The effects of short-term hypoxia on hypoxia-inducible factor 1 alpha mRNA and microRNA in Fundulus heteroclitus
Bibliographic record
Abstract
Hypoxia-inducible factor 1 (Hif1) is responsible for coordinating and regulating much of the response to acute hypoxia in vertebrates. This complex response is multifactorial, involving a large network of regulators. Within this network, epigenetic modulators, such as microRNA (miRNA), have garnered increasing interest due to their ability to regulate and fine-tune a myriad of cellular processes. Thus, we aimed to determine the effects of acute mild hypoxia on four known hypoxia-responsive miRNAs, along with three hypoxia-related transcripts. To do this, we exposed Atlantic killifish, Fundulus heteroclitus , to either normoxic (~ 8.70 mg O 2 l −1 ) or mild hypoxic (~ 2.40 mg O 2 l −1 ) conditions and sampled the gills and brain tissue throughout the exposure (0, 1, 3, 6, 12 h). While mild hypoxia resulted in a downregulation of gill hif1α mRNA, one of its downstream targets, insulin-like growth factor-binding protein 1 ( igfbp1 ), was significantly upregulated. Furthermore, hif1α mRNA, along with several miRNAs (miR-18, miR-455-3p, and miR-222), varied across time, suggesting potential involvement of the circadian clock. Finally, we observed that mild hypoxia resulted in differential expression of miR-210-5p and miR-222 in the brain, but not the gills, indicating tissue-specific epigenetic differences. Collectively, our results emphasize the complexity of the Hif1-mediated hypoxic response, warranting further investigation into underlying epigenetic mechanisms. • Hypoxia downregulates hypoxia-inducible factor 1 alpha ( hif1α ) mRNA in the gills. • hif1α mRNA and some microRNAs are differentially expressed throughout the day. • Hypoxia upregulates gill insulin-like growth factor-binding protein 1 mRNA. • Short-term hypoxia alters miR-210-5p and miR-222 levels in the brain. • Regulation of hif1α is complex and responds to environmental stress and time of day.
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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".