Dietary nucleotides can prevent glucocorticoid-induced telomere attrition in a fast-growing wild vertebrate
Bibliographic record
Abstract
Telomeres are chromosome protectors that shorten during cell replication and in stressful conditions. Developing individuals are susceptible to telomere erosion when their growth is fast and resources limited. This is critical because the rate of telomere attrition in early life is linked to health and life span of adults. The metabolic telomere attrition hypothesis (MeTA) suggests that telomere dynamics can respond to biochemical signals conveying information about the organism’s energetic state. Among these signals are glucocorticoids (hormones that promote catabolic processes, potentially impairing costly telomere maintenance) and nucleotides, which activate anabolic pathways though the cellular enzyme target of rapamycin (TOR) preventing telomere attrition. During the energetically demanding growth phase, the regulation of telomeres in response to two contrasting signals—one promoting telomere maintenance and the other inducing attrition—provides an ideal experimental setting to test MeTa. We studied nestlings of a rapidly developing free-living passerine, the great tit (Parus major), that either received glucocorticoids (Cort-chicks), nucleotides (Nuc-chicks), or a combination of both (NucCort-chicks) all compared with controls (Cnt-chicks). Contrary to Cort-chicks, which showed telomere attrition, NucCort-chicks, did not. NucCort-chicks was the only group showing increased gene expression of telo2 (proxy for TOR activation), of mitochondrial enzymes linked to ATP production (atp5f1a-atp5f1b-cox6a1-cox4) and a higher efficiency in aerobically producing ATP. NucCort-chicks had also a higher expression of telomere maintenance genes (trf2) and of enzymatic antioxidant genes (gpx4-sod1). The findings show that nucleotides availability is crucial for preventing telomere erosion during fast growth in stressful environments.
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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 | 0.000 |
| 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".