TReP-132 Controls Cell Proliferation by Regulating the Expression of the Cyclin-Dependent Kinase Inhibitors p21 <sup>WAF1/Cip1</sup> and p27 <sup>Kip1</sup>
Bibliographic record
Abstract
The transcriptional regulating protein of 132 kDa (TReP-132) has been identified in steroidogenic tissues, where it acts as a coactivator of steroidogenic factor 1 (SF-1). We show here that TReP-132 plays a role in the control of cell proliferation. In human HeLa cells, TReP-132 knockdown by using small interfering RNA resulted in increased G(1)-->S cell cycle progression. The growth-inhibitory effects of TReP-132 was further shown to be mediated by induction of G(1) cyclin-dependent kinase inhibitors p21(WAF1) (p21) and p27(KIP1) (p27) expression levels. As a consequence, G(1) cyclin/cyclin-dependent kinase activities and pRB phosphorylation were markedly reduced, and cell cycle progression was blocked in the G(1) phase. The stimulatory effect of TReP-132 on p21 and p27 gene transcription involved interaction of TReP-132 with the transcription factor Sp1 at proximal Sp1-binding sites in their promoters. Moreover, in different breast tumor cell lines, endogenous TReP-132 expression was positively related with a lower proliferation rate. In addition, TReP-132 knockdown resulted in enhanced cell proliferation and lowered p21 and p27 mRNA levels in the steroid-responsive and nonresponsive T-47D and MDA-MB-231 cell lines, respectively. Finally, a statistic profiling of human breast tumor samples highlighted that expression of TReP-132 is correlated with p21 and p27 levels and is associated with lower tumor incidence and aggressiveness. Together, these results identify TReP-132 as a basal cell cycle regulatory protein acting, at least in part, by interacting with Sp1 to activate the p21 and p27 gene promoters.
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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".