The Death Effector Domain-containing DEDD Supports S6K1 Activity via Preventing Cdk1-dependent Inhibitory Phosphorylation
Bibliographic record
Abstract
Cell cycle regulation and biochemical responses upon nutrients and growth factors are the major regulatory mechanisms for cell sizing in mammals. Recently, we identified that the death effector domain-containing DEDD impedes mitotic progression by inhibiting Cdk1 (cyclin-dependent kinase 1) and thus maintains an increase of cell size during the mitotic phase. Here we found that DEDD also associates with S6 kinase 1 (S6K1), downstream of phosphatidylinositol 3-kinase, and supports its activity by preventing inhibitory phosphorylation of S6K1 brought about by Cdk1 during the mitotic phase. DEDD(-/-) cells showed reduced S6K1 activity, consistently demonstrating decreased levels in activating phosphorylation at the Thr-389 site. In addition, levels of Cdk1-dependent inhibitory phosphorylation at the C terminus of S6K1 were enhanced in DEDD(-/-) cells and tissues. Consequently, as in S6K1(-/-) mice, the insulin mass within pancreatic islets was reduced in DEDD(-/-) mice, resulting in glucose intolerance. These findings suggest a novel cell sizing mechanism achieved by DEDD through the maintenance of S6K1 activity prior to cell division. Our results also suggest that DEDD may harbor important roles in glucose homeostasis and that its deficiency might be involved in the pathogenesis of type 2 diabetes mellitus.
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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".