Genomic and Metabolic Preparation of Muscle in Sockeye Salmon<i>Oncorhynchus nerka</i>for Spawning Migration
Bibliographic record
Abstract
Prolonged endurance exercise and fasting are two major metabolic challenges facing Pacific salmon during spawning migrations that often occur over 1,000 km. Because both prolonged exercise and fasting stimulate the oxidation of lipids, particularly in heavily recruited tissues such as muscle, we sought to investigate the regulatory mechanisms that establish and maintain the capacity for substrate oxidation at four separate locations during the final 750 km of nonfeeding migration in sockeye salmon Oncorhynchus nerka. Transcript levels of multiple genes encoding for important regulators of lipid, carbohydrate, and protein oxidation as well as the activity of several important enzymes involved in lipid and carbohydrate oxidation were examined in red and white muscle. We found in both muscle types that the messenger RNA (mRNA) expression of carnitine palmitoyltransferase I isoforms, peroxisome proliferator-activated receptors α and β, and adenosine monophosphate-activated protein kinase β1 were all significantly higher at the onset compared to later stages of nonfeeding migration. However, the activities of β-hydroxyacyl-CoA dehydrogenase and citrate synthase were higher only early in migration and only in red muscle. Later in the migration and as muscle lipid stores were greatly depleted, the mRNA levels of hexokinase I and aspartate aminotransferase increased in white muscle. Overall, at the onset of migration, high transcript and metabolic enzyme activity levels in skeletal muscle of sockeye salmon may help support the high rates of lipid oxidation needed for endurance swimming. Furthermore, we suggest that the muscle capacity to use carbohydrates and proteins may be adjusted throughout migration on an as-needed basis to fuel burst exercise through very difficult hydraulic passages in the river and perhaps during mating activities.
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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".