<i>Fusobacterium nucleatum</i> Increases Collagenase 3 Production and Migration of Epithelial Cells
Bibliographic record
Abstract
Fusobacterium nucleatum is closely associated with human periodontal diseases and may also be a causative agent in other infections, such as pericarditis, septic arthritis, and abscesses of tonsils and liver. Initiation and outcome of infective diseases depend critically on the host cell signaling system altered by the microbe. Production of proteinases by infected cells is an important factor in pericellular tissue destruction and cell migration. We studied binding of F. nucleatum to human epithelial cells (HaCaT keratinocyte line) and subsequent cell signaling related to collagenase 3 expression, cell motility, and cell survival, using a scratch wound cell culture model. F. nucleatum increased levels of 12 protein kinases involved in cell migration, proliferation, and cell survival signaling, as assessed by the Kinetworks immunoblotting system. Epithelial cells of the artificial wound margins were clearly preferential targets of F. nucleatum. The bacterium colocalized with lysosomal structures and stimulated migration of these cells. Of the 13 anaerobic oral bacterial species, F. nucleatum and Fusobacterium necrophorum were among the best inducers of collagenase 3 mRNA levels, a powerful matrix metalloproteinase. Production of collagenase 3 was detected in fusobacterium-infected cells and cell culture medium by immunocytochemistry, immunoblotting, and zymography. The proteinase production involved activation of p38 mitogen-activated protein kinase in the infected cells. The study suggests that F. nucleatum may be involved in the pathogenesis of periodontal diseases (and other infections) by activating multiple cell signaling systems that lead to stimulation of collagenase 3 expression and increased migration and survival of the infected epithelial cells.
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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) | 0.002 | 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".