<scp>hTERT</scp> Increases <scp>TRF2</scp> to Induce Telomere Compaction and Extend Cell Replicative Lifespan
Bibliographic record
Abstract
Replicative senescence occurs in response to shortened telomeres and is triggered by ATM and TP53-mediated DNA damage signaling that blocks replication. hTERT lengthens telomeres, which is thought to block damage signaling and the onset of senescence. We find that normal diploid fibroblasts expressing hTERT mutants unable to maintain telomere length do not initiate DNA damage signaling and continue to replicate, despite having telomeres shorter than senescent cells. The TRF1 and TRF2 DNA binding proteins of the shelterin complex stabilize telomeres, and we find that expression of different mutant hTERT proteins decreases levels of the Siah1 E3 ubiquitin ligase that targets TRF2 to the proteasome, by increasing levels of the CDC20 and FBXO5 E3 ligases that target Siah1. This restores the TRF2:TRF1 ratio to block the activation of ATM and subsequent activation of TP53 that is usually associated with DNA damage-induced senescence signaling. All hTERT variants reduce DNA damage signaling, and this occurs concomitantly with telomeres assuming a more compact, denser conformation than senescent cells as measured by super-resolution microscopy. This indicates that hTERT variants induce TRF2-mediated telomere compaction that is independent of telomere length, and it plays a dominant role in regulating the DNA damage signaling that induces senescence and blocks replication of human fibroblasts. These observations support the idea that very short telomeres often seen in cancer cells may fail to induce senescence due to selective stabilization of components of the shelterin complex, increasing telomere density, rather than maintaining telomere length via the reverse transcriptase activity of hTERT.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".