Vitamin D inhibits the growth of and virulence factor gene expression by<i>Porphyromonas gingivalis</i>and blocks activation of the nuclear factor kappa B transcription factor in monocytes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Increasing evidence suggests that 1,25-dihydroxyvitamin D3 (1,25(OH)2 D3 ), a fat-soluble secosteroid hormone, has a positive impact on periodontal health through diverse mechanisms. The present study was aimed at investigating the effect of 1,25(OH)2 D3 on the growth of and virulence factor gene expression by the periodontopathogenic bacterium Porphyromonas gingivalis. The effect of 1,25(OH)2 D3 on P. gingivalis-mediated activation of nuclear factor kappa B (NF-κB) transcription factor in monocytes was also assessed. MATERIAL AND METHODS: A broth microdilution assay was used to determine the antibacterial activity of 1,25(OH)2 D3 . The modulation of virulence factor gene expression in P. gingivalis was assessed by quantitative reverse transcription-polymerase chain reaction. NF-κB activation was assessed using a human monocytic cell line stably transfected with a luciferase reporter containing NF-κB binding sites. RESULTS: Minimal inhibitory concentrations of 1,25(OH)2 D3 against P. gingivalis ranged from 3.125 to 6.25 μg/mL. Moreover, a partial synergistic effect was observed when 1,25(OH)2 D3 was used in association with metronidazole. 1,25(OH)2 D3 attenuated the virulence of P. gingivalis by reducing the expression of genes coding for important virulence factors, including adhesins (fimA, hagA and hagB) and proteinases (rgpA, rgpB and kgp). 1,25(OH)2 D3 dose-dependently prevented P. gingivalis-induced NF-κB activation in a monocyte model. CONCLUSION: Our study suggested that 1,25(OH)2 D3 selectively inhibits the growth of and virulence factor gene expression by P. gingivalis, in addition to attenuating NF-κB activation by this periodontopathogen. This dual action on P. gingivalis and the inflammatory response of host cells may be of particular interest with a view to developing a novel and inexpensive preventive/therapeutic strategy.
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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 | 0.000 |
| 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".