REGγ deficiency promotes premature aging via the casein kinase 1 pathway
Bibliographic record
Abstract
Our recent studies suggest a role for the proteasome activator REG (11S regulatory particles, 28-kDa proteasome activator)γ in the regulation of tumor protein 53 (p53). However, the molecular details and in vivo biological significance of REGγ-p53 interplay remain elusive. Here, we demonstrate that REGγ-deficient mice develop premature aging phenotypes that are associated with abnormal accumulation of casein kinase (CK) 1δ and p53. Antibody array analysis led us to identify CK1δ as a direct target of REGγ. Silencing CK1δ or inhibition of CK1δ activity prevented decay of murine double minute (Mdm)2. Interestingly, a massive increase of p53 in REGγ(-/-) tissues is associated with reduced Mdm2 protein levels despite that Mdm2 transcription is enhanced. Allelic p53 haplodeficiency in REGγ-deficient mice attenuated premature aging features. Furthermore, introducing exogenous Mdm2 to REGγ(-/-) MEFs significantly rescues the phenotype of cellular senescence, thereby establishing a REGγ-CK1-Mdm2-p53 regulatory pathway. Given the conflicting evidence regarding the "antiaging" and "proaging" effects of p53, our results indicate a key role for CK1δ-Mdm2-p53 regulation in the cellular aging process. These findings reveal a unique model that mimics acquired aging in mammals and indicates that modulating the activity of the REGγ-proteasome may be an approach for intervention in aging-associated disorders.
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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) | 0.001 | 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".