Metabolic rate depression in animals: transcriptional and translational controls
Bibliographic record
Abstract
Metabolic rate depression is an important survival strategy for many animal species and a common element of hibernation, torpor, aestivation, anaerobiosis, diapause, and anhydrobiosis. Studies of the biochemical mechanisms that regulate reversible transitions to and from hypometabolic states are identifying principles of regulatory control that are conserved across phylogenetic lines and that are broadly applied to the control of multiple cell functions. One such mechanism is reversible protein phosphorylation which is now known to contribute to the regulation of fuel metabolism, to ion channel arrest, and to the suppression of protein synthesis during hypometabolism. The present review focuses on two new areas of research in hypometabolism: (1) the role of differential gene expression in supplying protein products that adjust metabolism or protect cell functions for long-term survival, and (2) the mechanisms of protein life extension in hypometabolism involving inhibitory controls of transcription, translation and protein degradation. Control of translation examines reversible phosphorylation regulation of ribosomal initiation and elongation factors, the dissociation of polysomes and storage of mRNA transcripts during hypometabolism, and control over the translation of different mRNA types by differential sequestering of mRNA into polysome versus monosome fractions. The analysis draws primarily from current research on two animal models, hibernating mammals and anoxia-tolerant molluscs, with selected examples from multiple other sources.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".