Signaling by the Cysteinyl-Leukotriene Receptor 2
Bibliographic record
Abstract
Cysteinyl-leukotrienes are involved in inflammation and act on at least two G-protein-coupled receptors, CysLT1 and CysLT2. However, the role of the CysLT2 receptor as well as its signaling remain poorly understood. Here we show that leukotriene (LT)C(4) induced the production of the chemokine interleukin (IL)-8 in endothelial cells. To further study the signaling cascade involved, HEK293 cells were stably transfected with CysLT2 and used to study the transcriptional regulation of the IL-8 promoter. Stimulation of the cells with increasing concentrations of LTC(4) resulted in a time- and concentration-dependent induction of IL-8 transcription and protein synthesis. Use of IL-8 promoter mutants with substitutions in their NF-kappaB, AP-1, or NF-IL-6 binding elements revealed an almost total requirement for NF-kappaB and AP-1 elements, and a lesser requirement for the NF-IL-6 element. Overexpression of dominant-negative IkappaBalpha prevented the IL-8 transactivation induced by LTC(4). LTC(4) stimulation induced NF-kappaB and AP-1 DNA binding, which involved the formation of a p50/p65 and a c-JUN.c-FOS complex, respectively. Transfection of the cells with a dominant negative (dn) form of PKCepsilon prevented p65 phosphorylation, whereas dnPKCdelta prevented AP-1 binding. Moreover, dnPKCdelta, dnPKCepsilon, and dnPKCzeta prevented LTC(4)-induced IL-8 transcription in response to LTC(4). Our data show for the first time that LTC(4) can act via the CysLT2 receptor to transcriptionally activate chemokine production through induction of NF-kappaB and AP-1 transcription factors. These findings suggest the potential implication of CysLT2 in the inflammatory response through the modulation of chemokine gene transcription.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".