A mechanism for telomere-specific telomere length regulation
Bibliographic record
Abstract
Telomere length is a critical determinant of telomere function and hence chromosome stability. Critically short telomeres induce cellular senescence and division arrest, which eventually may lead to devastating age-related degenerative diseases. Conversely, maintenance of telomere length is a hallmark of cancer. How telomere set-length is established and molecular mechanisms for telomere-specific length regulation remained unknown. Here, we detail a mechanism of a telomere-specific set-length regulation that causes important differences in telomere length between individual telomeres in the same cell. Indeed, the results show that telomerase recruitment is modulated in cis in a telomere-specific way. Increased Sir4 abundance on yeast TEL03L subtelomeric heterochromatin leads to a set-length maintenance that is 1.5 to 2 times higher than on other telomeres. Remarkably, the distal 15 kb of TEL03L are sufficient to transfer this telomere-specific set-length regulation to another chromosome end. Furthermore, a mutation in the telomere boundary element protein Tbf1 allow increased Sir4 binding on telomeres and hence results in longer set-lengths. The results, therefore, will force a rethinking of telomere length regulation away from the generalized view that all telomeres are treated the same, to a more telomere-specific treatment.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".