UHRF1 Overexpression Generates Distinct Senescent States with Different Tp53 Dependencies
Bibliographic record
Abstract
Senescence is a pleiotropic phenotype that alternatively suppresses or promotes cancer. The tumor suppressive roles are linked to clearance of damaged cells by the immune system, while cells with tumor promoting functions resist apoptosis, evade immune clearance and either persist and support the tumor microenvironment, or escape and proliferate. What generates these diverse populations is unclear. We investigated this in preneoplastic zebrafish livers where the epigenetic regulator, UHRF1, is overexpressed in hepatocytes. Double strand breaks, DNA methylation repatterning, retrotransposon expression, cell cycle withdrawal and activation of Atm and Tp53 dependent senescence were early responses to UHRF1 overexpression. This evolved to generate diverse populations of senescent cells, some which expressed immune and senescence signatures plus anti-apoptotic markers, and others co-expressed proliferative genes. The fate of these populations was dictated by UHRF1 levels and Tp53, as Tp53 loss enabled proliferation of cells with reduced UHRF1 expression but not in cells expressing high UHRF1. The senolytic Navitoclax targeted only a subset of senescent cells. Thus, the diversity of senescent cells driven by epigenetic changes can generate divergent outcomes.
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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".