The p38 MAPK Pathway Mediates the Growth Inhibitory Effects of Interferon-α in BCR-ABL-expressing Cells
Bibliographic record
Abstract
The mechanisms by which interferon-alpha (IFN-alpha) mediates its anti-leukemic effects in chronic myelogenous leukemia (CML) cells are not known. We determined whether p38 MAPK is activated by IFN-alpha in BCR-ABL-expressing cells and whether its function is required for the generation of growth inhibitory responses. IFN-alpha treatment induced phosphorylation/activation of p38 in the IFN-alpha-sensitive KT-1 cell line, but not in IFN-alpha-resistant K562 cells. Consistent with this, IFN-alpha treatment of KT-1 (but not K562) cells induced activation of the small GTPase Rac1, which functions as an upstream regulator of p38. In addition, IFN-alpha-dependent phosphorylation/activation of p38 was induced by treatment of primary granulocytes isolated from the peripheral blood of patients with CML. To define the functional role of the Rac1/p38 MAPK pathway in IFN-alpha signaling, the effects of pharmacological inhibition of p38 on the induction of IFN-alpha responses were determined. Treatment of KT-1 cells with the p38-specific inhibitors SB203580 and SB202190 reversed the growth inhibitory effects of IFN-alpha. On the other hand, the MEK kinase inhibitor PD098059 had no effects, further demonstrating the specificity of these findings. To directly determine the significance of IFN-alpha-dependent activation of p38 in the induction of the anti-leukemic effects of IFN-alpha, we evaluated the effects of p38 inhibition on leukemic colony formation in bone marrow samples of patients with CML. IFN-alpha inhibited leukemic granulocyte/macrophage colony formation in a dose-dependent manner, whereas concomitant treatment with p38 inhibitors reversed such an inhibition. Thus, the Rac1/p38 MAPK pathway is activated by IFN-alpha in BCR-ABL-expressing cells and appears to play a key role in the generation of the growth inhibitory effects of IFN-alpha in CML cells.
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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 | 0.001 |
| 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".