The Specific Mitogen- and Stress-Activated Protein Kinase MSK1 Inhibitor SB-747651A Modulates Chemokine-Induced Neutrophil Recruitment
Bibliographic record
Abstract
Mitogen-activated protein kinase (MAPK) signaling is involved in a variety of cellular functions. MAPK-dependent functions rely on phosphorylation of target proteins such as mitogen- and stress-activated protein kinase 1 (MSK1). MSK1 participates in the early gene expression and in the production of pro- and anti-inflammatory cytokines. However, the role of MSK1 in neutrophil recruitment remains elusive. Here, we show that chemokine macrophage inflammatory protein-2 (CXCL2) enhances neutrophil MSK1 expression. Using intravital microscopy and time-lapsed video analysis of cremasteric microvasculature in mice, we studied the effect of pharmacological suppression of MSK1 by SB-747651A on CXCL2-elicited neutrophil recruitment. SB-747651A treatment enhanced CXCL2-induced neutrophil adhesion while temporally attenuating neutrophil emigration. CXCL2-induced intraluminal crawling was reduced following SB-747651A treatment. Fluorescence-activated cell sorting analysis of integrin expression revealed that SB-747651A treatment attenuated neutrophil integrin αMβ2 (Mac-1) expression following CXCL2 stimulation. Both the transmigration time and detachment time of neutrophils from the venule were increased following SB-747651A treatment. It also decreased the velocity of neutrophil migration in cremasteric tissue in CXCL2 chemotactic gradient. SB-747651A treatment enhanced the extravasation of neutrophils in mouse peritoneal cavity not at 1–2 h but at 3–4 h following CXCL2 stimulation. Collectively, our data suggest that inhibition of MSK1 by SB-747651A treatment affects CXCL2-induced neutrophil recruitment by modulating various steps of the recruitment cascade in vivo.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".