A role for SUV39H1-mediated H3K9 trimethylation in the control of genome stability and senescence in WI38 human diploid lung fibroblasts
Bibliographic record
Abstract
Cellular senescence has been associated with the age-dependent decline in tissue repair and regeneration, the increasing deterioration of the immune system, and the age-dependent increase in the incidence of cancer. Here, we show that senescence of human lung fibroblast WI-38 cells is associated with extensive changes to the gene expression profile, including the differential expression of transcriptional and epigenetic regulators. Among those,SUV39H1 was downregulated in senescent cells, correlated with a decrease in global H3K9 trimethylation, reduced H3K9me3 levels in repetitive DNA sequence regions such as satellites and transposable elements, and increased transcription of these repetitive DNA sequences. This indicates that SUV39H1 plays a role in limiting genomic instability in dividing cells and suggests that SUV39H1 downregulation may contribute to the establishment of senescence by increasing genomic instability. Additionally, the manipulation of SUV39H1 expression levels resulted in altered cell cycle distribution, suggesting a causal role of SUV39H1 in the establishment of cellular senescence. Thus, based on our findings and the results from previous reports, we propose a model in which SUV39H1 downregulation promotes the establishment of cellular senescence.
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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".