Activation of TAK1 by Chemotactic and Growth Factors, and Its Impact on Human Neutrophil Signaling and Functional Responses
Bibliographic record
Abstract
The MAP3 kinase, TAK1, is known to act upstream of IKK and MAPK cascades in several cell types, and is typically activated in response to cytokines (e.g., TNF, IL-1) and TLR ligands. In this article, we report that in human neutrophils, TAK1 can also be activated by different classes of inflammatory stimuli, namely, chemoattractants and growth factors. After stimulation with such agents, TAK1 becomes rapidly and transiently activated. Blocking TAK1 kinase activity with a highly selective inhibitor (5z-7-oxozeaenol) attenuated the inducible phosphorylation of ERK occurring in response to these stimuli but had little or no effect on that of p38 MAPK or PI3K. Inhibition of TAK1 also impaired MEKK3 (but not MEKK1) activation by fMLF. Moreover, both TAK1 and the MEK/ERK module were found to influence inflammatory cytokine expression and release in fMLF- and GM-CSF-activated neutrophils, whereas the PI3K pathway influenced this response independently of TAK1. Besides cytokine production, other responses were found to be under TAK1 control in neutrophils stimulated with chemoattractants and/or GM-CSF, namely, delayed apoptosis and leukotriene biosynthesis. Our data further emphasize the central role of TAK1 in controlling signaling cascades and functional responses in primary neutrophils, making it a promising target for therapeutic intervention in view of the foremost role of neutrophils in several chronic inflammatory conditions.
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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) | 0.003 | 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".