Suppression of αvβ6 Integrin Expression by Polymicrobial Oral Biofilms in Gingival Epithelial Cells
Bibliographic record
Abstract
Abstract Periodontal diseases manifest by the formation of deep pockets between the gingiva and teeth where multispecies bacterial biofilms flourish, causing inflammation and bone loss. Epithelial cell receptor αvβ6 integrin that regulates inflammation by activating the anti-inflammatory cytokine transforming growth factor-β1, is highly expressed in healthy junctional epithelium that connects the gingiva to the tooth enamel. However, its expression is attenuated in human periodontal disease. Moreover, Itgb6−/− mice display increased periodontal inflammation compared to wild-type mice. We hypothesized that bacterial biofilms present in the periodontal pockets suppress αvβ6 integrin levels in periodontal disease and that this change aggravates inflammation. To this end, we generated three-week-old multi-species oral biofilms in vitro and treated cultured gingival epithelial cells (GECs) with their extracts. The biofilm extracts caused suppression of β6 integrin expression and upregulation of pro-inflammatory cytokines, including interleukin-1β and -6. Furthermore, GECs with β6 integrin siRNA knockdown showed increased interleukin-1β expression, indicating that αvβ6 integrin-deficiency is associated with pro-inflammatory cytokine responsiveness. FSL-1, a synthetic bacterial lipopeptide, also suppressed β6 integrin expression in GECs. Therefore, biofilm components, including lipopeptides, may downregulate αvβ6 integrin expression in the pocket epithelium and thus promote epithelial cell-driven pro-inflammatory response in periodontal disease.
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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) | 0.001 | 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".