Negative feedback by SNAI2 regulates TGFβ1‐induced amelotin gene transcription in epithelial–mesenchymal transition
Bibliographic record
Abstract
Junctional epithelium (JE) demonstrates biological responses with the rapid turnover of gingival epithelial cells. The state occurs in inflammation of gingiva and wound healing after periodontal therapy. To understand the underlying mechanisms and to maintain homeostasis of JE, it is important to investigate roles of JE-specific genes. Amelotin (AMTN) is localized at JE and regulated by inflammatory cytokines and apoptotic factors that represent a critical role of AMTN in stabilizing the dentogingival attachment, which is an entrance of oral bacteria. In this study, we demonstrated that the AMTN gene expression was regulated by SNAI2 and transforming growth factor β1 (TGFβ1)-induced epithelial-mesenchymal transition (EMT) that occurs in wound healing and fibrosis during chronic inflammation. SNAI2 downregulated AMTN gene expression via SNAI2 bindings to E-boxes (E2 and E4) in the mouse AMTN gene promoter in EMT of gingival epithelial cells. Meanwhile, TGFβ1-induced AMTN gene expression was attenuated by SNAI2 and TGFβ1-induced SNAI2, without inhibition of the TGFβ1-Smad3 signaling pathway. Moreover, SNAI2 small interfering RNA (siRNA) rescued SNAI2-induced downregulation of AMTN gene expression, and TGFβ1-induced AMTN gene expression was potentiated by SNAI2 siRNA. Taken together, these data demonstrated that AMTN gene expression in the promotion of EMT was downregulated by SNAI2. The inhibitory effect of AMTN gene expression was an independent feedback on the TGFβ1-Smad3 signaling pathway, suggesting that the mechanism can be engaged in maintaining homeostasis of gingival epithelial cells at JE and the wound healing phase.
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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 | 0.001 |
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".