Cold-inducible RNA-binding protein Cirp, but not Rbm3, may regulate transcript processing and protection in tissues of the hibernating ground squirrel
Bibliographic record
Abstract
RNA-binding proteins (RBPs) have important roles in transcription, pre-mRNA processing/transport, mRNA degradation, translation, and non-coding RNA processing, among others. RBPs that are expressed in response to cold stress, such as Cirp and Rbm3, could regulate RNA stability and translation in hibernating mammals that reduce their body temperatures from 37 °C to as low as 0-5 °C during torpor bouts. RBPs including Cirp, Rbm3, and stress-inducible HuR translocate from the nucleus to stabilize mRNAs in the cytoplasm, and thereby could regulate which mRNA transcripts are protected from degradation and are translated, versus stored, for future protein synthesis or degraded by nucleases during cell stress associated with metabolic rate depression. This is the first study to explore the transcriptional/translational regulation, and subcellular localization of cold-inducible RBPs in a model hibernator, the 13-lined ground squirrel (Ictidomys tridecemlineatus). Cirp protein levels were upregulated in liver, skeletal muscle, and brown adipose tissue throughout the torpor-arousal cycle whereas Rbm3 protein levels stayed constant or decreased, suggesting an important role for Cirp, but likely not Rbm3, in the hibernator stress response. Increased cytoplasmic localization of Cirp in liver and muscle and HuR in liver during torpor, but no changes in the relative levels of Rbm3 in the cytoplasm, emphasizes a role for Cirp and possibly HuR in regulating mRNA processing during torpor. This study informs our understanding of the natural adaptations that extreme animals use in the face of stress, and highlight natural stress response mediators that could be used to bolster cryoprotection of human organs donated for transplant.
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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".