Regulation of matrix metalloproteinases and tissue inhibitors of matrix metalloproteinases by <i>Porphyromonas gingivalis</i> in an engineered human oral mucosa model
Bibliographic record
Abstract
Under physiological conditions, matrix metalloproteinases (MMPs) are involved in the remodeling and turnover of periodontal tissue and their activity is tightly regulated by tissue inhibitors of metalloproteinases (TIMPs). Disturbances in the balance between MMPs and TIMPs may result in excessive tissue destruction. We previously used an engineered human oral mucosa (EHOM) model to demonstrate that Porphyromonas gingivalis, a major etiological agent of periodontitis, infiltrates connective tissue and induces significant loss of attachment of the stratified epithelium from the basement membrane. The aim of the present study was to investigate the effect of P. gingivalis on the expression and production of MMP-2, MMP-9, TIMP-1, and TIMP-2 by oral fibroblasts and epithelial cells. The EHOM model was infected with P. gingivalis ATCC 33277 or its derivative gingipain-null mutant (KDP128) for different periods of time. MMP and TIMP mRNA expression was evaluated by reverse transcription-polymerase chain reaction (RT-PCR) analysis, while protein secretion into the culture medium was assessed by enzyme-linked immunosorbent assays. P. gingivalis significantly up-regulated MMP-2 and MMP-9 mRNA expression by oral epithelial cells. This MMP gene activation was paralleled by TIMP-2 gene activation. However, only MMP-9 mRNA expression was significantly enhanced by the gingipain-null mutant. At 8 and 24 h post-infection, P. gingivalis increased significantly the MMP-9 protein level compared to the uninfected EHOM model. The present study reports the ability of P. gingivalis to regulate MMP and TIMP production by oral cells, a phenomenon that may contribute to tissue destruction.
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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".