Loss of Cdk5rap2 triggers cellular senescence via β-catenin-mediated downregulation of WIP1
Bibliographic record
Abstract
Abstract Loss-of-function mutations in Cdk5rap2 is associated with the developmental disorders, primary microcephaly and primordial dwarfism, but the underlying molecular link remains obscure. Here, we show that Cdk5rap2 loss in BJ-5ta human fibroblasts triggers senescence that is associated with proliferation defect, which is manifested as small body size in Cdk5rap2 an/an mice. In fibroblasts, Cdk5rap2 loss induces p53 Ser15 phosphorylation that correlates with decreased level of the p53 phosphatase, WIP1. Ectopic WIP1 expression reverses senescence in Cdk5rap2-depleted cells, linking senescence to WIP1 downregulation. Cdk5rap2 interacts with GSK3β, increasing inhibitory Ser9 phosphorylation in GSK3β, which phosphorylates and tags β-catenin for degradation. Thus, Cdk5rap2 loss decreases GSK3β Ser9 phosphorylation and increases GSK3β activity, reducing β-catenin that affects expression of NF-κB target genes, including WIP1. Consequently, Cdk5rap2 or β-catenin depletion downregulates WIP1. GSK3β Inhibition in Cdk5rap2-depleted cells restores β-catenin and WIP1 levels, reducing p53 Ser15 phosphorylation and preventing senescence. Conversely, WIP1 inhibition increases p53 Ser15 phosphorylation and senescence in Cdk5rap2-depleted cells lacking GSK3β activity. Senescence through GSK3β/β-catenin downregulation of WIP1 may contribute to the developmental disorders associated with Cdk5rap2 loss-of-function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".