Green tea extract and its major constituent, epigallocatechin‐3‐gallate, induce epithelial beta‐defensin secretion and prevent beta‐defensin degradation by <i><scp>P</scp>orphyromonas gingivalis</i>
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Antimicrobial peptides, such as beta-defensins, secreted by gingival epithelial cells, are thought to play a major role in preventing periodontal diseases. In the present study, we investigated the ability of green tea polyphenols to induce human beta-defensin (hBD) secretion in gingival epithelial cells and to protect hBDs from proteolytic degradation by Porphyromonas gingivalis. MATERIAL AND METHODS: Gingival epithelial cells were treated with various amounts (25-200 μg/mL) of green tea extract or epigallocatechin-3-gallate (EGCG). The secretion of hBD1 and hBD2 was measured using ELISAs, and gene expression was quantified by real-time PCR. The treatments were also carried out in the presence of specific kinase inhibitors to identify the signaling pathways involved in hBD secretion. The ability of green tea extract and EGCG to prevent hBD degradation by proteases of P. gingivalis present in a bacterial culture supernatant was evaluated by ELISA. RESULTS: The secretion of hBD1 and hBD2 was up-regulated, in a dose-dependent manner, following the stimulation of gingival epithelial cells with a green tea extract or EGCG. Expression of the hBD gene in gingival epithelial cells treated with green tea polyphenols was also increased. EGCG-induced secretion of hBD1 and hBD2 appeared to involve extracellular signal-regulated kinase 1/2 and p38 mitogen-activated protein kinase. Lastly, green tea extract and EGCG prevented the degradation of recombinant hBD1 and hBD2 by a culture supernatant of P. gingivalis. CONCLUSION: Green tea extract and EGCG, through their ability to induce hBD secretion by epithelial cells and to protect hBDs from proteolytic degradation by P. gingivalis, have the potential to strengthen the epithelial antimicrobial barrier. Future clinical studies will indicate whether these polyphenols represent a valuable therapeutic agent for treating/preventing periodontal diseases.
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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 | 0.001 |
| 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".