Casein Kinase 1ϵ Promotes Cell Proliferation by Regulating mRNA Translation
Bibliographic record
Abstract
Deregulation of translation initiation factors contributes to many pathogenic conditions, including cancer. Here, we report the definition of a novel regulatory pathway for translational initiation with possible therapeutic import in cancer. Specifically, we found that casein kinase 1ε (CK1ε) is highly expressed in breast tumors and plays a critical role in cancer cell proliferation by controlling mRNA translation. Eukaryotic translation initiation factor eIF4E, an essential component of the translation initiation complex eIF4F, is downregulated by binding the negative-acting factor 4E-BP1. We found that genetic or pharmacologic inhibition of CK1ε attenuated 4E-BP1 phosphorylation, thereby increasing 4E-BP1 binding to eIF4E and inhibiting mRNA translation. Mechanistic investigations showed that CK1ε interacted with and phosphorylated 4E-BP1 at two novel sites T41 and T50, which were essential for 4E-BP1 inactivation along with increased mRNA translation and cell proliferation. In summary, our work identified CK1ε as a pivotal regulator of mRNA translation and cell proliferation that acts by inhibiting 4E-BP1 function. As CK1ε is highly expressed in breast tumors, these findings offer an initial rationale to explore CK1ε blockade as a therapeutic strategy to treat cancers driven by deregulated 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".