Depleting the methyltransferase Suv39h1 improves DNA repair and extends lifespan in a progeria mouse model
Bibliographic record
Abstract
A de novo G608G mutation in LMNA gene leads to Hutchinson–Gilford progeria syndrome. Mice lacking the prelamin A-processing metalloprotease, Zmpste24, recapitulate many of the progeroid features of Hutchinson–Gilford progeria syndrome. Here we show that A-type lamins interact with SUV39H1, and prelamin A/progerin exhibits enhanced binding capacity to SUV39H1, protecting it from proteasomal degradation and, consequently, increasing H3K9me3 levels. Depletion of Suv39h1 reduces H3K9me3 levels, restores DNA repair capacity and delays senescence in progeroid cells. Remarkably, loss of Suv39h1 in Zmpste24−/− mice delays body weight loss, increases bone mineral density and extends lifespan by ∼60%. Thus, increased H3K9me3 levels, possibly mediated by enhanced Suv39h1 stability in the presence of prelamin A/progerin, compromise genome maintenance, which in turn contributes to accelerated senescence in laminopathy-based premature aging. Our study provides an explanation for epigenetic alterations in Hutchinson–Gilford progeria syndrome and a potential strategy for intervention by targeting SUV39H1-mediated heterochromatin remodelling. Accelerated cellular ageing in patients with progeria can be caused by the accumulation of nuclear lamins, leading to DNA damage and histone methylation. Here Liu et al. show that the metyltransferase SUV39H1 regulates lamin A stability, and that SUV39H1 depletion extends lifespan in a progeria mouse model.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".