Identification of a prosurvival neuroprotective mitochondrial peptide in a mammalian hibernator
Bibliographic record
Abstract
Hibernation requires the intricate regulation of physiological and biochemical adaptations to facilitate the decrease in metabolic rate and activation of prosurvival factors needed for winter survival. Mitochondria play important roles in eliciting these responses and in coordinating the required energy shifts. Herein, we report the presence of a novel mitochondrial peptide, s-humanin, in the hibernating 13-lined ground squirrel, Ictidomys tridecemlineatus. S-humanin was shown to have strong structural and sequence similarities to its human analogue, humanin-a powerful neuroprotective mitochondrial peptide. An assessment of the protein and gene expression levels of this peptide in ground squirrels revealed stark tissue-specific regulatory responses whereby transcript levels increased in brain cortex, skeletal muscle, and adipose tissues during hibernation, suggesting a protective torpor-induced activation. Accompanying peptide measurements found that s-humanin levels were suppressed in liver of torpid squirrels but enhanced in brain cortex. The enhanced transcript and protein levels of s-humanin in brain cortex suggest that it is actively involved in protecting delicate brain tissues and neuronal connections from hibernation-associated stresses. We propose that this squirrel-specific peptide is involved in modulating tissue-specific cytoprotective functions, expanding its role from human-specific neuroprotection to environmental stress protection. SIGNIFICANCE OF THE STUDY: Understanding the molecular mechanisms, which protect against oxidative stress in a model hibernator such as the ground squirrel, could be pivotal to the regulation of cytoprotection. This study expands on our knowledge of metabolic rate depression and could suggest a potential role for humanin therapy in neurodegenerative diseases.
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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".