p53‐dependent up‐regulation of <i><scp>CDKN</scp>1A</i> and down‐regulation of <i><scp>CCNE</scp>2</i> in response to beryllium
Bibliographic record
Abstract
OBJECTIVES: Beryllium salts (here, beryllium sulphate) can produce a cytostatic effect in some cell types. The basis for this effect may include increased expression of proliferation inhibitors, reduced expression of proliferation promoters, or both. This study sought to determine the role of p53, the tumour-suppressing transcription factor, in mediating beryllium-induced cytostasis. MATERIALS AND METHODS: Human A172 glioma cells express wild-type TP53 gene. Activity of p53 was experimentally manipulated using siRNA and related approaches. Key elements of the beryllium-response were compared in normal and p53-knockdown A172 cells using RT-PCR and Western blotting. RESULTS: caused 300% increase in CDKN1A (cyclin-dependent kinase inhibitor p21) mRNA and 90% reduction of CCNE2 (cyclin E2) mRNA. The increased p21 mRNA and reduced cyclin E2 mRNA were each dependent on presence of functional p53. For p21, increased mRNA led to commensurately increased protein levels. In contrast, reduction in cyclin E2 mRNA levels did not lead to corresponding reductions in cyclin E2 protein. The proteasomal inhibitor MG-132 caused p53 protein to increase, but it had no effect on cyclin E2 protein levels. Cycloheximide time course studies indicated that the cyclin E2 protein half-life was more than 12 hours in these cells. CONCLUSIONS: Beryllium elicited p53-dependent changes in mRNA levels of key determinants of cell proliferation such as p21 and cyclin E2. However, cyclin E2 protein appeared to be aberrantly regulated in this cell type, as its turnover was unexpectedly slow.
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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) | 0.003 | 0.001 |
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".