Distinct p53 Transcriptional Programs Dictate Acute DNA-Damage Responses and Tumor Suppression
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The molecular basis for p53-mediated tumor suppression remains unclear. Here, to elucidate mechanisms of p53 tumor suppression, we use knockin mice expressing an allelic series of p53 transcriptional activation mutants. Microarray analysis reveals that one mutant, p53(25,26), is severely compromised for transactivation of most p53 target genes, and, moreover, p53(25,26) cannot induce G(1)-arrest or apoptosis in response to acute DNA damage. Surprisingly, p53(25,26) retains robust activity in senescence and tumor suppression, indicating that efficient transactivation of the majority of known p53 targets is dispensable for these pathways. In contrast, the transactivation-dead p53(25,26,53,54) mutant cannot induce senescence or inhibit tumorigenesis, like p53 nullizygosity. Thus, p53 transactivation is essential for tumor suppression but, intriguingly, in association with a small set of novel p53 target genes. Together, our studies distinguish the p53 transcriptional programs involved in acute DNA-damage responses and tumor suppression-a critical goal for designing therapeutics that block p53-dependent side effects of chemotherapy without compromising p53 tumor suppression.
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The p53 transcription factor uses two transactivation domains to drive gene expression.
“The p53 transcription factor uses two transactivation domains to drive gene expression58 and the transactivation of target genes requires cooperative interaction between the p53 molecules at DNA REs.23, 59”
From Census and evaluation of p53 target genes (2017) · cites this paper as [58]
H-RasV12 induces senescence in MEFs that express the wild-type form of p53.
“These experiments were based on a previous study (Brady et al., 2011) showing that H-RasV12 induces senescence in MEFs that express the wild-type form of p53.”
From p53-dependent chemokine production by senescent tumor cells supports NKG2D-dependent tumor elimination by natural killer cells (2013) · cites this paper as Brady et al., 2011
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PML is a ROS sensor activating p53 upon oxidative stress
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Deciphering the acetylation code of p53 in transcription regulation and tumor suppression
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Multifaceted role for p53 in pancreatic cancer suppression
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- Authors
- Brady, C. A., Jiang, D., Mello, S. S., Johnson, T. M., Jarvis, L. A., Kozak, M. M., Broz, D. K., Basak, S., Park, E. J., McLaughlin, M. E., Karnezis, A. N., & Attardi, L. D.
- Affiliations
- Stanford Medicine (US)
- Massachusetts Institute of Technology (US)
- University of California, San Francisco (US)
- Corresponding author
- Attardi, Laura D.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2011-05
- Volume
- 145
- Issue
- 4
- Pages
- 571-583
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC3259909
- OpenAlex
- W2118833232
- Cited by
- 533
- References
- 61
- Citations by year
- 2026: 12 · 2025: 25 · 2024: 25 · 2023: 30 · 2022: 38 · 2021: 33 · 2020: 32 · 2019: 32 · 2018: 38 · 2017: 40 · 2016: 50 · 2015: 32 · 2014: 46 · 2013: 47 · 2012: 39
- FWCI
- 22.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (CA140875, R21CA141087, R01CA140875)
- Topics
- Health Sciences › Medicine › Oncology › Cancer-related Molecular Pathways
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › DNA Repair Mechanisms
- Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
- Keywords
- p53, transcriptional activation, p53 target genes, tumor suppression, DNA damage response, cellular senescence, G1 cell cycle arrest, apoptosis, mutant p53, chemotherapy side effects
- MeSH terms
- Animals, Cell Cycle, DNA Damage, DNA Repair, Humans, Mutation, Neoplasms, Transcriptional Activation, Tumor Suppressor Protein p53, Cellular Senescence, Apoptosis, Protein Structure, Tertiary, Mice, Gene Knock-In Techniques
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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