Essential Role for Mnk Kinases in Type II Interferon (IFNγ) Signaling and Its Suppressive Effects on Normal Hematopoiesis
Bibliographic record
Abstract
IFNγ exhibits potent antitumor effects and plays important roles in the innate immunity against cancer. However, the mechanisms accounting for the antiproliferative effects of IFNγ still remain to be elucidated. We examined the role of Mnk1 (MAPK-interacting protein kinase 1) in IFNγ signaling. Our data demonstrate that IFNγ treatment of sensitive cells results in engagement of Mnk1, activation of its kinase domain, and downstream phosphorylation of the cap-binding protein eIF4E on Ser-209. Such engagement of Mnk1 plays an important role in IFNγ-induced IRF-1 (IFN regulatory factor 1) gene mRNA translation/protein expression and is essential for generation of antiproliferative responses. In studies aimed to determine the role of Mnk1 in the induction of the suppressive effects of IFNs on primitive hematopoietic progenitors, we found that siRNA-mediated Mnk1/2 knockdown results in partial reversal of the suppressive effects of IFNγ on human CD34+-derived myeloid (CFU-GM) and erythroid (BFU-E) progenitors. These findings establish a key role for the Mnk/eIF4E pathway in the regulatory effects of IFNγ on normal hematopoiesis and identify Mnk kinases as important elements in the control of IFNγ-inducible ISG mRNA translation.
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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".