Epidermal growth factor receptor signaling suppresses αvβ6 integrin and promotes periodontal inflammation and bone loss
Bibliographic record
Abstract
ABSTRACT In periodontal disease (PD), bacterial biofilms cause gingival inflammation, leading to bone loss. In healthy individuals, αvβ6 integrin in junctional epithelium maintains anti-inflammatory transforming growth factor-β1 (TGF-β1) signaling, whereas its expression is lost in individuals with PD. Bacterial biofilms suppress β6 integrin expression in cultured gingival epithelial cells (GECs) by attenuating TGF-β1 signaling, leading to an enhanced pro-inflammatory response. In the present study, we show that GEC exposure to biofilms induced activation of mitogen-activated protein kinases and epidermal growth factor receptor (EGFR). Inhibition of EGFR and ERK stunted both the biofilm-induced ITGB6 suppression and IL1B stimulation. Furthermore, biofilm induced the expression of endogenous EGFR ligands that suppressed ITGB6 and stimulated IL1B expression, indicating that the effects of the biofilm were mediated by autocrine EGFR signaling. Biofilm and EGFR ligands induced inhibitory phosphorylation of the TGF-β1 signaling mediator Smad3 at S208. Overexpression of a phosphorylation-defective mutant of Smad3 (S208A) reduced the β6 integrin suppression. Furthermore, inhibition of EGFR signaling significantly reduced bone loss and inflammation in an experimental PD model. Thus, EGFR inhibition may provide a target for clinical therapies to prevent inflammation and bone loss in PD. This article has an associated First Person interview with the first author of the paper.
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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) | 0.002 | 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".