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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".