Developmental exposure to constant elevated temperatures and diel thermal variation alters microRNA expression and performance in zebrafish (Danio rerio).
Bibliographic record
Abstract
In their natural environments, fish are exposed to diel thermal fluctuations, the range of which is expected to increase dramatically with climate change. Recently, it has been posited that epigenetic modulators like microRNAs (miRNAs) could buffer fish against such changes. To investigate this, we conducted two sequential experiments using zebrafish ( Danio rerio ). In Experiment 1, we exposed zebrafish to constant control (CTRL; 28 °C), fluctuating (FLUX; 28 ± 5 °C), or elevated (HEAT; 33 °C) temperatures throughout early development (embryo to juvenile, up to 30 days post-fertilization [dpf]). Throughout early development, we assessed survival, metabolism, and the expression of seven thermosensitive miRNAs and three heat shock proteins (HSPs). While HEAT conditions significantly reduced survival, FLUX exposure did not. Both FLUX and HEAT juveniles exhibited metabolic compensation, though this was more pronounced in FLUX fish. Notably, miR-181a-5p, which regulates mitochondrial biogenesis and respiration, was significantly upregulated in FLUX juveniles, suggesting its involvement in improving metabolic performance. Experiment 2 evaluated the long-term effects of ontogenetic thermal variability. Starting at 30 dpf, remaining fish in both CTRL and FLUX treatments were maintained under CTRL conditions until adulthood. We then assessed miRNA expression in the brain and thermal tolerance (CT max ). miR-181a-5p remained elevated in the brains of adult fish with FLUX developmental histories, though CT max was unaffected. Together, these findings suggest that early-life exposure to diel thermal fluctuations induces lasting epigenetic changes, potentially optimizing metabolic performance in the short-term at the cost of reduced capacity for further thermal tolerance and stress recovery later in life.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".