To be or not to be: the regulation of mRNA fate as a survival strategy during mammalian hibernation
Bibliographic record
Abstract
Mammalian hibernators undergo profound behavioral, physiological, and biochemical changes in order to cope with hypothermia, ischemia-reperfusion, and finite fuel reserves over days or weeks of continuous torpor. Against a backdrop of global reductions in energy-expensive processes such as transcription and translation, a subset of genes/proteins are strategically upregulated in order to meet challenges associated with hibernation. Consequently, hibernation involves substantial transcriptional and posttranscriptional regulatory mechanisms and provides a phenomenon with which to understand how a set of common genes/proteins can be differentially regulated in order to enhance stress tolerance beyond that which is possible for nonhibernators. The present review focuses on the involvement of messenger RNA (mRNA) interacting factors that play a role in the regulation of gene/protein expression programs that define the hibernating phenotype. These include proteins involved in mRNA processing (i.e., capping, splicing, and polyadenylation) and the possible role of alternative splicing as a means of enhancing protein diversity. Since the total pool of mRNA remains constant throughout torpor, mechanisms which enhance mRNA stability are discussed in the context of RNA binding proteins and mRNA decay pathways. Furthermore, mechanisms which control the global reduction of cap-dependent translation and the involvement of internal ribosome entry sites in mRNAs encoding stress response proteins are also discussed. Finally, the concept of regulating each of these factors in discrete subcellular compartments for enhanced efficiency is addressed. The analysis draws on recent research from several well-studied mammalian hibernators including ground squirrels, bats, and bears.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".