IL-5 Up-Regulates Cysteinyl Leukotriene 1 Receptor Expression in HL-60 Cells Differentiated into Eosinophils
Bibliographic record
Abstract
The cysteinyl leukotrienes, leukotriene (LT) C(4), LTD(4), and LTE(4), are lipid mediators that have been implicated in the pathogenesis of several inflammatory processes, including asthma. The human LTD(4) receptor, CysLT(1)R, was recently cloned and characterized. We had previously shown that HL-60 cells differentiated toward the eosinophilic lineage (HL-60/eos) developed specific functional LTD(4) receptors. The present work was undertaken to study the potential modulation of CysLT(1)R expression in HL-60/eos by IL-5, an important regulator of eosinophil function. Here, we report that IL-5 rapidly up-regulates CysLT(1)R mRNA expression, with consequently enhanced CysLT(1)R protein expression and function in HL-60/eos. CysLT(1)R mRNA expression was augmented 2- to 15-fold following treatment with IL-5 (1-20 ng/ml). The effect was seen after 2 h, was maximal by 4 h, and maintained at 8 h. Although CysLT(1)R mRNA was constitutively expressed in undifferentiated HL-60 cells, its expression was not modulated by IL-5 in the absence of differentiation. Differentiated HL-60/eos cells pretreated with IL-5 (10 ng/ml) for 24 h showed enhanced CysLT(1)R expression on the cell surface, as assessed by flow cytometry using a polyclonal anti-CysLT(1)R Ab. They also showed enhanced responsiveness to LTD(4), but not to LTB(4) or platelet-activating factor, in terms of Ca(2+) mobilization, and augmented the chemotactic response to LTD(4). Our findings suggest a possible mechanism by which IL-5 can modulate eosinophil functions and particularly their responsiveness to LTD(4), and thus contribute to the pathogenesis of asthma and allergic diseases.
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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) | 0.001 | 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".