<i>Actinobacillus actinomycetemcomitans</i> lipopolysaccharide regulates matrix metalloproteinase, tissue inhibitors of matrix metalloproteinase, and plasminogen activator production by human gingival fibroblasts: A potential role in connective tissue destruction
Bibliographic record
Abstract
Fibroblasts, a major constituent of gingival connective tissue, can produce immunoregulatory cytokines and proteolytic enzymes that may contribute to tissue destruction. In this study, we evaluated the production of matrix metalloproteinases (MMPs), tissue inhibitors of MMPs (TIMPs), and plasminogen activators by gingival fibroblasts stimulated with lipopolysaccharides (LPS) produced by periodontopathogens, including Actinobacillus actinomycetemcomitans. In addition, changes in the expression and phosphorylation state of fibroblast intracellular signaling proteins induced by A. actinomycetemcomitans LPS were characterized using antibody microarrays. We showed that A. actinomycetemcomitans LPS induced the production of a 50 kDa plasminogen activator, MMP-2 and, to a lesser extent, MMP-3 by fibroblasts. The stimulation of fibroblasts with A. actinomycetemcomitans LPS also resulted in the overproduction of TIMP-1, but had no effect on the production of TIMP-2. Comparable responses were also obtained with Porphyromonas gingivalis and Fusobacterium nucleatum subsp. nucleatum LPS. The results of the microarray analyses showed that A. actinomycetemcomitans LPS induced changes in the phosphorylation state and expression of gingival fibroblast intracellular signaling proteins. More specifically, they suggested that A. actinomycetemcomitans LPS may induce both Jun N-terminus protein-serine kinases (JNK) and mitogen-activated protein-serine kinase p38 alpha (p38alpha MAPK) pathway activation, leading to increased activator protein-1 (AP-1) and nuclear factor kappa-B (NFkappaB) activities, which in turn can stimulate MMP-2, MMP-3, TIMP-1, and urokinase-type plasminogen activator (uPA) expression. This may contribute to periodontal connective 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".