Effects of Dipeptide Bestatin on <i>Porphyromonas gingivalis</i> and Epithelial Cells
Bibliographic record
Abstract
BACKGROUND: Dipeptide bestatin has been previously reported to selectively inhibit the growth of Porphyromonas gingivalis. The aims of this study were to investigate the mechanism of action of bestatin and to evaluate its effect on epithelial cells. METHODS: The inhibitory effect of bestatin on P. gingivalis was tested in vitro (culture medium) and in vivo (guinea pig model). Radiolabeled compounds were used to investigate the effect of bestatin on the uptake of amino acids and peptides. The cytotoxic effect of bestatin was evaluated using a keratinocyte cell line. RESULTS: The growth inhibition of P. gingivalis by bestatin was concentration-dependent. Even at high concentrations, compounds possessing a chemical structure or an aminopeptidase inhibitor activity related to bestatin had no effect on growth of P. gingivalis. When injected in the presence of P. gingivalis, bestatin was able to prevent the development of a necrotic abscess in a guinea pig model. Data were obtained suggesting that bestatin does not act on proteinases of P. gingivalis. Rather, bestatin was found to inhibit the intracellular uptake of radioactivity from 14C-labeled amino acids or heat-denatured type I collagen. This was not observed with a spontaneous mutant of P. gingivalis, whose growth was not affected by bestatin. In the second part of the study, bestatin was found to have no effect on epithelial cell viability in culture at concentrations effective on P. gingivalis. In addition, bestatin did not show effects on epithelial cell migration or production of gelatinases. CONCLUSIONS: This study suggests that bestatin selectively inhibits growth of P. gingivalis by affecting the intracellular uptake of amino acids and peptides, which serve as energy and nitrogen sources for this bacterial species. Bestatin has no cytotoxicity and may represent a therapeutic molecule for local treatment of P. gingivalis-associated periodontitis.
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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".