Polymorphisms within the Telomerase Reverse Transcriptase gene (TERT) in four breeds of dogs selected for difference in lifespan and cancer susceptibility
Bibliographic record
Abstract
BACKGROUND: Enzymatic activity of Telomerase Reverse Transcriptase (TERT) is important in maintaining the telomere length and has been implicated in cancer and aging related pathology. Since cancer susceptibility as well as longevity of dogs vary between breeds, this study involved sequencing the entire TERT gene of Canis familiaris from DNA samples obtained from forty dogs, with ten dogs each of four breeds: Shih Tzu, Dachshund, Irish Wolfhound, and Newfoundland, each with different life expectancies and susceptibility to cancer. RESULTS: We compared the sequences of all forty individuals amongst one another and with the published sequence of canine TERT, and analyzed relationships between members of the same or different breeds. Two separate phylogenetic trees were generated and analyzed from these individuals. Polymorphisms were found most frequently in intronic regions of the gene, although exonic polymorphisms also were observed. In many locations genotypes were observed that were either homozygous for the reference sequence or heterozygous, but the variant homozygous genotype was not observed. CONCLUSIONS: We propose that these homozygous variants are likely to have adverse effects in dogs. It was also found that the polymorphisms did not segregate by breed. Because the four breeds chosen come from geographically and physiologically distinct backgrounds, it can be inferred that the polymorphic diversification of TERT preceded breed derivation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".