Dexamethasone Inhibits IL-12p40 Production in Lipopolysaccharide-Stimulated Human Monocytic Cells by Down-Regulating the Activity of c-Jun N-Terminal Kinase, the Activation Protein-1, and NF-κB Transcription Factors
Bibliographic record
Abstract
IL-12 plays a critical role in the development of cell-mediated immune responses and in the pathogenesis of inflammatory and autoimmune disorders. Dexamethasone (DXM), an anti-inflammatory glucocorticoid, has been shown to inhibit IL-12p40 production in LPS-stimulated monocytic cells. In this study, we investigated the molecular mechanism by which DXM inhibits IL-12p40 production by studying the role of the mitogen-activated protein kinases (MAPKs), and the key transcription factors involved in human IL-12p40 production in LPS-stimulated monocytic cells. A role for c-Jun N-terminal kinase (JNK) MAPK in LPS-induced IL-12p40 regulation in a promonocytic THP-1/CD14 cell line was demonstrated by using specific inhibitors of JNK activation, SP600125 and a dominant-negative stress-activated protein/extracellular signal-regulated kinase kinase-1 mutant. To identify transcription factors regulating IL-12p40 gene transcription, extensive deletion analyses of the IL-12p40 promoter was performed. The results revealed the involvement of a sequence encompassing the AP-1-binding site, in addition to that of NF-kappaB. The role of AP-1 in IL-12p40 transcription was confirmed by using antisense c-fos and c-jun oligonucleotides. Studies conducted to understand the regulation of AP-1 and NF-kappaB activation by JNK MAPK revealed that both DXM and SP600125 inhibited IL-12p40 gene transcription by inhibiting the activation of AP-1 and NF-kappaB transcription factors as revealed by luciferase reporter and gel mobility shift assays. Taken together, our results suggest that DXM may inhibit IL-12p40 production in LPS-stimulated human monocytic cells by down-regulating the activation of JNK MAPK, the AP-1, and NF-kappaB transcription factors.
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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 | 0.000 |
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".