HP1α mediates defective heterochromatin repair and accelerates senescence in<i>Zmpste24</i>-deficient cells
Bibliographic record
Abstract
Heterochromatin protein 1 (HP1) interacts with various proteins, including lamins, to play versatile functions within nuclei, such as chromatin remodeling and DNA repair. Accumulation of prelamin A leads to misshapen nuclei, heterochromatin disorganization, genomic instability, and premature aging in Zmpste24-null mice. Here, we investigated the effects of prelamin A on HP1α homeostasis, subcellular distribution, phosphorylation, and their contribution to accelerated senescence in mouse embryonic fibroblasts (MEFs) derived from Zmpste24(-/-) mice. The results showed that the level of HP1α was significantly increased in Zmpste24(-/-) cells. Although prelamin A interacted with HP1α in a manner similar to lamin A, HP1α associated with the nuclease-resistant nuclear matrix fraction was remarkably increased in Zmpste24(-/-) MEFs compared with that in wild-type littermate controls. In wild-type cells, HP1α was phosphorylated at Thr50, and the phosphorylation was maximized around 30 min, gradually dispersed 2 h after DNA damage induced by camptothecin. However, the peak of HP1α phosphorylation was significantly compromised and appeared until 2 h, which is correlated with the delayed maximal formation of γ-H2AX foci in Zmpste24(-/-) MEFs. Furthermore, knocking down HP1α by siRNA alleviated the delayed DNA damage response and accelerated senescence in Zmpste24(-/-) MEFs, evidenced by the rescue of the delayed γ-H2AX foci formation, downregulation of p16, and reduction of senescence-associated β-galactosidase activity. Taken together, these findings establish a functional link between prelamin A, HP1α, chromatin remodeling, DNA repair, and early senescence in Zmpste24-deficient mice, suggesting a potential therapeutic strategy for laminopathy-based premature aging via the intervention of HP1α.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".