Mechanisms involved in interleukin‐15‐induced suppression of human neutrophil apoptosis: role of the anti‐apoptotic Mcl‐1 protein and several kinases including Janus kinase‐2, p38 mitogen‐activated protein kinase and extracellular signal‐regulated kinases‐1/2
Bibliographic record
Abstract
Interleukin-15 (IL-15) is a pro-inflammatory cytokine known as a general inhibitor of apoptosis, which possesses potential therapeutic properties. Although IL-15 was previously found to be a human neutrophil agonist, its mode of action remains unknown. Herein, we were interested in elucidating the mechanisms by which it delays neutrophil apoptosis. IL-15 was found to induce tyrosine phosphorylation events and to prevent loss of the anti-apoptotic Mcl-1 protein expression. Using different signal transduction inhibitors, we found that Janus kinase (Jak)-2, Jak-3, p38 mitogen-activated protein kinase (MAPK) and extracellular signal-regulated kinase (ERK), but not G proteins, are involved in IL-15-induced suppression of apoptosis. Furthermore, we found that IL-15 activates Jak-2, p38 MAPK and ERK-1/2, but, unlike granulocyte macrophage-colony-stimulating factor (GM-CSF), it does not activate signal transducer and activator of transcription (STAT)-5a/b. We conclude that IL-15 delays neutrophil apoptosis via several pathways, and that Mcl-1 and several kinases contribute to this. We also conclude that, unlike GM-CSF, IL-15 does not activate the Jak-2/STAT-5 pathway found to be important in neutrophil signaling.
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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".