Differential involvement of NF-κB and MAP kinase pathways in the generation of inflammatory cytokines by human neutrophils
Bibliographic record
Abstract
The ability of human neutrophils to express a variety of genes encoding inflammatory mediators is well documented, and mounting evidence suggests that neutrophil-derived cytokines and chemokines contribute to the recruitment of discrete leukocyte populations at inflammatory sites. Despite this, our understanding of the signaling intermediates governing the generation of inflammatory cytokines by neutrophils remains fragmentary. Here, we report that inhibitors of the p38 MAPK and MEK pathways substantially diminish the release of (and in the case of p38 inhibitors, the gene expression of) several inflammatory cytokines in neutrophils stimulated with LPS or TNF. In addition, various NF-kappaB inhibitors were found to profoundly impede the inducible gene expression and release of inflammatory cytokines in these cells. The MAPK inhibitors did not affect NF-kappaB activation; instead, the transcriptional effects of the p38 MAPK inhibitor appear to involve transcriptional factor IID. Conversely, the NF-kappaB inhibitors failed to affect the activation of MAPKs. Finally, the MAPK inhibitors were found to prevent the activation a key component of the translational machinery, S6 ribosomal protein, in keeping with their post-transcriptional impact on cytokine generation. To our knowledge, this constitutes the first demonstration that in neutrophils, the inducible expression of proinflammatory cytokines by physiological stimuli largely reflects the ability of the latter to activate NF-kappaB and selected MAPK pathways. Our data also raise the possibility that NF-kappaB or MAPK inhibitors could be useful in the treatment of inflammatory disorders in which neutrophils predominate.
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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) | 0.001 | 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".