Effects of cyclic AMP response element binding protein–Zhangfei (CREBZF) on the unfolded protein response and cell growth are exerted through the tumor suppressor p53
Bibliographic record
Abstract
Zhangfei/CREBZF, a basic region-leucine zipper (bLZip) transcription factor, is a potent suppressor of growth and the unfolded protein response (UPR) in some cancer cell lines, including the canine osteosarcoma cell line, D-17. However, the effects of Zhangfei are not universal, and it has no obvious effects on untransformed cells and some cancer cell lines, suggesting that Zhangfei may act through an intermediary that is either not induced or is defective in cells that it does not affect. Here we identify the tumor suppressor protein p53 as this intermediary. We show the following: in cells ectopically expressing Zhangfei, the protein stabilizes p53 and co-localizes with it in cellular nuclei; the bLZip domain of Zhangfei is required for its profound effects on cell growth and interaction with p53. Suppression of p53 by siRNA at least partially inhibits the effects of Zhangfei on the UPR and cell growth. The effects of Zhangfei on D-17 cells is mirrored by its effects on the p53-expressing human osteosarcoma cell line U2OS, while Zhangfei has no effect on the p53-null osteosarcoma cell line MG63. In U2OS cells, Zhangfei displaces the E3 ubiquitin ligase mouse double minute homolog 2 (Mdm2) from its association with p53, suggesting a mechanism for the effects of Zhangfei on p53.
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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".