The p38-MSK1 Signaling Cascade Influences Cytokine Production through CREB and C/EBP Factors in Human Neutrophils
Bibliographic record
Abstract
Neutrophils influence innate and adaptative immunity by generating numerous cytokines and chemokines whose regulation largely depends on transcriptional activators such as NF-κB and C/EBP factors. In this study, we describe the critical involvement of CREB transcription factors (CREB1 and activating transcription factor-1) in this functional response as well as relevant upstream signaling components. Neutrophil stimulation with LPS or TNF led to the phosphorylation, DNA binding activity, and chemokine promoter association of CREB1 and activating transcription factor-1. These responses occurred downstream of the p38-MSK1 signaling axis, as did the phosphorylation and promoter association of another bZIP factor, C/EBPβ. Conversely, inhibition of RSK1 failed to alter the phosphorylation of either CREB1 or C/EBPβ in neutrophils. From a more functional standpoint, the inhibition of p38 MAPK or MSK1 interfered with cytokine generation in neutrophils. Likewise, overexpression of a dominant-negative CREB1 mutant (K-CREB) or of a point mutant (S133A) resulted in a decreased ability of human neutrophil-like PLB-985 cells to generate inflammatory cytokines (CXCL8, CCL3, CCL4, and TNF-α). Collectively, our data show the involvement of CREB1 in neutrophil cytokine production, the key role of its S133 residue, important upstream signaling events, and the parallel activation of another bZIP factor. These are all potential molecular targets that could be exploited in the context of several chronic inflammatory diseases that prominently feature neutrophils and their products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".