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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".