TNF up-regulates Pentraxin3 expression in human airway smooth muscle cells via JNK and ERK1/2 MAPK pathways
Bibliographic record
Abstract
BACKGROUND: Long pentraxin 3 (PTX3) is a novel candidate marker for inflammation in many chronic diseases. As a soluble pattern recognition receptor, PTX3 is involved in amplification of inflammatory reactions and regulation of innate immunity. Previously, we demonstrate that human airway smooth muscle cells (HASMC) express constitutively PTX3 and upon TNF stimulation. However, very little is known about the mechanism governing its expression in HASMC. We sought to investigate the mechanism governing TNF induced PTX3 expression in primary HASMC. METHODS: HASMC were stimulated with TNF in the presence of transcriptional inhibitor actinomycin D (ActD) or MAPKs pharmacological inhibitors. PTX3 mRNA and protein expression were analyzed by Real-time RT-PCR and ELISA, respectively. PTX3 promoter activity was determined using luciferase assay. RESULTS: PTX3 mRNA and protein are expressed constitutively by HASMC and significantly up-regulated by TNF. TNF-induced PTX3 mRNA and protein release in HASMC were inhibited by transcriptional inhibitor actinomycin D. TNF induced significantly PTX3 promoter activation in HASMC. MAPK JNK and ERK1/2 specific inhibitors (SP600125 and UO126), but not p38, significantly down regulates TNF induced PTX3 promoter activity and protein release in HASMC. Finally, TNF mediated PTX3 promoter activity in HASMC was abolished upon mutation of NF-κβ and AP1 binding sites. CONCLUSIONS: Our data suggest that TNF induced PTX3 in HASMC at least via a transcriptional mechanism that involved MAPK (JNK and ERK1/2), NF-κβ and AP1 pathways. These results rise the possibility that HASMC derived PTX3 may participate in immune regulation in the airways.
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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".