Telomerase inhibition causes premature senescence of fetal guinea pig muscle cells
Bibliographic record
Abstract
Abstract Premature senescence in low birth weight rodents is associated with later life metabolic disease, including the development of insulin resistance. Telomerase, a reverse transcriptase enzyme with telomeric and non-telomeric functions, is present at high levels during development to maintain and repair long telomere lengths and to protect cells from oxidative stress-induced growth arrest. Adverse In utero environments are often associated with increased reactive oxygen species (ROS), and ROS have been documented to impair/alter telomerase function. We postulate that telomerase protects cells against oxidative stress-induced damage, and its inhibition could lead to premature senescence. A primary cell line of fetal guinea pig muscle cells was differentiated under high (20%) and low (1-2%) oxygen concentrations and telomerase activity was pharmacologically inhibited using a synthetic tea catechin. Following 48 hours, ROS detection was conducted with MitoSOX, Mitotracker and 6-carboxy-2’,7’-dichlorodihydrofluorescein diacetate staining. Cells cultured at 20% O 2 and treated with a telomerase activity inhibitor displayed reduced cell growth rates and increased levels of senescence markers, including p21 and p53. Telomeric DNA damage, measured by phosphorylated-γH2A.X staining at telomeres, was significantly increased in cells cultured at all oxygen concentrations with telomerase inhibition. Telomerase inhibition altered metabolic signaling (e.g. mTOR, p66Shc) and increased mitochondrial ROS levels. Telomerase may protect cells during development from adverse in utero environments that cause premature senescence.
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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".