Smad6 Methylation Represses NFκB Activation and Periodontal Inflammation
Bibliographic record
Abstract
The balance between pro- and anti-inflammatory signals maintains tissue homeostasis and defines the outcome of chronic inflammatory diseases such as periodontitis, a condition that afflicts the tooth-supporting tissues and exerts an impact on systemic health. The induction of tissue inflammation relies heavily on Toll-like receptor (TLR) signaling, which drives a proinflammatory pathway through recruiting myeloid differentiation primary response gene 88 (MyD88) and activating nuclear factor kappa-light-chain-enhancer of activated B cells (NFκB). TLR-induced production of proinflammatory cytokines and chemokines is reined in by anti-inflammatory cytokines, including the transforming growth factor β (TGFβ) family of cytokines. Although Smad6 is a key mediator of TGFβ-induced anti-inflammatory signaling, the exact mechanism by which TGFβ regulates TLR proinflammatory signaling in the periodontal tissue has not been addressed to date. In this study, we demonstrate for the first time that the ability of TGFβ to inhibit TLR-NFκB signaling is mediated by protein arginine methyltransferase 1 (PRMT1)-induced Smad6 methylation. Upon methylation, Smad6 recruited MyD88 and promoted MyD88 degradation, thereby inhibiting NFκB activation. Most important, Smad6 is expressed and methylated in the gingival epithelium, and PRMT1-Smad6 signaling promotes tissue homeostasis by limiting inflammation. Consistent with this, disturbance of Smad6 methylation exacerbates inflammation and bone loss in experimental periodontitis. The dissected mechanism is therapeutically important, as it highlights the manipulation of PRMT1-Smad6 signaling as a novel promising strategy to modulate the host immune response in periodontitis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".