Tumor Necrosis Factor Stimulates Matrix Metalloproteinase 9 Secretion from Cultured Human Chorionic Trophoblast Cells Through TNF Receptor 1 Signaling to IKBKB-NFKB and MAPK1/3 Pathway1
Bibliographic record
Abstract
The identification of proinflammatory signal transduction pathways may suggest new therapeutic targets. In this study, we examine which signaling pathways are involved in tumor necrosis factor (TNF)-induced matrix metalloproteinase 9 (MMP9) secretion in human chorionic trophoblast (CT) cells. Purified CT cells were cultured in the presence of antibodies or chemical inhibitors that specifically block/inhibit distinct TNF receptors and kinase pathways. TNF-induced proMMP9 production, as measured by zymography, was significantly blocked/inhibited by TNF receptor 1 (TNFRSF1A) antibody, NFKB activation inhibitor (NFKBAI), and MAPK1/3 (ERK) inhibitor (U0126) (P < 0.01), but not by TNF receptor 2 (TNFRSF1B) antibody, MAPK14 (p38 MAPK) inhibitor (SB203580), and MAPK8/9/10 (JNK) inhibitor (SP600125). By Western blot analysis, we found that TNF rapidly and significantly increased phosphorylation of IKBKB, MAPK1/3, and MAPK8/9/10 and that the phosphorylation of these kinases by TNF was reduced significantly by TNFRSF1A neutralizing antibody, but not by TNFRSF1B neutralizing antibody. Moreover, we found that TNF increased TNF receptor-associated factor (TRAF) 1 and decreased TRAF2 protein expression through TNFRSF1A, but not TNFRSF1B. The CT cells that had increased TRAF1 and decreased TRAF2 after an initial TNF treatment demonstrated a dramatic deficiency in phosphorylation of the above protein kinases following a secondary TNF treatment. Localization of RELA subunit by immunocytochemistry was shifted to the nuclei after TNF treatment compared to cytosol in untreated controls. We also found cross-talk between the phosphoinositide 3-kinase pathway and ERK pathway. In summary, we have demonstrated that TNF stimulates proMMP9 production in CT cells through TNFRSF1A-TRAFs-IKBKB-NFKB and ERK signaling pathways, but not through TNFRSF1B and JNK/p38-AP-1 pathways.
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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) | 0.001 | 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) | 0.002 | 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".