Selective pericentromeric heterochromatin dismantling caused by TP53 activation during senescence
Bibliographic record
Abstract
Cellular senescence triggers various types of heterochromatin remodeling that contribute to aging. However, the age-related mechanisms that lead to these epigenetic alterations remain elusive. Here, we asked how two key aging hallmarks, telomere shortening and constitutive heterochromatin loss, are mechanistically connected during senescence. We show that, at the onset of senescence, pericentromeric heterochromatin is specifically dismantled consisting of chromatin decondensation, accumulation of DNA breakages, illegitimate recombination and loss of DNA. This process is caused by telomere shortening or genotoxic stress by a sequence of events starting from TP53-dependent downregulation of the telomere protective protein TRF2. The resulting loss of TRF2 at pericentromeres triggers DNA breaks activating ATM, which in turn leads to heterochromatin decondensation by releasing KAP1 and Lamin B1, recombination and satellite DNA excision found in the cytosol associated with cGAS. This TP53-TRF2 axis activates the interferon response and the formation of chromosome rearrangements when the cells escape the senescent growth arrest. Overall, these results reveal the role of TP53 as pericentromeric disassembler and define the basic principles of how a TP53-dependent senescence inducer hierarchically leads to selective pericentromeric dismantling through the downregulation of TRF2.
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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".