content and increased extracellular DNA, which appears to be associated with membrane vesicle production rather than cell lysis. RNA-seq analysis revealed only few differentially expressed genes directly involved in biofilm formation, such as gtfC , suggesting that the biofilm defect may result from indirect effects or alternative regulatory mechanisms. Notably, the downregulation of mutanobactin-related genes and upregulation of genes involved in de novo purine nucleotide biosynthesis point to novel pathways influenced by DNA methylation. These findings contribute to a deeper understanding of the multifactorial nature of biofilm formation and the role of epigenetic modifications in microbial behavior. IMPORTANCE This study highlights the critical role of DNA methylation in regulating biofilm formation and virulence in Streptococcus mutans . By examining the interplay between adenine methylation, extracellular DNA (eDNA), membrane vesicles (MVs), and glucan production, we provide new insights into the complex biology of biofilm development. Our findings challenge traditional views by emphasizing the importance of MVs and eDNA in maintaining biofilm integrity. Understanding these epigenetics modifications not only advances our knowledge of microbial regulation but also identifies novel targets for antimicrobial therapy. Since adenine methylation is rare or absent in mammalian cells, targeting this modification presents a promising strategy to disrupt biofilm formation and combat bacterial infections. The insights gained from this study may inform the development of innovative approaches to manage biofilm-associated infections and improve oral health outcomes.
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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".