Interleukin-9 enhances interleukin-5 receptor expression, differentiation, and survival of human eosinophils
Bibliographic record
Abstract
Abstract Interleukin-9 (IL-9) has been implicated in the pathogenesis of allergic disorders. To examine the interaction between IL-9 and eosinophils, we evaluated mature peripheral blood eosinophils for their expression of the specific α-subunit of the IL-9 receptor (IL-9R–α). The expression of IL-9R–α by human eosinophils was detected at the messenger RNA (mRNA) and protein levels by reverse transcriptase–polymerase chain reaction (RT-PCR), flow cytometry, and immunocytochemical analysis, respectively. Functional analyses demonstrated that recombinant human (rh)IL-9 inhibited in vitro peripheral blood human eosinophil apoptosis in a concentration-dependent manner. We then examined the role of IL-9 in eosinophil differentiation using the human cord blood CD34+cells and human promyelocytic leukemia cells (HL-60). The addition of IL-9 to CD34+ cells cultured in IL-3 and IL-5 enhanced eosinophil development, and IL-9 alone induced the expression of IL-5R–α. IL-9 also up-regulated the IL-5R–α chain cell surface expression during terminal eosinophil differentiation of the HL-60 cell line. Our findings suggest that IL-9 may potentiate in vivo eosinophil function by increasing their survival and IL-5–mediated differentiation and maturation. Taken together, these results suggest a mechanism by which IL-9 potentiates airway and tissue eosinophilia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".