Molecular mechanisms involved in interleukin-4-induced human neutrophils: expression and regulation of suppressor of cytokine signaling
Bibliographic record
Abstract
Interleukin-4 (IL-4) is a CD132-dependent cytokine known to activate the Jak-STAT pathway in different cells and cell lines. Although IL-4 has been demonstrated previously to be an agonist in human neutrophils, its capacity to activate different cell signaling pathways in these cells has never been investigated. Two types of IL-4 receptor (IL-4R) exist: the Type I (CD132/IL-4Ralpha heterodimer) and the Type II (IL-4Ralpha/IL-13Ralpha1 heterodimer). In a previous study, we demonstrated that neutrophils express the Type I receptor. Herein, using flow cytometry, we demonstrated that neutrophils, unlike U-937 cells, do not express IL-13Ralpha1 and IL-13Ralpha2 and confirmed the expression of CD132 and IL-4Ralpha on their surface. We also demonstrated that IL-4 induced phosphorylation of Syk, p38, Erk-1/2, JNK, Jak-1, Jak-2, STAT6, and STAT1 and that treatment of cells with the inhibitors piceatannol, SB203580, PD98059, or AG490 reversed the ability of IL-4 to delay neutrophil apoptosis. Using RT-PCR, we demonstrated for the first time that neutrophils express mRNA for all suppressor of cytokine signaling (SOCS) members, namely SOCS1-7 and cytokine-inducible Src homology 2 protein. It is interesting that IL-4 increased expression of SOCS3 at the mRNA and protein levels. The effect of IL-4 on SOCS3 protein expression was increased markedly when the proteasome inhibitor MG132 was added to the cultures, but this was inhibited by cycloheximide, suggesting that SOCS3 is de novo-synthesized in response to IL-4. We conclude that neutrophils express only the Type I IL-4R on their surface and that IL-4 signals via different cell signaling pathways, including the Jak/STAT/SOCS pathway.
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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".