Inflammatory Cytokine Expression Is Independent of the c-Jun N-Terminal Kinase/AP-1 Signaling Cascade in Human Neutrophils
Bibliographic record
Abstract
In the last decade, the ability of neutrophils to generate proinflammatory cytokines has become firmly established. Because neutrophils typically infiltrate inflammatory sites in large numbers, they could significantly contribute to the cytokine environment and even represent a substantial source of cytokines in chronic inflammatory disorders in which they predominate over other cell types. To date, however, most studies have focused on identifying which mediators are produced by neutrophils, as opposed to elucidating the molecular bases underlying this process. We previously showed that most stimuli of cytokine production in neutrophils also activate NF-kappaB in these cells. In this report, we turned our attention to another transcription factor that plays a central role in inflammation, AP-1. Among Jun/Fos proteins, only JunD and c-Fos are abundantly expressed in neutrophils, and they are mainly cytoplasmic. Both the cellular levels and distribution of the Jun/Fos proteins remain unaffected by various neutrophil stimuli, including those that are known to increase the corresponding mRNA transcripts. Similarly, c-Jun N-terminal kinase (JNK) 1 is overwhelmingly cytoplasmic in neutrophils and does not translocate to the nucleus upon cell activation. Although JNK is not activatable under most circumstances, specific conditions do allow its phosphorylation in response to TNF. However, no experimental condition (even those leading to JNK activation) resulted in the induction of genuine AP-1 complexes in neutrophils. Accordingly, the potent JNK inhibitor, SP 600125, failed to inhibit inflammatory cytokine gene expression in neutrophils. Collectively, our findings strongly suggest that the JNK/AP-1 signaling pathway has little or no impact on the generation of inflammatory mediators in neutrophils.
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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 | 0.001 |
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".