Distinct Role of p38 and c-Jun N-Terminal Kinases in IL-10-Dependent and IL-10-Independent Regulation of the Costimulatory Molecule B7.2 in Lipopolysaccharide-Stimulated Human Monocytic Cells
Bibliographic record
Abstract
The costimulatory molecule B7.2 (CD86) plays a vital role in immune activation and development of Th responses. The molecular mechanisms by which B7.2 expression is regulated are not understood. We investigated the role of mitogen-activated protein kinases (MAPK) in the regulation of B7.2 expression in LPS-stimulated human monocytic cells. LPS stimulation of human monocytes resulted in the down-regulation of B7.2 expression that could be abrogated by anti-IL-10 Abs. Furthermore, SB202190, a specific inhibitor of p38 MAPK, inhibited LPS-induced IL-10 production and reversed B7.2 down-regulation, suggesting that LPS-induced B7.2 down-regulation may be mediated, at least in part, via regulation of IL-10 production by p38 MAPK. In contrast to human promonocytic THP-1 cells that are refractory to the inhibitory effects of IL-10, LPS stimulation enhanced B7.2 expression. This IL-10-independent B7.2 induction was not influenced by specific inhibitors of either p38 or p42/44 MAPK. To ascertain the role of the c-Jun N-terminal kinase (JNK) MAPK, dexamethasone, an inhibitor of JNK activation, was used, which inhibited LPS-induced B7.2 expression. Transfection of THP-1 cells with a plasmid expressing a dominant-negative stress-activated protein/extracellular signal-regulated kinase kinase 1 significantly reduced LPS-induced B7.2 expression, thus confirming the involvement of JNK. To study the signaling events downstream of JNK activation, we show that dexamethasone did not inhibit LPS-induced NF-kappaB activation in THP-1 cells, suggesting that JNK may not be involved in NF-kappaB activation leading to B7.2 expression. Taken together, our results reveal the distinct involvement of p38 in IL-10-dependent, and JNK in IL-10-independent regulation of B7.2 expression in LPS-stimulated monocytic 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) | 0.001 | 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".