MNKs act as a regulatory switch for eIF4E1 and eIF4E3 driven mRNA translation in DLBCL
Bibliographic record
Abstract
The phosphorylation of eIF4E1 at serine 209 by MNK1 or MNK2 has been shown to initiate oncogenic mRNA translation, a process that favours cancer development and maintenance. Here, we interrogate the MNK-eIF4E axis in diffuse large B-cell lymphoma (DLBCL) and show a distinct distribution of MNK1 and MNK2 in germinal centre B-cell (GCB) and activated B-cell (ABC) DLBCL. Despite displaying a differential distribution in GCB and ABC, both MNKs functionally complement each other to sustain cell survival. MNK inhibition ablates eIF4E1 phosphorylation and concurrently enhances eIF4E3 expression. Loss of MNK protein itself downregulates total eIF4E1 protein level by reducing eIF4E1 mRNA polysomal loading without affecting total mRNA level or stability. Enhanced eIF4E3 expression marginally suppresses eIF4E1-driven translation but exhibits a unique translatome that unveils a novel role for eIF4E3 in translation initiation. We propose that MNKs can modulate oncogenic translation by regulating eIF4E1-eIF4E3 levels and activity in DLBCL.
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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".