Mammalian hibernation: differential gene expression and novel application of epigenetic controls
Bibliographic record
Abstract
This review highlights current information about the regulatory mechanisms that govern gene expression during mammalian hibernation, in particular the potential role of epigenetic controls in coordinating the global suppression of transcription. Hibernation is characterized by long periods of deep torpor (when core body temperature drops to near ambient) that are interspersed with brief arousal periods back to euthermia. Entry into torpor requires coordinated controls which strongly suppress and reprioritize all metabolic functions, including global controls on both transcription and translation. At the same time, however, selected hibernation-specific genes are up-regulated under the control of specific transcription factors to support the torpid state; this includes genes that encode proteins involved in lipid fuel catabolism and in long term cytoprotection (e.g. antioxidants, chaperones). We evaluate the currently available information on global transcriptional suppression in hibernation and propose that epigenetic mechanisms such as DNA methylation, histone modification, SUMOylation and the actions of sirtuins play crucial roles in transcriptional suppression during torpor. Global controls providing translational suppression also occur during hibernation including reversible phosphorylation control of ribosomal initiation and elongation factors as well as polysome dissociation. We also present initial data that mRNA transcripts are regulated via inhibitory interactions with microRNA species during torpor and provide the first evidence of differential expression of miRNAs in hibernators. When taken together, these mechanisms provide hibernators with multiple layers of regulatory controls that achieve both global repression of gene expression and selected enhancement of genes/proteins that achieve the hibernation phenotype.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".