Evaluations of short telomere risk-associated single nucleotide polymorphisms in telomerase reverse transcriptase gene on telomere length maintenance
Bibliographic record
Abstract
ABSTRACT Telomere biology disorders (TBDs) refer to a spectrum of tissue degenerative disorders caused by genetic mutations in telomere biology genes. Most patients with TBDs suffer from telomere maintenance defects secondary to telomerase deficiency. While the highly penetrant mutations in the telomerase reverse transcriptase ( TERT ) gene that drive disease onset and progression of TBDs are relatively rare, there exist several single nucleotide polymorphisms (SNPs) in TERT that have been linked to various diseases in the TBD spectrum. In this study, we investigated the biochemical properties of five TERT variants. In an ex vivo cell model, we found that primary human fibroblasts expressing nonsynonymous TERT SNPs had comparable cell growth kinetics to primary cells expressing WT-TERT, while a parallel vector control expressing-cell line entered replicative senescence. At the molecular level, primary cells expressing the minor alleles of two of the five TERT variants (A279T, ΔE441) had replication-dependent loss of telomere length. In an in vitro primer extension assay, these two variants showed reduced telomerase nucleotide addition processivity. Together, our data suggested that selective, common TERT variants could be revealed to harbour telomere maintenance defects, leading to a plausible explanation for their observed associations to telomere biology disorders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".