Leukotriene B4 Potentiates CpG Signaling for Enhanced Cytokine Secretion by Human Leukocytes
Bibliographic record
Abstract
TLRs are known to be important in innate host defense against a variety of microbial infections. In particular, TLR9 has been associated with immune defense against different foreign organisms by recognition of unmethylated DNA sequences. In this report, we provide evidence that leukotriene B(4) (LTB(4)) has the capacity to modulate TLR9 expression on human neutrophils. The effect of LTB(4) was found to be specific, because related leukotrienes such as LTC(4) and LTD(4) or neutrophil agonists IL-8 and C5a failed to modulate TLR9 expression in neutrophils. Using fluorochrome-tagged CpG DNA, we observed that LTB(4) treatment also increased TLR9 ligand binding in neutrophils. Moreover, LTB(4) stimulation potentiates CpG-mediated signaling via an endosome-independent mechanism in human neutrophils, leading to enhanced secretion of proinflammatory cytokines. The increase in cytokine secretion by LTB(4) following CpG stimulation of neutrophils was associated with the activation of TGF-beta-activated kinase (TAK-1) as well as p38 and c-Jun (JNK) kinases. In contrast, in PBMC LTB(4) leads to an increase in cytokine secretion following CpG stimulation but via a MyD88- and endosome-dependent mechanism. As observed in neutrophils, PBMC stimulation with LTB(4) in the presence of CpG also results in enhanced TAK-1, p38, and JNK phosphorylation/activation. These data provide new evidence underlying the immunomodulatory properties of LTB(4) leading to antimicrobial defense.
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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 | 0.001 |
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".