IL-32γ Delays Spontaneous Apoptosis of Human Neutrophils through MCL-1, Regulated Primarily by the p38 MAPK Pathway
Bibliographic record
Abstract
IL-32γ is a multifunctional cytokine involved in various inflammatory and auto-immune diseases in which neutrophils can affect the evolution of these diseases. To persist at inflammatory sites, neutrophils require inhibition of their rapid and constitutive apoptosis, an inhibitory effect that phlogogenic cytokines support. To date, the effects of IL-32γ on neutrophils remain unknown. We demonstrate that IL-32γ delays, in a dose-dependent manner, the spontaneous apoptosis of human blood neutrophils by activating mainly p38 MAPK through rapid p38 phosphorylation. PI3-K and ERK1/2 MAPK are also involved, but to a lesser extent. Most of cytokines that induce retardation of neutrophil apoptosis activate the expression of MCL-1 at both mRNA and protein levels. IL-32γ added to human blood neutrophils in vitro is associated with sustained levels of MCL-1 protein. This effect in neutrophils corresponds to a decrease of MCL-1 protein degradation without any effect on MCL-1 mRNA levels. The sustained levels of MCL-1 induced by IL-32γ are only abrogated by the p38β MAPK inhibitor SB202190. Additionally, IL-32γ induces a reduction in caspase 3 activity in neutrophils. In conclusion, IL-32γ affects human blood neutrophils in vitro by increasing their survival, suggesting that this cytokine could have profound effects on the deleterious functions of neutrophils in several diseases.
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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".