Preferential maintenance of critically short telomeres in mammalian cells heterozygous for <i>mTert</i>
Bibliographic record
Abstract
Prolonged growth of murine embryonic stem (ES) cells lacking the telomerase reverse transcriptase, mTert, results in a loss of telomere DNA and an increased incidence of end-to-end fusions and aneuploidy. Furthermore, loss of only one copy of mTert also results in telomere shortening intermediate between wild-type (wt) and mTert-null ES cells [Liu, Y., Snow, B. E., Hande, M. P., Yeung, D., Erdmann, N. J., Wakeham, A., Itie, A., Siderovski, D. P., Lansdorp, P. M., Robinson, M. O. & Harrington, L. (2000) Curr. Biol. 10, 1459-1462]. Unexpectedly, although average telomere length in mTert(+/-) ES cells declined to a similar level as mTert-null ES cells, mTert(+/-) ES cell lines retained a minimal telomeric DNA signal at all chromosome ends. Consequently, no end-to-end fusions and genome instability were observed in the latest passages of mTert(+/-) ES cell lines. These data uncover a functional distinction between the dosage-dependent function of telomerase in average telomere-length maintenance and the selective maintenance of critically short telomeres in cells heterozygous for mTert. In normal and tumor cells, we suggest that telomerase activity insufficient to maintain a given average telomere length may, nonetheless, provide a protective advantage from end-to-end fusion and genome instability.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".