Back

UHRF1 Overexpression Generates Distinct Senescent States with Different Tp53 Dependencies

Magnani, E.; Chen, C.; Macchi, F.; Madakashira, B. P.; Randic, T.; McBain, I.; Sadler, K. C.

2025-11-30 cancer biology
10.1101/2025.11.26.690503 bioRxiv
Show abstract

SummarySenescence 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. Short summaryUHRF1 overexpression in zebrafish hepatocytes induces DNA damage leading to Atm-tp53-dependent senescence with UHRF1 levels dictating whether hepatocytes remain senescent, escape, or undergo senolytic elimination.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.