The OxymiR response to oxygen limitation: a comparative microRNA perspective
Bibliographic record
Abstract
From squid at the bottom of the ocean to humans at the top of mountains, animals have adapted to diverse oxygen-limited environments. Surviving these challenging conditions requires global metabolic reorganization that is orchestrated, in part, by microRNAs that can rapidly and reversibly target all biological functions. Herein, we review the involvement of microRNAs in natural models of anoxia and hypoxia tolerance, with a focus on the involvement of oxygen-responsive microRNAs (OxymiRs) in coordinating the metabolic rate depression that allows animals to tolerate reduced oxygen levels. We begin by discussing animals that experience acute or chronic periods of oxygen deprivation at the ocean's oxygen minimum zone and go on to consider more elevated environments, up to mountain plateaus over 3500 m above sea level. We highlight the commonalities and differences between OxymiR responses of over 20 diverse animal species, including invertebrates and vertebrates. This is followed by a discussion of the OxymiR adaptations, and maladaptations, present in hypoxic high-altitude environments where animals, including humans, do not enter hypometabolic states in response to hypoxia. Comparing the OxymiR responses of evolutionarily disparate animals from diverse environments allows us to identify species-specific and convergent microRNA responses, such as miR-210 regulation. However, it also sheds light on the lack of a single unified response to oxygen limitation. Characterizing OxymiRs will help us to understand their protective roles and raises the question of whether they can be exploited to alleviate the pathogenesis of ischemic insults and boost recovery. This Review takes a comparative approach to addressing such possibilities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".