TAK1 contributes to the enhanced responsiveness of LTB4-treated neutrophils to Toll-like receptor ligands
Bibliographic record
Abstract
Pattern-recognition receptors such as Toll-like receptors (TLRs) are essential sensors implicated in the early and efficient innate immune response against pathogens. We have previously demonstrated that leukotriene B(4)(LTB(4)) has the capacity to enhance leukocyte responses to TLR9 ligands and to control viral infection. In this report, we provide evidence that LTB(4) treatment of human neutrophils leads to a potentiation in proinflammatory cytokine secretion induced by various myeloid differentiation factor 88-dependent TLR agonists. LTB(4) failed to enhance TLR mRNA levels as well as expression of TLR2 and TLR4 receptors, suggesting that LTB(4) acts through intracellular mechanism(s) to potentiate neutrophil responses to TLR ligands. We found that while IRAK can be activated by LTB(4), this process is dispensable to LTB(4) to potentiate neutrophil responses to TLR ligands since pretreatment of neutrophils with IRAK1/4 inhibitor did not affect its potentiating effects. However, our data clearly show that LTB(4) treatment of neutrophils led to the phosphorylation of downstream signaling molecules, TAK1 and p38, a process found essential to observe an increased secretion of cytokines by neutrophils activated with TLR ligands. Pretreatment of neutrophils with TAK1 or p38 kinase inhibitors strongly repressed the effect of LTB(4) on cytokine synthesis by neutrophils stimulated with LTA, LPS or CpG. The same pattern was observed in agonist-treated human embryonic kidney 293 cells transfected with TAK1-targeting siRNA where secretion of IL-8 was significantly reduced to basal levels. These results indicate that TAK1 and p38 kinases appear to be central in the 'priming effect' of LTB(4) on neutrophils to enhance response to TLR ligands.
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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) | 0.001 | 0.001 |
| 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) | 0.004 | 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".