Inhibition of GM-CSF/IL-3/IL-5 Signaling by Antisense Oligodeoxynucleotides Targeting the Common Beta Chain of Their Receptors
Bibliographic record
Abstract
Granulocyte-macrophage colony-stimulating factor (GM-CSF), interleukin-3 (IL-3), and IL-5 play a key role in allergic inflammation. They mediate their effect via receptors that consist of two distinct subunits, a cytokine-specific alpha subunit and a common beta subunit (betac) that transduces cell signaling. We sought to down-regulate the biologic activities of GM-CSF, IL-3, and IL-5 simultaneously by inhibiting betac mRNA expression with antisense technology. Experiments were performed with TF-1 cells (a human erythroleukemia cell line expressing GM-CSF, IL-3, and IL-5 receptors, which proliferates in response to these cytokines), monocytic U937 cells, which require these cytokines for differentiation, and purified human eosinophils. Cells were treated with antisense phosphorothioate oligodeoxynucleotides (ODN) targeting betac mRNA. In contrast to nontreated cells and cells treated by sense or mismatched ODN, antisense ODN inhibited betac mRNA expression and significantly decreased the level of cell surface betac protein expression on TF-1 and U937 cells. Receptor function was also affected. Antisense ODN were able to inhibit TF-1 cell proliferation in vitro in the presence of GM-CSF, IL-3, or IL-5 in the culture medium and eosinophil survival. We suggest that antisense ODN against betac may provide a new therapeutic alternative for the treatment of neoplastic or allergic diseases associated with eosinophilic inflammation.
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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 | 0.000 |
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 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".