Telomere length is inherited with resetting of the telomere set-point
Bibliographic record
Abstract
We have studied models of telomerase haploinsufficiency in humans and mice to analyze regulation of telomere length and the significance of "set points" in inheritance of telomere length. In three families with clinical syndromes associated with short telomeres resulting from haploinsufficient mutations in TERT, the gene encoding telomerase reverse transcriptase, we asked whether restoration of normal genotypes in offspring of affected individuals would elongate inherited short telomeres. Telomeres were shorter than normal in some but not all genotypically normal offspring of telomerase-mutant parents or grandparents. Analysis of these findings was complicated by heterogeneity of telomere length among individuals, as well as by the admixing of telomeres inherited from affected parents with those inherited from unaffected ("wild-type" TERT) parents. To understand further the inheritance of telomere length, we established a shortened-telomere mouse model. When Tert(+/-) heterozygous mice were successively cross-bred through 17 generations, telomere length shortened progressively. The late-generation Tert(+/-) mice were intercrossed to produce genotypically wild-type Tert(+/+) mice, for which telomere length was characterized. Strikingly, telomere length in these Tert(+/+) mice was not longer than that of their Tert(+/-) parents. Moreover, when successive crosses were carried out among these short-telomere Tert(+/+) offspring mice, telomere length was stable, with no elongation up to six generations. This breeding strategy therefore has established a mouse strain, B6.ST (short telomeres), with C57BL/6 genotype and stable short telomeres. These findings suggest that the set point of telomere lengths of offspring is determined by the telomere lengths of their parents in the presence of normal expression of telomerase.
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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) | 0.002 | 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".