The emerging roles of microRNAs in the molecular responses of metabolic rate depression
Bibliographic record
Abstract
Metabolic rate depression is an important survival strategy for many animal species and a common element of hibernation, torpor, estivation, anoxia and diapause. Studies of the molecular mechanisms that regulate reversible transitions to and from hypometabolic states have identified principles of regulatory control. These control mechanisms are conserved among biologically diverse organisms and include the coordinated reduction of specific groups of key regulatory enzymes or proteins in the cell, a process likely driven by microRNA target repression/degradation. The present review focuses on a growing area of research in hypometabolism and mechanisms involving the rapid and reversible control of translation facilitated by microRNAs. The analysis draws primarily from current research on three animal models: hibernating mammals, anoxic turtles and freeze-tolerant frogs (with selected examples from multiple other sources). Here, we demonstrate a link between metabolic rate depression, a well-documented response to periods of environmental stress, and microRNA expression. Microarray-based expression profiles and PCR-driven studies have revealed that specific microRNAs are induced in response to environmental stress. Selected members of this group decrease pro-apoptotic signaling, reduce muscle wasting and reduce protein translation, whereas other members contribute to cell cycle arrest and mitogen-activated protein kinase signaling. Many of the same microRNAs are frequently deregulated in numerous disease pathologies and, hence, the hypometabolism model could provide a novel approach for the treatment of stroke and heart attack in humans.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".