Synergistic Up-Regulation of Epithelial Cell Matrix Metalloproteinase-9 Secretion in Tuberculosis
Bibliographic record
Abstract
Mycobacterium tuberculosis (MTb) kills approximately 2 million people each year. MTb must drive host tissue destruction to disseminate and also to cause pulmonary cavitation. Matrix metalloproteinase-9 (MMP-9, gelatinase B) is implicated in this Tb-related immunopathology. We demonstrate that conditioned media from MTb-infected monocytes (CoMTb), but not direct infection with MTb, up-regulates MMP-9 gene expression and secretion from primary human bronchial epithelial cells (NHBE). MMP-9 secretion was increased 8.7-fold by CoMTb (P < 0.05) as assayed by gelatin zymography. A549 and 16HBE14o epithelial cell MMP secretion was significantly less than primary NHBE secretion. MMP-9 secretion was decreased 53.2% by inhibition of the p38 mitogen-activated protein kinase (MAPK) by SB203580 (P < 0.01) and 48.3% by inhibition of extracellular signal-regulated kinase with PD98059 (P < 0.05). MMP-9 secretion was prostaglandin independent. TNF-alpha was necessary but not sufficient for MMP-9 up-regulation by the monocyte-epithelial cell network. Soluble factors derived from Tb culture synergized with TNF-alpha to increase MMP-9 secretion by NHBE 6-fold (P < 0.01 compared with either stimulus alone). Together, these data reveal a new mechanism by which host- and pathogen-derived factors act together in MTb infection to drive MAPK-dependent MMP-9 secretion from respiratory 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".