MEK-independent ERK activation in human neutrophils and its impact on functional responses
Bibliographic record
Abstract
Neutrophils influence innate and adaptative immunity, notably through the generation of numerous cytokines and chemokines and through the modulation of their constitutive apoptosis. Several signaling cascades are known to control neutrophil responses, including the MEK pathway, which is normally coupled to ERK. However, we show here that in human neutrophils stimulated with cytokines or TLR ligands, MEK and ERK are activated independently of each other. Pharmacological blockade of MEK had no effect on the induction of ERK kinase activity and vice versa. In autologous PBMC exposed to the same stimuli or in neutrophils exposed to chemoattractants, this uncoupling of MEK and ERK was not observed. Whereas we had shown before that MEK inhibition impairs cytokine generation translationally in LPS- or TNF-stimulated neutrophils, ERK inhibition affected this response transcriptionally and translationally. Transcriptional targets or ERK include the mitogen- and stress-activated protein kinase 1 (MSK-1) and its substrates, C/EBPβ and CREB, whereas translational targets include the S6 kinase and its substrate, the S6 ribosomal protein. In addition to affecting cytokine production, ERK inhibition interfered with how LPS or TNF promotes neutrophil survival and levels of the myeloid cell leukemia 1 (Mcl-1) antiapoptotic protein. Whereas the ERK-activating kinase was not identified, we found that the MAP3K, TGF-β-activated kinase 1 (TAK1), acts upstream of ERK and MEK in neutrophils. Our results document a functional uncoupling of the MEK/ERK module under certain stimulatory conditions and suggest that therapeutic strategies based on MEK inhibition might benefit from being complemented by ERK inhibition, particularly in chronic inflammatory conditions featuring a strong neutrophilic component.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".