MicroRNAs regulate survival in oxygen-deprived environments
Bibliographic record
Abstract
Some animals must endure prolonged periods of oxygen deprivation to survive. One such extreme model is the Northern Crayfish (Orconectes virilis), that regularly survives year-round hypoxic and anoxic stresses in its warm stagnant summer waters and in its cold, ice-locked winter waters. To elucidate the molecular underpinnings of anoxia-resistance in this natural model, we surveyed the expression profiles of 76 highly-conserved microRNAs in crayfish hepatopancreas and tail muscle from normoxic, acute 2hr anoxia, and chronic 20hr anoxia exposures. MicroRNAs are known to regulate a diverse array of cellular functions required for environmental stress adaptations, and here we explore their role in anoxia tolerance. The tissue-specific anoxia responses observed herein, with 22 anoxia-responsive microRNAs in hepatopancreas and only 4 changing microRNAs in muscle, suggest that microRNAs facilitate a reprioritization of resources to preserve crucial organ functions. Bioinformatic microRNA target enrichment analysis predicted that the anoxia-downregulated microRNAs in hepatopancreas targeted hippo-signalling, suggesting that cell proliferation and apoptotic signalling are highly regulated in this liver-like organ during anoxia. Compellingly, miR-125-5p, miR-33-5p, and miR-190-5p, all known to target the master regulator of oxygen deprivation responses HIF1 (Hypoxia Inducible Factor-1), were anoxia-downregulated in hepatopancreas. The anoxia-increased transcript levels of the oxygen dependent subunit HIF1α, highlight a potential critical role for miRNA-HIF targeting in facilitating a successful anoxia response. Studying the cytoprotective mechanisms in place to protect against the challenges associated with surviving in oxygen-poor environments is critical to elucidating microRNAs’ vast and substantial role in the regulation of metabolism and stress in aquatic invertebrates.
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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".