Identification of Distinctive Patterns of USP19-Mediated Growth Regulation in Normal and Malignant Cells
Bibliographic record
Abstract
We previously reported that the USP19 deubiquitinating enzyme positively regulates proliferation in fibroblasts by stabilizing KPC1, a ubiquitin ligase for p27(Kip1). To explore whether this role of USP19 extends to other cellular systems, we tested the effects of silencing of USP19 in several human prostate and breast models, including carcinoma cell lines. Depletion of USP19 inhibited proliferation in prostate cancer DU145, PC-3 and 22RV1 cells, which was similar to the pattern established in fibroblasts in that it was due to decreased progression from G1 to S phase and associated with a stabilization of the cyclin-dependent kinase inhibitor p27(Kip1). However, in contrast to previous findings in fibroblasts, the stabilization of p27(Kip1) upon USP19 depletion was not associated with changes in the levels of the KPC1 ligase. USP19 could also regulate the growth of immortalized MCF10A breast epithelial cells through a similar mechanism. This regulatory pattern was lost, though, in breast cancer MCF7 and MDA-MB-231 cells and in prostate carcinoma LNCaP cells. Of interest, the transformation of fibroblasts through overexpression of an oncogenic form of Ras disrupted the USP19-mediated regulation of cell growth and of levels of p27(Kip1) and KPC1. Thus, the cell context appears determinant for the ability of USP19 to regulate cell proliferation and p27(Kip1) levels. This may occur through both KPC1 dependent and independent mechanisms. Moreover, a complete loss of USP19 function on cell growth may arise as a result of oncogenic transformation of cells.
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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 | 0.001 | 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".