Metabolic consequences of PGC-1α dysregulation in adult zebrafish muscle
Bibliographic record
Abstract
The peroxisome proliferator-activated receptor γ coactivator 1 α (PGC-1α) is central to the regulation of cellular and mitochondrial energy homeostasis in mammals, but its role in other vertebrates remains unclear. Indeed, previous work suggests extensive structural and functional divergence of PGC-1α in teleosts but this remains to be directly tested. Here, we describe the initial characterization of heterozygous PGC-1α mutant zebrafish lines created by CRISPR-Cas9 disruptions of an evolutionarily conserved regulatory region of the PGC-1α proximal promoter. Using qPCR, we confirmed the disruption of PGC-1α gene expression in striated muscle, leading to a simultaneous fourfold increase in mixed skeletal muscle PGC-1α mRNA levels and an opposite fourfold downregulation in cardiac muscle. In mixed skeletal muscle, most downstream effector genes were largely unaffected yet two mitochondrial lipid transporters, carnitine palmitoyltransferase-1 and -2, were strongly induced. Conversely, PGC-1α depression in cardiac muscle reduced the expression of several transcriptional regulators (estrogen-related receptor α, nuclear respiratory factor 1, and PGC-1β) without altering metabolic gene expression. Using high-resolution respirometry, we determined that white muscle exhibited increased lipid oxidative capacity with little difference in markers of mitochondrial abundance. Finally, using whole animal intermittent respirometry, we show that mutant fish exhibit a twofold higher basal metabolism than their wild-type counterparts. Altogether, this new model confirms a central but complex role for PGC-1α in mediating energy utilization in zebrafish, and we propose its use as a valuable tool to explore the intricate regulatory pathways of energy homeostasis in a popular biomedical model.
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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".