Transcriptional regulation of temperature-induced remodeling of muscle bioenergetics in goldfish
Bibliographic record
Abstract
Central to mammalian mitochondrial biogenesis is the transcriptional master regulator peroxisome proliferator-activated receptor (PPAR)-γ coactivator-1α (PGC-1α), and a network of DNA-binding proteins it coactivates. We explored the role of this pathway in muscle mitochondrial biogenesis in response to thermal acclimation in goldfish (Carassius auratus). We investigated the transcriptional response of PGC-1α, PGC-1β, and their antagonist the nuclear receptor interacting protein 1 (RIP140), as well as the mRNA and protein patterns of DNA-binding proteins that bind PGC-1, including nuclear respiratory factors (NRF) 1 and 2, retinoid X receptor α (RXRα), estrogen-related receptor α (ERRα), thyroid receptor α-1 (TRα-1), PPARα, and PPARβ/δ, and the host cell factor 1 (HCF1), which links PGC-1 and NRF-2. Cold-acclimated (4°C) fish had higher COX activities (4.5-fold) and COX4-1 mRNA levels (3.5-fold per total RNA; 6.5-fold per gram tissue) than warm-acclimated (32°C) fish. The transcription factor patterns were profoundly influenced by changes in RNA per gram tissue (2-fold higher in cold fish) and nuclear protein content (2-fold higher in warm fish). In cold-acclimated fish, mRNA per gram tissue was elevated for PGC-1β, RIP140, NRF-1, HCF1, NRF-2α, NRF-2β-2, ERRα, PPAR β/δ, and RXRα, but other transcriptional regulators either did not change (PGC-1α, PPARα) or even decreased (TRα-1). Nuclear protein levels in cold-acclimated fish were higher only for NRF-1; other proteins were either unaffected (NRF-2α, ERRα) or decreased (NRF-2β1/2, TRα, RXRα). Collectively, these data support the role for NRF-1 in regulating cold-induced mitochondrial biogenesis in goldfish, with effects mediated by PGC-1β, rather than PGC-1α.
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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".